diff --git "a/SHOW/.vscode/PythonImportHelper-v2-Completion.json" "b/SHOW/.vscode/PythonImportHelper-v2-Completion.json"
new file mode 100644--- /dev/null
+++ "b/SHOW/.vscode/PythonImportHelper-v2-Completion.json"
@@ -0,0 +1,31655 @@
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+ "detail": "SHOW.load_models",
+ "documentation": {}
+ },
+ {
+ "label": "save_one_results",
+ "importPath": "SHOW.save_results",
+ "description": "SHOW.save_results",
+ "isExtraImport": true,
+ "detail": "SHOW.save_results",
+ "documentation": {}
+ },
+ {
+ "label": "save_one_results",
+ "importPath": "SHOW.save_results",
+ "description": "SHOW.save_results",
+ "isExtraImport": true,
+ "detail": "SHOW.save_results",
+ "documentation": {}
+ },
+ {
+ "label": "op_base",
+ "importPath": "SHOW.datasets",
+ "description": "SHOW.datasets",
+ "isExtraImport": true,
+ "detail": "SHOW.datasets",
+ "documentation": {}
+ },
+ {
+ "label": "op_dataset",
+ "importPath": "SHOW.datasets",
+ "description": "SHOW.datasets",
+ "isExtraImport": true,
+ "detail": "SHOW.datasets",
+ "documentation": {}
+ },
+ {
+ "label": "op_base",
+ "importPath": "SHOW.datasets",
+ "description": "SHOW.datasets",
+ "isExtraImport": true,
+ "detail": "SHOW.datasets",
+ "documentation": {}
+ },
+ {
+ "label": "images_to_video",
+ "importPath": "mmhuman3d.utils.ffmpeg_utils",
+ "description": "mmhuman3d.utils.ffmpeg_utils",
+ "isExtraImport": true,
+ "detail": "mmhuman3d.utils.ffmpeg_utils",
+ "documentation": {}
+ },
+ {
+ "label": "setup_logger",
+ "importPath": "SHOW.loggers.logger",
+ "description": "SHOW.loggers.logger",
+ "isExtraImport": true,
+ "detail": "SHOW.loggers.logger",
+ "documentation": {}
+ },
+ {
+ "label": "setup_logger",
+ "importPath": "SHOW.loggers.logger",
+ "description": "SHOW.loggers.logger",
+ "isExtraImport": true,
+ "detail": "SHOW.loggers.logger",
+ "documentation": {}
+ },
+ {
+ "label": "build_prior",
+ "importPath": "SHOW.prior",
+ "description": "SHOW.prior",
+ "isExtraImport": true,
+ "detail": "SHOW.prior",
+ "documentation": {}
+ },
+ {
+ "label": "load_assets",
+ "importPath": "SHOW.load_assets",
+ "description": "SHOW.load_assets",
+ "isExtraImport": true,
+ "detail": "SHOW.load_assets",
+ "documentation": {}
+ },
+ {
+ "label": "load_assets",
+ "importPath": "SHOW.load_assets",
+ "description": "SHOW.load_assets",
+ "isExtraImport": true,
+ "detail": "SHOW.load_assets",
+ "documentation": {}
+ },
+ {
+ "label": "parse_weight",
+ "importPath": "SHOW.parse_weight",
+ "description": "SHOW.parse_weight",
+ "isExtraImport": true,
+ "detail": "SHOW.parse_weight",
+ "documentation": {}
+ },
+ {
+ "label": "*",
+ "importPath": "SHOW.losses",
+ "description": "SHOW.losses",
+ "isExtraImport": true,
+ "detail": "SHOW.losses",
+ "documentation": {}
+ },
+ {
+ "label": "condor_cfg",
+ "importPath": "configs.cfg_ins",
+ "description": "configs.cfg_ins",
+ "isExtraImport": true,
+ "detail": "configs.cfg_ins",
+ "documentation": {}
+ },
+ {
+ "label": "condor_cfg",
+ "importPath": "configs.cfg_ins",
+ "description": "configs.cfg_ins",
+ "isExtraImport": true,
+ "detail": "configs.cfg_ins",
+ "documentation": {}
+ },
+ {
+ "label": "atach_model_func",
+ "importPath": "SHOW.datasets.model_func_atach",
+ "description": "SHOW.datasets.model_func_atach",
+ "isExtraImport": true,
+ "detail": "SHOW.datasets.model_func_atach",
+ "documentation": {}
+ },
+ {
+ "label": "FLAMETex",
+ "importPath": "SHOW.flame.FLAME",
+ "description": "SHOW.flame.FLAME",
+ "isExtraImport": true,
+ "detail": "SHOW.flame.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "ImagesDataset",
+ "importPath": "SHOW.smplx_dataset",
+ "description": "SHOW.smplx_dataset",
+ "isExtraImport": true,
+ "detail": "SHOW.smplx_dataset",
+ "documentation": {}
+ },
+ {
+ "label": "Renderer",
+ "importPath": "SHOW.renderer",
+ "description": "SHOW.renderer",
+ "isExtraImport": true,
+ "detail": "SHOW.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "save_tracker",
+ "importPath": "SHOW.save_tracker",
+ "description": "SHOW.save_tracker",
+ "isExtraImport": true,
+ "detail": "SHOW.save_tracker",
+ "documentation": {}
+ },
+ {
+ "label": "log_config_tracker_nep",
+ "kind": 5,
+ "importPath": "configs.base.log_config",
+ "description": "configs.base.log_config",
+ "peekOfCode": "log_config_tracker_nep = dict( \n interval=100, \n hooks=[ \n dict(type='MyNeptuneLogger', \n with_step=False,\n init_kwargs=dict(\n name='test',\n project='lithiumice/tracker',\n api_token=\"==\",\n )) ",
+ "detail": "configs.base.log_config",
+ "documentation": {}
+ },
+ {
+ "label": "log_config_smplifyx_nep",
+ "kind": 5,
+ "importPath": "configs.base.log_config",
+ "description": "configs.base.log_config",
+ "peekOfCode": "log_config_smplifyx_nep = dict( \n interval=100, \n hooks=[ \n dict(type='MyNeptuneLogger', \n with_step=False,\n init_kwargs=dict(\n name='test',\n project='lithiumice/smplifyx',\n api_token=\"==\",\n )) ",
+ "detail": "configs.base.log_config",
+ "documentation": {}
+ },
+ {
+ "label": "log_config_smplifyx_wandb",
+ "kind": 5,
+ "importPath": "configs.base.log_config",
+ "description": "configs.base.log_config",
+ "peekOfCode": "log_config_smplifyx_wandb = dict( \n interval=100, \n hooks=[ \n dict(type='MyWandbLogger', \n log_artifact=False,\n wandb_key='',\n wandb_name='NEED_TO_BE_FILLED',\n init_kwargs=dict(\n reinit=True,\n resume='allow',",
+ "detail": "configs.base.log_config",
+ "documentation": {}
+ },
+ {
+ "label": "log_config_tracker_wandb",
+ "kind": 5,
+ "importPath": "configs.base.log_config",
+ "description": "configs.base.log_config",
+ "peekOfCode": "log_config_tracker_wandb = dict( \n interval=100, \n hooks=[ \n dict(type='MyWandbLogger', \n log_artifact=False,\n wandb_key='',\n wandb_name='NEED_TO_BE_FILLED',\n init_kwargs=dict(\n reinit=True,\n resume='allow',",
+ "detail": "configs.base.log_config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg_smoothnet_w8",
+ "kind": 5,
+ "importPath": "configs.base.smoothnet_cfg",
+ "description": "configs.base.smoothnet_cfg",
+ "peekOfCode": "cfg_smoothnet_w8 = dict(\n type='smoothnet',\n window_size=8,\n output_size=8,\n checkpoint='https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/'\n 'mmhuman3d/../models/smoothnet/smoothnet_windowsize8.pth.tar?versionId'\n '=CAEQPhiBgMDo0s7shhgiIDgzNTRmNWM2ZWEzYTQyYzRhNzUwYTkzZWZkMmU5MWEw',\n device='cpu')\ncfg_smoothnet_w16 = dict(\n type='smoothnet',",
+ "detail": "configs.base.smoothnet_cfg",
+ "documentation": {}
+ },
+ {
+ "label": "cfg_smoothnet_w16",
+ "kind": 5,
+ "importPath": "configs.base.smoothnet_cfg",
+ "description": "configs.base.smoothnet_cfg",
+ "peekOfCode": "cfg_smoothnet_w16 = dict(\n type='smoothnet',\n window_size=16,\n output_size=16,\n checkpoint='https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/'\n 'mmhuman3d/../models/smoothnet/smoothnet_windowsize16.pth.tar?versionId'\n '=CAEQPhiBgMC.s87shhgiIGM3ZTI1ZGY1Y2NhNDQ2YzRiNmEyOGZhY2VjYWFiN2Zi',\n device='cpu')\ncfg_smoothnet_w32 = dict(\n type='smoothnet',",
+ "detail": "configs.base.smoothnet_cfg",
+ "documentation": {}
+ },
+ {
+ "label": "cfg_smoothnet_w32",
+ "kind": 5,
+ "importPath": "configs.base.smoothnet_cfg",
+ "description": "configs.base.smoothnet_cfg",
+ "peekOfCode": "cfg_smoothnet_w32 = dict(\n type='smoothnet',\n window_size=32,\n output_size=32,\n checkpoint='https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/'\n 'mmhuman3d/../models/smoothnet/smoothnet_windowsize32.pth.tar?versionId'\n '=CAEQPhiBgIDf0s7shhgiIDhmYmM3YWQ0ZGI3NjRmZTc4NTk2NDE1MTA2MTUyMGRm',\n device='cpu')\ncfg_smoothnet_w64 = dict(\n type='smoothnet',",
+ "detail": "configs.base.smoothnet_cfg",
+ "documentation": {}
+ },
+ {
+ "label": "cfg_smoothnet_w64",
+ "kind": 5,
+ "importPath": "configs.base.smoothnet_cfg",
+ "description": "configs.base.smoothnet_cfg",
+ "peekOfCode": "cfg_smoothnet_w64 = dict(\n type='smoothnet',\n window_size=64,\n output_size=64,\n checkpoint='https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/'\n 'mmhuman3d/../models/smoothnet/smoothnet_windowsize64.pth.tar?versionId'\n '=CAEQPhiBgMCyw87shhgiIGEwODI4ZjdiYmFkYTQ0NzZiNDVkODk3MDBlYzE1Y2Rh',\n device='cpu')",
+ "detail": "configs.base.smoothnet_cfg",
+ "documentation": {}
+ },
+ {
+ "label": "num_gaussians",
+ "kind": 5,
+ "importPath": "configs.base.smplifyx_prior_config",
+ "description": "configs.base.smplifyx_prior_config",
+ "peekOfCode": "num_gaussians = 12\nhand_prior_type = 'l2'",
+ "detail": "configs.base.smplifyx_prior_config",
+ "documentation": {}
+ },
+ {
+ "label": "hand_prior_type",
+ "kind": 5,
+ "importPath": "configs.base.smplifyx_prior_config",
+ "description": "configs.base.smplifyx_prior_config",
+ "peekOfCode": "hand_prior_type = 'l2'",
+ "detail": "configs.base.smplifyx_prior_config",
+ "documentation": {}
+ },
+ {
+ "label": "opt_weights_dict",
+ "kind": 5,
+ "importPath": "configs.base.smplifyx_weights",
+ "description": "configs.base.smplifyx_weights",
+ "peekOfCode": "opt_weights_dict = dict(\n jaw_prior_weight=[[47.8, 478.0, 478.0],]*4,\n bending_prior_weight=[15.15,]*4,\n body_pose_weight=[4.78, ]*4,\n expr_prior_weight=[5.0, ]*4,\n hand_prior_weight=[4.78,]*4,\n coll_loss_weight=[1.0,]*4,\n shape_weight=[5.0,]*4,\n data_weight=[1.0,]*4,\n body_joints_weight=[2,],",
+ "detail": "configs.base.smplifyx_weights",
+ "documentation": {}
+ },
+ {
+ "label": "ffmpeg_path",
+ "kind": 5,
+ "importPath": "configs.configs.machine_cfg",
+ "description": "configs.configs.machine_cfg",
+ "peekOfCode": "ffmpeg_path = '/usr/bin/ffmpeg'\nopenpose_root_path = '/content/openpose'\nopenpose_bin_path = 'build/examples/openpose/openpose.bin'\nvideo_out_base_path: str = '{{ fileDirname }}/../../../speech2gesture_dataset/crop4'\nintervals_csv_path: str = \"{{ fileDirname }}/../data_csv/intervals_sub4.csv\"\nintervals_csv_path_debug: str = \"{{ fileDirname }}/../data_csv/test.csv\"\nfolder_version: int = 1\nlow_res: int = 1\nfps=30\nbs_at_a_time=15",
+ "detail": "configs.configs.machine_cfg",
+ "documentation": {}
+ },
+ {
+ "label": "openpose_root_path",
+ "kind": 5,
+ "importPath": "configs.configs.machine_cfg",
+ "description": "configs.configs.machine_cfg",
+ "peekOfCode": "openpose_root_path = '/content/openpose'\nopenpose_bin_path = 'build/examples/openpose/openpose.bin'\nvideo_out_base_path: str = '{{ fileDirname }}/../../../speech2gesture_dataset/crop4'\nintervals_csv_path: str = \"{{ fileDirname }}/../data_csv/intervals_sub4.csv\"\nintervals_csv_path_debug: str = \"{{ fileDirname }}/../data_csv/test.csv\"\nfolder_version: int = 1\nlow_res: int = 1\nfps=30\nbs_at_a_time=15\ncoap_bs_at_a_time=400",
+ "detail": "configs.configs.machine_cfg",
+ "documentation": {}
+ },
+ {
+ "label": "openpose_bin_path",
+ "kind": 5,
+ "importPath": "configs.configs.machine_cfg",
+ "description": "configs.configs.machine_cfg",
+ "peekOfCode": "openpose_bin_path = 'build/examples/openpose/openpose.bin'\nvideo_out_base_path: str = '{{ fileDirname }}/../../../speech2gesture_dataset/crop4'\nintervals_csv_path: str = \"{{ fileDirname }}/../data_csv/intervals_sub4.csv\"\nintervals_csv_path_debug: str = \"{{ fileDirname }}/../data_csv/test.csv\"\nfolder_version: int = 1\nlow_res: int = 1\nfps=30\nbs_at_a_time=15\ncoap_bs_at_a_time=400\no3d_opt_bs_at_a_time=400",
+ "detail": "configs.configs.machine_cfg",
+ "documentation": {}
+ },
+ {
+ "label": "request_gpus",
+ "kind": 5,
+ "importPath": "configs.configs.machine_cfg",
+ "description": "configs.configs.machine_cfg",
+ "peekOfCode": "request_gpus = 1",
+ "detail": "configs.configs.machine_cfg",
+ "documentation": {}
+ },
+ {
+ "label": "condor_cfg",
+ "kind": 5,
+ "importPath": "configs.cfg_ins",
+ "description": "configs.cfg_ins",
+ "peekOfCode": "condor_cfg = SHOW.from_rela_path(\n __file__, \n './configs/condor_mmcv_cfg.py')\ncondor_cfg.is_linux = 1 if platform.system() == \"Linux\" else 0\ngpu_info = SHOW.get_gpu_info()\ncondor_cfg.merge_from_dict(SHOW.from_rela_path(\n __file__,\n './configs/machine_cfg.py'))",
+ "detail": "configs.cfg_ins",
+ "documentation": {}
+ },
+ {
+ "label": "condor_cfg.is_linux",
+ "kind": 5,
+ "importPath": "configs.cfg_ins",
+ "description": "configs.cfg_ins",
+ "peekOfCode": "condor_cfg.is_linux = 1 if platform.system() == \"Linux\" else 0\ngpu_info = SHOW.get_gpu_info()\ncondor_cfg.merge_from_dict(SHOW.from_rela_path(\n __file__,\n './configs/machine_cfg.py'))",
+ "detail": "configs.cfg_ins",
+ "documentation": {}
+ },
+ {
+ "label": "gpu_info",
+ "kind": 5,
+ "importPath": "configs.cfg_ins",
+ "description": "configs.cfg_ins",
+ "peekOfCode": "gpu_info = SHOW.get_gpu_info()\ncondor_cfg.merge_from_dict(SHOW.from_rela_path(\n __file__,\n './configs/machine_cfg.py'))",
+ "detail": "configs.cfg_ins",
+ "documentation": {}
+ },
+ {
+ "label": "gen_path_from_ours_root",
+ "kind": 2,
+ "importPath": "configs.csv_parser",
+ "description": "configs.csv_parser",
+ "peekOfCode": "def gen_path_from_ours_root(\n speaker_name,\n all_top_dir,\n ours_name='ours',\n mica_name='ours_exp',\n **kwargs\n):\n ###########################################\n ours_output_folder = osp.join(\n all_top_dir, ours_name)",
+ "detail": "configs.csv_parser",
+ "documentation": {}
+ },
+ {
+ "label": "_base_",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "_base_ = [\n './base/face_ider.py',\n './base/log_config.py',\n './base/optimizer_config.py',\n './base/model_smplx_config.py',\n './base/model_flame_config.py',\n './base/smplifyx_weights.py',\n './base/smplifyx_loss_configs.py',\n './base/smplifyx_prior_config.py',\n './base/betas_generate.py',",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "batch_size",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "batch_size = -1\nsave_betas=False\nload_betas=False\nshape_path = ''\nsave_objs = False\nsave_template = False\nsave_smplpix = True\nsave_ours_images = True\nsave_pkl_file=True\nfocal_length = 5000",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "shape_path",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "shape_path = ''\nsave_objs = False\nsave_template = False\nsave_smplpix = True\nsave_ours_images = True\nsave_pkl_file=True\nfocal_length = 5000\nuse_vposer = True\ndevice = 'cuda'\ndtype = \"float32\"",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "save_objs",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "save_objs = False\nsave_template = False\nsave_smplpix = True\nsave_ours_images = True\nsave_pkl_file=True\nfocal_length = 5000\nuse_vposer = True\ndevice = 'cuda'\ndtype = \"float32\"\noutput_img_ext = 'png'",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "save_template",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "save_template = False\nsave_smplpix = True\nsave_ours_images = True\nsave_pkl_file=True\nfocal_length = 5000\nuse_vposer = True\ndevice = 'cuda'\ndtype = \"float32\"\noutput_img_ext = 'png'\nimg_save_mode='origin'",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "save_smplpix",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "save_smplpix = True\nsave_ours_images = True\nsave_pkl_file=True\nfocal_length = 5000\nuse_vposer = True\ndevice = 'cuda'\ndtype = \"float32\"\noutput_img_ext = 'png'\nimg_save_mode='origin'\nload_tracker_checkpoint=False",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "save_ours_images",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "save_ours_images = True\nsave_pkl_file=True\nfocal_length = 5000\nuse_vposer = True\ndevice = 'cuda'\ndtype = \"float32\"\noutput_img_ext = 'png'\nimg_save_mode='origin'\nload_tracker_checkpoint=False\ntracker_checkpoint_root=''",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "focal_length",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "focal_length = 5000\nuse_vposer = True\ndevice = 'cuda'\ndtype = \"float32\"\noutput_img_ext = 'png'\nimg_save_mode='origin'\nload_tracker_checkpoint=False\ntracker_checkpoint_root=''\nstart_stage=0\nend_stage=None",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "use_vposer",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "use_vposer = True\ndevice = 'cuda'\ndtype = \"float32\"\noutput_img_ext = 'png'\nimg_save_mode='origin'\nload_tracker_checkpoint=False\ntracker_checkpoint_root=''\nstart_stage=0\nend_stage=None\nload_checkpoint=False",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "device",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "device = 'cuda'\ndtype = \"float32\"\noutput_img_ext = 'png'\nimg_save_mode='origin'\nload_tracker_checkpoint=False\ntracker_checkpoint_root=''\nstart_stage=0\nend_stage=None\nload_checkpoint=False\ncheck_pkl_metric=False",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "dtype",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "dtype = \"float32\"\noutput_img_ext = 'png'\nimg_save_mode='origin'\nload_tracker_checkpoint=False\ntracker_checkpoint_root=''\nstart_stage=0\nend_stage=None\nload_checkpoint=False\ncheck_pkl_metric=False\nload_ckpt_st_stage=-1",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "output_img_ext",
+ "kind": 5,
+ "importPath": "configs.mmcv_smplifyx_config",
+ "description": "configs.mmcv_smplifyx_config",
+ "peekOfCode": "output_img_ext = 'png'\nimg_save_mode='origin'\nload_tracker_checkpoint=False\ntracker_checkpoint_root=''\nstart_stage=0\nend_stage=None\nload_checkpoint=False\ncheck_pkl_metric=False\nload_ckpt_st_stage=-1\nload_ckpt_ed_stage=None",
+ "detail": "configs.mmcv_smplifyx_config",
+ "documentation": {}
+ },
+ {
+ "label": "_base_",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "_base_ = [\n './base/model_smplx_config.py',\n './base/model_flame_config.py',\n './base/log_config.py',\n './base/face_ider.py',\n]\nbatch_size = -1\nshape_path = ''\ndevice = 'cuda'\ndtype = \"float32\"",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "batch_size",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "batch_size = -1\nshape_path = ''\ndevice = 'cuda'\ndtype = \"float32\"\noutput_img_ext = 'png'\nexp_weight = 0.02\nbatch_size = 1\nw_pho = 10.\nw_lmks = 2.\nsampling = 0",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "shape_path",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "shape_path = ''\ndevice = 'cuda'\ndtype = \"float32\"\noutput_img_ext = 'png'\nexp_weight = 0.02\nbatch_size = 1\nw_pho = 10.\nw_lmks = 2.\nsampling = 0\nkeyframes = []",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "device",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "device = 'cuda'\ndtype = \"float32\"\noutput_img_ext = 'png'\nexp_weight = 0.02\nbatch_size = 1\nw_pho = 10.\nw_lmks = 2.\nsampling = 0\nkeyframes = []\nuse_keyframes = False",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "dtype",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "dtype = \"float32\"\noutput_img_ext = 'png'\nexp_weight = 0.02\nbatch_size = 1\nw_pho = 10.\nw_lmks = 2.\nsampling = 0\nkeyframes = []\nuse_keyframes = False\nwarmup_steps = 1",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "output_img_ext",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "output_img_ext = 'png'\nexp_weight = 0.02\nbatch_size = 1\nw_pho = 10.\nw_lmks = 2.\nsampling = 0\nkeyframes = []\nuse_keyframes = False\nwarmup_steps = 1\nbbox_scale = 2.5",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "exp_weight",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "exp_weight = 0.02\nbatch_size = 1\nw_pho = 10.\nw_lmks = 2.\nsampling = 0\nkeyframes = []\nuse_keyframes = False\nwarmup_steps = 1\nbbox_scale = 2.5\nmake_image_square = True",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "batch_size",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "batch_size = 1\nw_pho = 10.\nw_lmks = 2.\nsampling = 0\nkeyframes = []\nuse_keyframes = False\nwarmup_steps = 1\nbbox_scale = 2.5\nmake_image_square = True\nsquare_size = 512",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "w_pho",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "w_pho = 10.\nw_lmks = 2.\nsampling = 0\nkeyframes = []\nuse_keyframes = False\nwarmup_steps = 1\nbbox_scale = 2.5\nmake_image_square = True\nsquare_size = 512\nuse_kinect = False",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "w_lmks",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "w_lmks = 2.\nsampling = 0\nkeyframes = []\nuse_keyframes = False\nwarmup_steps = 1\nbbox_scale = 2.5\nmake_image_square = True\nsquare_size = 512\nuse_kinect = False\nuse_mediapipe = True",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "sampling",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "sampling = 0\nkeyframes = []\nuse_keyframes = False\nwarmup_steps = 1\nbbox_scale = 2.5\nmake_image_square = True\nsquare_size = 512\nuse_kinect = False\nuse_mediapipe = True\noptimize_shape = False",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "keyframes",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "keyframes = []\nuse_keyframes = False\nwarmup_steps = 1\nbbox_scale = 2.5\nmake_image_square = True\nsquare_size = 512\nuse_kinect = False\nuse_mediapipe = True\noptimize_shape = False\nimage_size = [512, 512]",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "use_keyframes",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "use_keyframes = False\nwarmup_steps = 1\nbbox_scale = 2.5\nmake_image_square = True\nsquare_size = 512\nuse_kinect = False\nuse_mediapipe = True\noptimize_shape = False\nimage_size = [512, 512]\nconfig_name = ''",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "warmup_steps",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "warmup_steps = 1\nbbox_scale = 2.5\nmake_image_square = True\nsquare_size = 512\nuse_kinect = False\nuse_mediapipe = True\noptimize_shape = False\nimage_size = [512, 512]\nconfig_name = ''\nsave_final_vis=True",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "bbox_scale",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "bbox_scale = 2.5\nmake_image_square = True\nsquare_size = 512\nuse_kinect = False\nuse_mediapipe = True\noptimize_shape = False\nimage_size = [512, 512]\nconfig_name = ''\nsave_final_vis=True\nsave_final_flame=True",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "make_image_square",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "make_image_square = True\nsquare_size = 512\nuse_kinect = False\nuse_mediapipe = True\noptimize_shape = False\nimage_size = [512, 512]\nconfig_name = ''\nsave_final_vis=True\nsave_final_flame=True\niters=149",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "square_size",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "square_size = 512\nuse_kinect = False\nuse_mediapipe = True\noptimize_shape = False\nimage_size = [512, 512]\nconfig_name = ''\nsave_final_vis=True\nsave_final_flame=True\niters=149\nbs_at_a_time=3",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "use_kinect",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "use_kinect = False\nuse_mediapipe = True\noptimize_shape = False\nimage_size = [512, 512]\nconfig_name = ''\nsave_final_vis=True\nsave_final_flame=True\niters=149\nbs_at_a_time=3\nsampling=[1/2,1,2]",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "use_mediapipe",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "use_mediapipe = True\noptimize_shape = False\nimage_size = [512, 512]\nconfig_name = ''\nsave_final_vis=True\nsave_final_flame=True\niters=149\nbs_at_a_time=3\nsampling=[1/2,1,2]\nuse_face_upsample=False",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "optimize_shape",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "optimize_shape = False\nimage_size = [512, 512]\nconfig_name = ''\nsave_final_vis=True\nsave_final_flame=True\niters=149\nbs_at_a_time=3\nsampling=[1/2,1,2]\nuse_face_upsample=False",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "image_size",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "image_size = [512, 512]\nconfig_name = ''\nsave_final_vis=True\nsave_final_flame=True\niters=149\nbs_at_a_time=3\nsampling=[1/2,1,2]\nuse_face_upsample=False",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "config_name",
+ "kind": 5,
+ "importPath": "configs.mmcv_tracker_config",
+ "description": "configs.mmcv_tracker_config",
+ "peekOfCode": "config_name = ''\nsave_final_vis=True\nsave_final_flame=True\niters=149\nbs_at_a_time=3\nsampling=[1/2,1,2]\nuse_face_upsample=False",
+ "detail": "configs.mmcv_tracker_config",
+ "documentation": {}
+ },
+ {
+ "label": "check_or_make_var",
+ "kind": 2,
+ "importPath": "configs.utils",
+ "description": "configs.utils",
+ "peekOfCode": "def check_or_make_var(meta_data,key_name,get_default_val_func=lambda :[]):\n if meta_data.get(key_name,None) is None:\n meta_data[key_name]=get_default_val_func()",
+ "detail": "configs.utils",
+ "documentation": {}
+ },
+ {
+ "label": "add_path",
+ "kind": 2,
+ "importPath": "configs._init_paths",
+ "description": "configs._init_paths",
+ "peekOfCode": "def add_path(path):\n if path not in sys.path:\n sys.path.insert(0, path)\nlocal_bin_paths=[\n r'C:\\Users\\lithiumice',\n r'C:\\Users\\lithiumice\\code',\n os.path.join(this_dir, '..'),\n # sys.path.append(Path(__file__).parent.parent.__str__())\n]\nfor path in local_bin_paths:",
+ "detail": "configs._init_paths",
+ "documentation": {}
+ },
+ {
+ "label": "this_dir",
+ "kind": 5,
+ "importPath": "configs._init_paths",
+ "description": "configs._init_paths",
+ "peekOfCode": "this_dir = os.path.dirname(__file__)\ndef add_path(path):\n if path not in sys.path:\n sys.path.insert(0, path)\nlocal_bin_paths=[\n r'C:\\Users\\lithiumice',\n r'C:\\Users\\lithiumice\\code',\n os.path.join(this_dir, '..'),\n # sys.path.append(Path(__file__).parent.parent.__str__())\n]",
+ "detail": "configs._init_paths",
+ "documentation": {}
+ },
+ {
+ "label": "IBasicBlock",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.iresnet",
+ "description": "modules.arcface_torch.backbones.iresnet",
+ "peekOfCode": "class IBasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None,\n groups=1, base_width=64, dilation=1):\n super(IBasicBlock, self).__init__()\n if groups != 1 or base_width != 64:\n raise ValueError('BasicBlock only supports groups=1 and base_width=64')\n if dilation > 1:\n raise NotImplementedError(\"Dilation > 1 not supported in BasicBlock\")\n self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)",
+ "detail": "modules.arcface_torch.backbones.iresnet",
+ "documentation": {}
+ },
+ {
+ "label": "IResNet",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.iresnet",
+ "description": "modules.arcface_torch.backbones.iresnet",
+ "peekOfCode": "class IResNet(nn.Module):\n fc_scale = 7 * 7\n def __init__(self,\n block, layers, dropout=0, num_features=512, zero_init_residual=False,\n groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):\n super(IResNet, self).__init__()\n self.extra_gflops = 0.0\n self.fp16 = fp16\n self.inplanes = 64\n self.dilation = 1",
+ "detail": "modules.arcface_torch.backbones.iresnet",
+ "documentation": {}
+ },
+ {
+ "label": "conv3x3",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.iresnet",
+ "description": "modules.arcface_torch.backbones.iresnet",
+ "peekOfCode": "def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes,\n out_planes,\n kernel_size=3,\n stride=stride,\n padding=dilation,\n groups=groups,\n bias=False,\n dilation=dilation)",
+ "detail": "modules.arcface_torch.backbones.iresnet",
+ "documentation": {}
+ },
+ {
+ "label": "conv1x1",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.iresnet",
+ "description": "modules.arcface_torch.backbones.iresnet",
+ "peekOfCode": "def conv1x1(in_planes, out_planes, stride=1):\n \"\"\"1x1 convolution\"\"\"\n return nn.Conv2d(in_planes,\n out_planes,\n kernel_size=1,\n stride=stride,\n bias=False)\nclass IBasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None,",
+ "detail": "modules.arcface_torch.backbones.iresnet",
+ "documentation": {}
+ },
+ {
+ "label": "iresnet18",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.iresnet",
+ "description": "modules.arcface_torch.backbones.iresnet",
+ "peekOfCode": "def iresnet18(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,\n progress, **kwargs)\ndef iresnet34(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,\n progress, **kwargs)\ndef iresnet50(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,\n progress, **kwargs)\ndef iresnet100(pretrained=False, progress=True, **kwargs):",
+ "detail": "modules.arcface_torch.backbones.iresnet",
+ "documentation": {}
+ },
+ {
+ "label": "iresnet34",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.iresnet",
+ "description": "modules.arcface_torch.backbones.iresnet",
+ "peekOfCode": "def iresnet34(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,\n progress, **kwargs)\ndef iresnet50(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,\n progress, **kwargs)\ndef iresnet100(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,\n progress, **kwargs)\ndef iresnet200(pretrained=False, progress=True, **kwargs):",
+ "detail": "modules.arcface_torch.backbones.iresnet",
+ "documentation": {}
+ },
+ {
+ "label": "iresnet50",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.iresnet",
+ "description": "modules.arcface_torch.backbones.iresnet",
+ "peekOfCode": "def iresnet50(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,\n progress, **kwargs)\ndef iresnet100(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,\n progress, **kwargs)\ndef iresnet200(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,\n progress, **kwargs)",
+ "detail": "modules.arcface_torch.backbones.iresnet",
+ "documentation": {}
+ },
+ {
+ "label": "iresnet100",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.iresnet",
+ "description": "modules.arcface_torch.backbones.iresnet",
+ "peekOfCode": "def iresnet100(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,\n progress, **kwargs)\ndef iresnet200(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,\n progress, **kwargs)",
+ "detail": "modules.arcface_torch.backbones.iresnet",
+ "documentation": {}
+ },
+ {
+ "label": "iresnet200",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.iresnet",
+ "description": "modules.arcface_torch.backbones.iresnet",
+ "peekOfCode": "def iresnet200(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,\n progress, **kwargs)",
+ "detail": "modules.arcface_torch.backbones.iresnet",
+ "documentation": {}
+ },
+ {
+ "label": "__all__",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.backbones.iresnet",
+ "description": "modules.arcface_torch.backbones.iresnet",
+ "peekOfCode": "__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']\nusing_ckpt = False\ndef conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes,\n out_planes,\n kernel_size=3,\n stride=stride,\n padding=dilation,\n groups=groups,",
+ "detail": "modules.arcface_torch.backbones.iresnet",
+ "documentation": {}
+ },
+ {
+ "label": "using_ckpt",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.backbones.iresnet",
+ "description": "modules.arcface_torch.backbones.iresnet",
+ "peekOfCode": "using_ckpt = False\ndef conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes,\n out_planes,\n kernel_size=3,\n stride=stride,\n padding=dilation,\n groups=groups,\n bias=False,",
+ "detail": "modules.arcface_torch.backbones.iresnet",
+ "documentation": {}
+ },
+ {
+ "label": "IBasicBlock",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.iresnet2060",
+ "description": "modules.arcface_torch.backbones.iresnet2060",
+ "peekOfCode": "class IBasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None,\n groups=1, base_width=64, dilation=1):\n super(IBasicBlock, self).__init__()\n if groups != 1 or base_width != 64:\n raise ValueError('BasicBlock only supports groups=1 and base_width=64')\n if dilation > 1:\n raise NotImplementedError(\"Dilation > 1 not supported in BasicBlock\")\n self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05, )",
+ "detail": "modules.arcface_torch.backbones.iresnet2060",
+ "documentation": {}
+ },
+ {
+ "label": "IResNet",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.iresnet2060",
+ "description": "modules.arcface_torch.backbones.iresnet2060",
+ "peekOfCode": "class IResNet(nn.Module):\n fc_scale = 7 * 7\n def __init__(self,\n block, layers, dropout=0, num_features=512, zero_init_residual=False,\n groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):\n super(IResNet, self).__init__()\n self.fp16 = fp16\n self.inplanes = 64\n self.dilation = 1\n if replace_stride_with_dilation is None:",
+ "detail": "modules.arcface_torch.backbones.iresnet2060",
+ "documentation": {}
+ },
+ {
+ "label": "conv3x3",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.iresnet2060",
+ "description": "modules.arcface_torch.backbones.iresnet2060",
+ "peekOfCode": "def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes,\n out_planes,\n kernel_size=3,\n stride=stride,\n padding=dilation,\n groups=groups,\n bias=False,\n dilation=dilation)",
+ "detail": "modules.arcface_torch.backbones.iresnet2060",
+ "documentation": {}
+ },
+ {
+ "label": "conv1x1",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.iresnet2060",
+ "description": "modules.arcface_torch.backbones.iresnet2060",
+ "peekOfCode": "def conv1x1(in_planes, out_planes, stride=1):\n \"\"\"1x1 convolution\"\"\"\n return nn.Conv2d(in_planes,\n out_planes,\n kernel_size=1,\n stride=stride,\n bias=False)\nclass IBasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None,",
+ "detail": "modules.arcface_torch.backbones.iresnet2060",
+ "documentation": {}
+ },
+ {
+ "label": "iresnet2060",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.iresnet2060",
+ "description": "modules.arcface_torch.backbones.iresnet2060",
+ "peekOfCode": "def iresnet2060(pretrained=False, progress=True, **kwargs):\n return _iresnet('iresnet2060', IBasicBlock, [3, 128, 1024 - 128, 3], pretrained, progress, **kwargs)",
+ "detail": "modules.arcface_torch.backbones.iresnet2060",
+ "documentation": {}
+ },
+ {
+ "label": "__all__",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.backbones.iresnet2060",
+ "description": "modules.arcface_torch.backbones.iresnet2060",
+ "peekOfCode": "__all__ = ['iresnet2060']\ndef conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes,\n out_planes,\n kernel_size=3,\n stride=stride,\n padding=dilation,\n groups=groups,\n bias=False,",
+ "detail": "modules.arcface_torch.backbones.iresnet2060",
+ "documentation": {}
+ },
+ {
+ "label": "Flatten",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.mobilefacenet",
+ "description": "modules.arcface_torch.backbones.mobilefacenet",
+ "peekOfCode": "class Flatten(Module):\n def forward(self, x):\n return x.view(x.size(0), -1)\nclass ConvBlock(Module):\n def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):\n super(ConvBlock, self).__init__()\n self.layers = nn.Sequential(\n Conv2d(in_c, out_c, kernel, groups=groups, stride=stride, padding=padding, bias=False),\n BatchNorm2d(num_features=out_c),\n PReLU(num_parameters=out_c)",
+ "detail": "modules.arcface_torch.backbones.mobilefacenet",
+ "documentation": {}
+ },
+ {
+ "label": "ConvBlock",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.mobilefacenet",
+ "description": "modules.arcface_torch.backbones.mobilefacenet",
+ "peekOfCode": "class ConvBlock(Module):\n def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):\n super(ConvBlock, self).__init__()\n self.layers = nn.Sequential(\n Conv2d(in_c, out_c, kernel, groups=groups, stride=stride, padding=padding, bias=False),\n BatchNorm2d(num_features=out_c),\n PReLU(num_parameters=out_c)\n )\n def forward(self, x):\n return self.layers(x)",
+ "detail": "modules.arcface_torch.backbones.mobilefacenet",
+ "documentation": {}
+ },
+ {
+ "label": "LinearBlock",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.mobilefacenet",
+ "description": "modules.arcface_torch.backbones.mobilefacenet",
+ "peekOfCode": "class LinearBlock(Module):\n def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):\n super(LinearBlock, self).__init__()\n self.layers = nn.Sequential(\n Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False),\n BatchNorm2d(num_features=out_c)\n )\n def forward(self, x):\n return self.layers(x)\nclass DepthWise(Module):",
+ "detail": "modules.arcface_torch.backbones.mobilefacenet",
+ "documentation": {}
+ },
+ {
+ "label": "DepthWise",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.mobilefacenet",
+ "description": "modules.arcface_torch.backbones.mobilefacenet",
+ "peekOfCode": "class DepthWise(Module):\n def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):\n super(DepthWise, self).__init__()\n self.residual = residual\n self.layers = nn.Sequential(\n ConvBlock(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)),\n ConvBlock(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride),\n LinearBlock(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1))\n )\n def forward(self, x):",
+ "detail": "modules.arcface_torch.backbones.mobilefacenet",
+ "documentation": {}
+ },
+ {
+ "label": "Residual",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.mobilefacenet",
+ "description": "modules.arcface_torch.backbones.mobilefacenet",
+ "peekOfCode": "class Residual(Module):\n def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)):\n super(Residual, self).__init__()\n modules = []\n for _ in range(num_block):\n modules.append(DepthWise(c, c, True, kernel, stride, padding, groups))\n self.layers = Sequential(*modules)\n def forward(self, x):\n return self.layers(x)\nclass GDC(Module):",
+ "detail": "modules.arcface_torch.backbones.mobilefacenet",
+ "documentation": {}
+ },
+ {
+ "label": "GDC",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.mobilefacenet",
+ "description": "modules.arcface_torch.backbones.mobilefacenet",
+ "peekOfCode": "class GDC(Module):\n def __init__(self, embedding_size):\n super(GDC, self).__init__()\n self.layers = nn.Sequential(\n LinearBlock(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)),\n Flatten(),\n Linear(512, embedding_size, bias=False),\n BatchNorm1d(embedding_size))\n def forward(self, x):\n return self.layers(x)",
+ "detail": "modules.arcface_torch.backbones.mobilefacenet",
+ "documentation": {}
+ },
+ {
+ "label": "MobileFaceNet",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.mobilefacenet",
+ "description": "modules.arcface_torch.backbones.mobilefacenet",
+ "peekOfCode": "class MobileFaceNet(Module):\n def __init__(self, fp16=False, num_features=512, blocks=(1, 4, 6, 2), scale=2):\n super(MobileFaceNet, self).__init__()\n self.scale = scale\n self.fp16 = fp16\n self.layers = nn.ModuleList()\n self.layers.append(\n ConvBlock(3, 64 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1))\n )\n if blocks[0] == 1:",
+ "detail": "modules.arcface_torch.backbones.mobilefacenet",
+ "documentation": {}
+ },
+ {
+ "label": "get_mbf",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.mobilefacenet",
+ "description": "modules.arcface_torch.backbones.mobilefacenet",
+ "peekOfCode": "def get_mbf(fp16, num_features, blocks=(1, 4, 6, 2), scale=2):\n return MobileFaceNet(fp16, num_features, blocks, scale=scale)\ndef get_mbf_large(fp16, num_features, blocks=(2, 8, 12, 4), scale=4):\n return MobileFaceNet(fp16, num_features, blocks, scale=scale)",
+ "detail": "modules.arcface_torch.backbones.mobilefacenet",
+ "documentation": {}
+ },
+ {
+ "label": "get_mbf_large",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.backbones.mobilefacenet",
+ "description": "modules.arcface_torch.backbones.mobilefacenet",
+ "peekOfCode": "def get_mbf_large(fp16, num_features, blocks=(2, 8, 12, 4), scale=4):\n return MobileFaceNet(fp16, num_features, blocks, scale=scale)",
+ "detail": "modules.arcface_torch.backbones.mobilefacenet",
+ "documentation": {}
+ },
+ {
+ "label": "Mlp",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.vit",
+ "description": "modules.arcface_torch.backbones.vit",
+ "peekOfCode": "class Mlp(nn.Module):\n def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU6, drop=0.):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop = nn.Dropout(drop)\n def forward(self, x):",
+ "detail": "modules.arcface_torch.backbones.vit",
+ "documentation": {}
+ },
+ {
+ "label": "VITBatchNorm",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.vit",
+ "description": "modules.arcface_torch.backbones.vit",
+ "peekOfCode": "class VITBatchNorm(nn.Module):\n def __init__(self, num_features):\n super().__init__()\n self.num_features = num_features\n self.bn = nn.BatchNorm1d(num_features=num_features)\n def forward(self, x):\n return self.bn(x)\nclass Attention(nn.Module):\n def __init__(self,\n dim: int,",
+ "detail": "modules.arcface_torch.backbones.vit",
+ "documentation": {}
+ },
+ {
+ "label": "Attention",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.vit",
+ "description": "modules.arcface_torch.backbones.vit",
+ "peekOfCode": "class Attention(nn.Module):\n def __init__(self,\n dim: int,\n num_heads: int = 8,\n qkv_bias: bool = False,\n qk_scale: Optional[None] = None,\n attn_drop: float = 0.,\n proj_drop: float = 0.):\n super().__init__()\n self.num_heads = num_heads",
+ "detail": "modules.arcface_torch.backbones.vit",
+ "documentation": {}
+ },
+ {
+ "label": "Block",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.vit",
+ "description": "modules.arcface_torch.backbones.vit",
+ "peekOfCode": "class Block(nn.Module):\n def __init__(self,\n dim: int,\n num_heads: int,\n num_patches: int,\n mlp_ratio: float = 4.,\n qkv_bias: bool = False,\n qk_scale: Optional[None] = None,\n drop: float = 0.,\n attn_drop: float = 0.,",
+ "detail": "modules.arcface_torch.backbones.vit",
+ "documentation": {}
+ },
+ {
+ "label": "PatchEmbed",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.vit",
+ "description": "modules.arcface_torch.backbones.vit",
+ "peekOfCode": "class PatchEmbed(nn.Module):\n def __init__(self, img_size=108, patch_size=9, in_channels=3, embed_dim=768):\n super().__init__()\n img_size = to_2tuple(img_size)\n patch_size = to_2tuple(patch_size)\n num_patches = (img_size[1] // patch_size[1]) * \\\n (img_size[0] // patch_size[0])\n self.img_size = img_size\n self.patch_size = patch_size\n self.num_patches = num_patches",
+ "detail": "modules.arcface_torch.backbones.vit",
+ "documentation": {}
+ },
+ {
+ "label": "VisionTransformer",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.backbones.vit",
+ "description": "modules.arcface_torch.backbones.vit",
+ "peekOfCode": "class VisionTransformer(nn.Module):\n \"\"\" Vision Transformer with support for patch or hybrid CNN input stage\n \"\"\"\n def __init__(self,\n img_size: int = 112,\n patch_size: int = 16,\n in_channels: int = 3,\n num_classes: int = 1000,\n embed_dim: int = 768,\n depth: int = 12,",
+ "detail": "modules.arcface_torch.backbones.vit",
+ "documentation": {}
+ },
+ {
+ "label": "config",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config = edict()\nconfig.margin_list = (1.0, 0.0, 0.4)\nconfig.network = \"mbf\"\nconfig.resume = False\nconfig.output = None\nconfig.embedding_size = 512\nconfig.sample_rate = 0.1\nconfig.fp16 = True\nconfig.momentum = 0.9\nconfig.weight_decay = 5e-4",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.margin_list",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.margin_list = (1.0, 0.0, 0.4)\nconfig.network = \"mbf\"\nconfig.resume = False\nconfig.output = None\nconfig.embedding_size = 512\nconfig.sample_rate = 0.1\nconfig.fp16 = True\nconfig.momentum = 0.9\nconfig.weight_decay = 5e-4\nconfig.batch_size = 512 # total_batch_size = batch_size * num_gpus",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.network",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.network = \"mbf\"\nconfig.resume = False\nconfig.output = None\nconfig.embedding_size = 512\nconfig.sample_rate = 0.1\nconfig.fp16 = True\nconfig.momentum = 0.9\nconfig.weight_decay = 5e-4\nconfig.batch_size = 512 # total_batch_size = batch_size * num_gpus\nconfig.lr = 0.1 # batch size is 512",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.resume",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.resume = False\nconfig.output = None\nconfig.embedding_size = 512\nconfig.sample_rate = 0.1\nconfig.fp16 = True\nconfig.momentum = 0.9\nconfig.weight_decay = 5e-4\nconfig.batch_size = 512 # total_batch_size = batch_size * num_gpus\nconfig.lr = 0.1 # batch size is 512\nconfig.rec = \"synthetic\"",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.output",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.output = None\nconfig.embedding_size = 512\nconfig.sample_rate = 0.1\nconfig.fp16 = True\nconfig.momentum = 0.9\nconfig.weight_decay = 5e-4\nconfig.batch_size = 512 # total_batch_size = batch_size * num_gpus\nconfig.lr = 0.1 # batch size is 512\nconfig.rec = \"synthetic\"\nconfig.num_classes = 30 * 10000",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.embedding_size",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.embedding_size = 512\nconfig.sample_rate = 0.1\nconfig.fp16 = True\nconfig.momentum = 0.9\nconfig.weight_decay = 5e-4\nconfig.batch_size = 512 # total_batch_size = batch_size * num_gpus\nconfig.lr = 0.1 # batch size is 512\nconfig.rec = \"synthetic\"\nconfig.num_classes = 30 * 10000\nconfig.num_image = 100000",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.sample_rate",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.sample_rate = 0.1\nconfig.fp16 = True\nconfig.momentum = 0.9\nconfig.weight_decay = 5e-4\nconfig.batch_size = 512 # total_batch_size = batch_size * num_gpus\nconfig.lr = 0.1 # batch size is 512\nconfig.rec = \"synthetic\"\nconfig.num_classes = 30 * 10000\nconfig.num_image = 100000\nconfig.num_epoch = 30",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.fp16",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.fp16 = True\nconfig.momentum = 0.9\nconfig.weight_decay = 5e-4\nconfig.batch_size = 512 # total_batch_size = batch_size * num_gpus\nconfig.lr = 0.1 # batch size is 512\nconfig.rec = \"synthetic\"\nconfig.num_classes = 30 * 10000\nconfig.num_image = 100000\nconfig.num_epoch = 30\nconfig.warmup_epoch = -1",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.momentum",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.momentum = 0.9\nconfig.weight_decay = 5e-4\nconfig.batch_size = 512 # total_batch_size = batch_size * num_gpus\nconfig.lr = 0.1 # batch size is 512\nconfig.rec = \"synthetic\"\nconfig.num_classes = 30 * 10000\nconfig.num_image = 100000\nconfig.num_epoch = 30\nconfig.warmup_epoch = -1\nconfig.val_targets = []",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.weight_decay",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.weight_decay = 5e-4\nconfig.batch_size = 512 # total_batch_size = batch_size * num_gpus\nconfig.lr = 0.1 # batch size is 512\nconfig.rec = \"synthetic\"\nconfig.num_classes = 30 * 10000\nconfig.num_image = 100000\nconfig.num_epoch = 30\nconfig.warmup_epoch = -1\nconfig.val_targets = []",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.batch_size",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.batch_size = 512 # total_batch_size = batch_size * num_gpus\nconfig.lr = 0.1 # batch size is 512\nconfig.rec = \"synthetic\"\nconfig.num_classes = 30 * 10000\nconfig.num_image = 100000\nconfig.num_epoch = 30\nconfig.warmup_epoch = -1\nconfig.val_targets = []",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.lr",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.lr = 0.1 # batch size is 512\nconfig.rec = \"synthetic\"\nconfig.num_classes = 30 * 10000\nconfig.num_image = 100000\nconfig.num_epoch = 30\nconfig.warmup_epoch = -1\nconfig.val_targets = []",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.rec",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.rec = \"synthetic\"\nconfig.num_classes = 30 * 10000\nconfig.num_image = 100000\nconfig.num_epoch = 30\nconfig.warmup_epoch = -1\nconfig.val_targets = []",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.num_classes",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.num_classes = 30 * 10000\nconfig.num_image = 100000\nconfig.num_epoch = 30\nconfig.warmup_epoch = -1\nconfig.val_targets = []",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.num_image",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.num_image = 100000\nconfig.num_epoch = 30\nconfig.warmup_epoch = -1\nconfig.val_targets = []",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.num_epoch",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.num_epoch = 30\nconfig.warmup_epoch = -1\nconfig.val_targets = []",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.warmup_epoch",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.warmup_epoch = -1\nconfig.val_targets = []",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config.val_targets",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.3millions",
+ "description": "modules.arcface_torch.configs.3millions",
+ "peekOfCode": "config.val_targets = []",
+ "detail": "modules.arcface_torch.configs.3millions",
+ "documentation": {}
+ },
+ {
+ "label": "config",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.base",
+ "description": "modules.arcface_torch.configs.base",
+ "peekOfCode": "config = edict()\n# Margin Base Softmax\nconfig.margin_list = (1.0, 0.5, 0.0)\nconfig.network = \"r50\"\nconfig.resume = False\nconfig.save_all_states = False\nconfig.output = \"ms1mv3_arcface_r50\"\nconfig.embedding_size = 512\n# Partial FC\nconfig.sample_rate = 1",
+ "detail": "modules.arcface_torch.configs.base",
+ "documentation": {}
+ },
+ {
+ "label": "config.margin_list",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.base",
+ "description": "modules.arcface_torch.configs.base",
+ "peekOfCode": "config.margin_list = (1.0, 0.5, 0.0)\nconfig.network = \"r50\"\nconfig.resume = False\nconfig.save_all_states = False\nconfig.output = \"ms1mv3_arcface_r50\"\nconfig.embedding_size = 512\n# Partial FC\nconfig.sample_rate = 1\nconfig.interclass_filtering_threshold = 0\nconfig.fp16 = False",
+ "detail": "modules.arcface_torch.configs.base",
+ "documentation": {}
+ },
+ {
+ "label": "config.network",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.base",
+ "description": "modules.arcface_torch.configs.base",
+ "peekOfCode": "config.network = \"r50\"\nconfig.resume = False\nconfig.save_all_states = False\nconfig.output = \"ms1mv3_arcface_r50\"\nconfig.embedding_size = 512\n# Partial FC\nconfig.sample_rate = 1\nconfig.interclass_filtering_threshold = 0\nconfig.fp16 = False\nconfig.batch_size = 128",
+ "detail": "modules.arcface_torch.configs.base",
+ "documentation": {}
+ },
+ {
+ "label": "config.resume",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.base",
+ "description": "modules.arcface_torch.configs.base",
+ "peekOfCode": "config.resume = False\nconfig.save_all_states = False\nconfig.output = \"ms1mv3_arcface_r50\"\nconfig.embedding_size = 512\n# Partial FC\nconfig.sample_rate = 1\nconfig.interclass_filtering_threshold = 0\nconfig.fp16 = False\nconfig.batch_size = 128\n# For SGD ",
+ "detail": "modules.arcface_torch.configs.base",
+ "documentation": {}
+ },
+ {
+ "label": "config.save_all_states",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.base",
+ "description": "modules.arcface_torch.configs.base",
+ "peekOfCode": "config.save_all_states = False\nconfig.output = \"ms1mv3_arcface_r50\"\nconfig.embedding_size = 512\n# Partial FC\nconfig.sample_rate = 1\nconfig.interclass_filtering_threshold = 0\nconfig.fp16 = False\nconfig.batch_size = 128\n# For SGD \nconfig.optimizer = \"sgd\"",
+ "detail": "modules.arcface_torch.configs.base",
+ "documentation": {}
+ },
+ {
+ "label": "config.output",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.base",
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+ "detail": "modules.arcface_torch.configs.wf4m_r50",
+ "documentation": {}
+ },
+ {
+ "label": "config.verbose",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.wf4m_r50",
+ "description": "modules.arcface_torch.configs.wf4m_r50",
+ "peekOfCode": "config.verbose = 2000\nconfig.dali = False\nconfig.rec = \"/train_tmp/WebFace4M\"\nconfig.num_classes = 205990\nconfig.num_image = 4235242\nconfig.num_epoch = 20\nconfig.warmup_epoch = 0\nconfig.val_targets = ['lfw', 'cfp_fp', \"agedb_30\"]",
+ "detail": "modules.arcface_torch.configs.wf4m_r50",
+ "documentation": {}
+ },
+ {
+ "label": "config.dali",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.wf4m_r50",
+ "description": "modules.arcface_torch.configs.wf4m_r50",
+ "peekOfCode": "config.dali = False\nconfig.rec = \"/train_tmp/WebFace4M\"\nconfig.num_classes = 205990\nconfig.num_image = 4235242\nconfig.num_epoch = 20\nconfig.warmup_epoch = 0\nconfig.val_targets = ['lfw', 'cfp_fp', \"agedb_30\"]",
+ "detail": "modules.arcface_torch.configs.wf4m_r50",
+ "documentation": {}
+ },
+ {
+ "label": "config.rec",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.wf4m_r50",
+ "description": "modules.arcface_torch.configs.wf4m_r50",
+ "peekOfCode": "config.rec = \"/train_tmp/WebFace4M\"\nconfig.num_classes = 205990\nconfig.num_image = 4235242\nconfig.num_epoch = 20\nconfig.warmup_epoch = 0\nconfig.val_targets = ['lfw', 'cfp_fp', \"agedb_30\"]",
+ "detail": "modules.arcface_torch.configs.wf4m_r50",
+ "documentation": {}
+ },
+ {
+ "label": "config.num_classes",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.wf4m_r50",
+ "description": "modules.arcface_torch.configs.wf4m_r50",
+ "peekOfCode": "config.num_classes = 205990\nconfig.num_image = 4235242\nconfig.num_epoch = 20\nconfig.warmup_epoch = 0\nconfig.val_targets = ['lfw', 'cfp_fp', \"agedb_30\"]",
+ "detail": "modules.arcface_torch.configs.wf4m_r50",
+ "documentation": {}
+ },
+ {
+ "label": "config.num_image",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.wf4m_r50",
+ "description": "modules.arcface_torch.configs.wf4m_r50",
+ "peekOfCode": "config.num_image = 4235242\nconfig.num_epoch = 20\nconfig.warmup_epoch = 0\nconfig.val_targets = ['lfw', 'cfp_fp', \"agedb_30\"]",
+ "detail": "modules.arcface_torch.configs.wf4m_r50",
+ "documentation": {}
+ },
+ {
+ "label": "config.num_epoch",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.wf4m_r50",
+ "description": "modules.arcface_torch.configs.wf4m_r50",
+ "peekOfCode": "config.num_epoch = 20\nconfig.warmup_epoch = 0\nconfig.val_targets = ['lfw', 'cfp_fp', \"agedb_30\"]",
+ "detail": "modules.arcface_torch.configs.wf4m_r50",
+ "documentation": {}
+ },
+ {
+ "label": "config.warmup_epoch",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.wf4m_r50",
+ "description": "modules.arcface_torch.configs.wf4m_r50",
+ "peekOfCode": "config.warmup_epoch = 0\nconfig.val_targets = ['lfw', 'cfp_fp', \"agedb_30\"]",
+ "detail": "modules.arcface_torch.configs.wf4m_r50",
+ "documentation": {}
+ },
+ {
+ "label": "config.val_targets",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.configs.wf4m_r50",
+ "description": "modules.arcface_torch.configs.wf4m_r50",
+ "peekOfCode": "config.val_targets = ['lfw', 'cfp_fp', \"agedb_30\"]",
+ "detail": "modules.arcface_torch.configs.wf4m_r50",
+ "documentation": {}
+ },
+ {
+ "label": "LFold",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.eval.verification",
+ "description": "modules.arcface_torch.eval.verification",
+ "peekOfCode": "class LFold:\n def __init__(self, n_splits=2, shuffle=False):\n self.n_splits = n_splits\n if self.n_splits > 1:\n self.k_fold = KFold(n_splits=n_splits, shuffle=shuffle)\n def split(self, indices):\n if self.n_splits > 1:\n return self.k_fold.split(indices)\n else:\n return [(indices, indices)]",
+ "detail": "modules.arcface_torch.eval.verification",
+ "documentation": {}
+ },
+ {
+ "label": "calculate_roc",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval.verification",
+ "description": "modules.arcface_torch.eval.verification",
+ "peekOfCode": "def calculate_roc(thresholds,\n embeddings1,\n embeddings2,\n actual_issame,\n nrof_folds=10,\n pca=0):\n assert (embeddings1.shape[0] == embeddings2.shape[0])\n assert (embeddings1.shape[1] == embeddings2.shape[1])\n nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n nrof_thresholds = len(thresholds)",
+ "detail": "modules.arcface_torch.eval.verification",
+ "documentation": {}
+ },
+ {
+ "label": "calculate_accuracy",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval.verification",
+ "description": "modules.arcface_torch.eval.verification",
+ "peekOfCode": "def calculate_accuracy(threshold, dist, actual_issame):\n predict_issame = np.less(dist, threshold)\n tp = np.sum(np.logical_and(predict_issame, actual_issame))\n fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))\n tn = np.sum(\n np.logical_and(np.logical_not(predict_issame),\n np.logical_not(actual_issame)))\n fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))\n tpr = 0 if (tp + fn == 0) else float(tp) / float(tp + fn)\n fpr = 0 if (fp + tn == 0) else float(fp) / float(fp + tn)",
+ "detail": "modules.arcface_torch.eval.verification",
+ "documentation": {}
+ },
+ {
+ "label": "calculate_val",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval.verification",
+ "description": "modules.arcface_torch.eval.verification",
+ "peekOfCode": "def calculate_val(thresholds,\n embeddings1,\n embeddings2,\n actual_issame,\n far_target,\n nrof_folds=10):\n assert (embeddings1.shape[0] == embeddings2.shape[0])\n assert (embeddings1.shape[1] == embeddings2.shape[1])\n nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n nrof_thresholds = len(thresholds)",
+ "detail": "modules.arcface_torch.eval.verification",
+ "documentation": {}
+ },
+ {
+ "label": "calculate_val_far",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval.verification",
+ "description": "modules.arcface_torch.eval.verification",
+ "peekOfCode": "def calculate_val_far(threshold, dist, actual_issame):\n predict_issame = np.less(dist, threshold)\n true_accept = np.sum(np.logical_and(predict_issame, actual_issame))\n false_accept = np.sum(\n np.logical_and(predict_issame, np.logical_not(actual_issame)))\n n_same = np.sum(actual_issame)\n n_diff = np.sum(np.logical_not(actual_issame))\n # print(true_accept, false_accept)\n # print(n_same, n_diff)\n val = float(true_accept) / float(n_same)",
+ "detail": "modules.arcface_torch.eval.verification",
+ "documentation": {}
+ },
+ {
+ "label": "evaluate",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval.verification",
+ "description": "modules.arcface_torch.eval.verification",
+ "peekOfCode": "def evaluate(embeddings, actual_issame, nrof_folds=10, pca=0):\n # Calculate evaluation metrics\n thresholds = np.arange(0, 4, 0.01)\n embeddings1 = embeddings[0::2]\n embeddings2 = embeddings[1::2]\n tpr, fpr, accuracy = calculate_roc(thresholds,\n embeddings1,\n embeddings2,\n np.asarray(actual_issame),\n nrof_folds=nrof_folds,",
+ "detail": "modules.arcface_torch.eval.verification",
+ "documentation": {}
+ },
+ {
+ "label": "load_bin",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval.verification",
+ "description": "modules.arcface_torch.eval.verification",
+ "peekOfCode": "def load_bin(path, image_size):\n try:\n with open(path, 'rb') as f:\n bins, issame_list = pickle.load(f) # py2\n except UnicodeDecodeError as e:\n with open(path, 'rb') as f:\n bins, issame_list = pickle.load(f, encoding='bytes') # py3\n data_list = []\n for flip in [0, 1]:\n data = torch.empty((len(issame_list) * 2, 3, image_size[0], image_size[1]))",
+ "detail": "modules.arcface_torch.eval.verification",
+ "documentation": {}
+ },
+ {
+ "label": "test",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval.verification",
+ "description": "modules.arcface_torch.eval.verification",
+ "peekOfCode": "def test(data_set, backbone, batch_size, nfolds=10):\n print('testing verification..')\n data_list = data_set[0]\n issame_list = data_set[1]\n embeddings_list = []\n time_consumed = 0.0\n for i in range(len(data_list)):\n data = data_list[i]\n embeddings = None\n ba = 0",
+ "detail": "modules.arcface_torch.eval.verification",
+ "documentation": {}
+ },
+ {
+ "label": "dumpR",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval.verification",
+ "description": "modules.arcface_torch.eval.verification",
+ "peekOfCode": "def dumpR(data_set,\n backbone,\n batch_size,\n name='',\n data_extra=None,\n label_shape=None):\n print('dump verification embedding..')\n data_list = data_set[0]\n issame_list = data_set[1]\n embeddings_list = []",
+ "detail": "modules.arcface_torch.eval.verification",
+ "documentation": {}
+ },
+ {
+ "label": "read_template_pair_list",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.utils.plot",
+ "description": "modules.arcface_torch.utils.plot",
+ "peekOfCode": "def read_template_pair_list(path):\n pairs = pd.read_csv(path, sep=' ', header=None).values\n t1 = pairs[:, 0].astype(np.int)\n t2 = pairs[:, 1].astype(np.int)\n label = pairs[:, 2].astype(np.int)\n return t1, t2, label\np1, p2, label = read_template_pair_list(\n os.path.join('%s/meta' % image_path,\n '%s_template_pair_label.txt' % 'ijbc'))\nmethods = []",
+ "detail": "modules.arcface_torch.utils.plot",
+ "documentation": {}
+ },
+ {
+ "label": "files",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.utils.plot",
+ "description": "modules.arcface_torch.utils.plot",
+ "peekOfCode": "files = [x.strip() for x in files]\nimage_path = \"/train_tmp/IJB_release/IJBC\"\ndef read_template_pair_list(path):\n pairs = pd.read_csv(path, sep=' ', header=None).values\n t1 = pairs[:, 0].astype(np.int)\n t2 = pairs[:, 1].astype(np.int)\n label = pairs[:, 2].astype(np.int)\n return t1, t2, label\np1, p2, label = read_template_pair_list(\n os.path.join('%s/meta' % image_path,",
+ "detail": "modules.arcface_torch.utils.plot",
+ "documentation": {}
+ },
+ {
+ "label": "image_path",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.utils.plot",
+ "description": "modules.arcface_torch.utils.plot",
+ "peekOfCode": "image_path = \"/train_tmp/IJB_release/IJBC\"\ndef read_template_pair_list(path):\n pairs = pd.read_csv(path, sep=' ', header=None).values\n t1 = pairs[:, 0].astype(np.int)\n t2 = pairs[:, 1].astype(np.int)\n label = pairs[:, 2].astype(np.int)\n return t1, t2, label\np1, p2, label = read_template_pair_list(\n os.path.join('%s/meta' % image_path,\n '%s_template_pair_label.txt' % 'ijbc'))",
+ "detail": "modules.arcface_torch.utils.plot",
+ "documentation": {}
+ },
+ {
+ "label": "methods",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.utils.plot",
+ "description": "modules.arcface_torch.utils.plot",
+ "peekOfCode": "methods = []\nscores = []\nfor file in files:\n methods.append(file)\n scores.append(np.load(file))\nmethods = np.array(methods)\nscores = dict(zip(methods, scores))\ncolours = dict(\n zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\nx_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]",
+ "detail": "modules.arcface_torch.utils.plot",
+ "documentation": {}
+ },
+ {
+ "label": "scores",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.utils.plot",
+ "description": "modules.arcface_torch.utils.plot",
+ "peekOfCode": "scores = []\nfor file in files:\n methods.append(file)\n scores.append(np.load(file))\nmethods = np.array(methods)\nscores = dict(zip(methods, scores))\ncolours = dict(\n zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\nx_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]\ntpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])",
+ "detail": "modules.arcface_torch.utils.plot",
+ "documentation": {}
+ },
+ {
+ "label": "methods",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.utils.plot",
+ "description": "modules.arcface_torch.utils.plot",
+ "peekOfCode": "methods = np.array(methods)\nscores = dict(zip(methods, scores))\ncolours = dict(\n zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\nx_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]\ntpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])\nfig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)",
+ "detail": "modules.arcface_torch.utils.plot",
+ "documentation": {}
+ },
+ {
+ "label": "scores",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.utils.plot",
+ "description": "modules.arcface_torch.utils.plot",
+ "peekOfCode": "scores = dict(zip(methods, scores))\ncolours = dict(\n zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\nx_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]\ntpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])\nfig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)\n fpr = np.flipud(fpr)",
+ "detail": "modules.arcface_torch.utils.plot",
+ "documentation": {}
+ },
+ {
+ "label": "colours",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.utils.plot",
+ "description": "modules.arcface_torch.utils.plot",
+ "peekOfCode": "colours = dict(\n zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\nx_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]\ntpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])\nfig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)\n fpr = np.flipud(fpr)\n tpr = np.flipud(tpr) # select largest tpr at same fpr",
+ "detail": "modules.arcface_torch.utils.plot",
+ "documentation": {}
+ },
+ {
+ "label": "x_labels",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.utils.plot",
+ "description": "modules.arcface_torch.utils.plot",
+ "peekOfCode": "x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]\ntpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])\nfig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)\n fpr = np.flipud(fpr)\n tpr = np.flipud(tpr) # select largest tpr at same fpr\n plt.plot(fpr,\n tpr,",
+ "detail": "modules.arcface_torch.utils.plot",
+ "documentation": {}
+ },
+ {
+ "label": "tpr_fpr_table",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.utils.plot",
+ "description": "modules.arcface_torch.utils.plot",
+ "peekOfCode": "tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])\nfig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)\n fpr = np.flipud(fpr)\n tpr = np.flipud(tpr) # select largest tpr at same fpr\n plt.plot(fpr,\n tpr,\n color=colours[method],",
+ "detail": "modules.arcface_torch.utils.plot",
+ "documentation": {}
+ },
+ {
+ "label": "fig",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.utils.plot",
+ "description": "modules.arcface_torch.utils.plot",
+ "peekOfCode": "fig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)\n fpr = np.flipud(fpr)\n tpr = np.flipud(tpr) # select largest tpr at same fpr\n plt.plot(fpr,\n tpr,\n color=colours[method],\n lw=1,",
+ "detail": "modules.arcface_torch.utils.plot",
+ "documentation": {}
+ },
+ {
+ "label": "CallBackVerification",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.utils.utils_callbacks",
+ "description": "modules.arcface_torch.utils.utils_callbacks",
+ "peekOfCode": "class CallBackVerification(object):\n def __init__(self, val_targets, rec_prefix, summary_writer=None, image_size=(112, 112)):\n self.rank: int = distributed.get_rank()\n self.highest_acc: float = 0.0\n self.highest_acc_list: List[float] = [0.0] * len(val_targets)\n self.ver_list: List[object] = []\n self.ver_name_list: List[str] = []\n if self.rank is 0:\n self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size)\n self.summary_writer = summary_writer",
+ "detail": "modules.arcface_torch.utils.utils_callbacks",
+ "documentation": {}
+ },
+ {
+ "label": "CallBackLogging",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.utils.utils_callbacks",
+ "description": "modules.arcface_torch.utils.utils_callbacks",
+ "peekOfCode": "class CallBackLogging(object):\n def __init__(self, frequent, total_step, batch_size, start_step=0,writer=None):\n self.frequent: int = frequent\n self.rank: int = distributed.get_rank()\n self.world_size: int = distributed.get_world_size()\n self.time_start = time.time()\n self.total_step: int = total_step\n self.start_step: int = start_step\n self.batch_size: int = batch_size\n self.writer = writer",
+ "detail": "modules.arcface_torch.utils.utils_callbacks",
+ "documentation": {}
+ },
+ {
+ "label": "get_config",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.utils.utils_config",
+ "description": "modules.arcface_torch.utils.utils_config",
+ "peekOfCode": "def get_config(config_file):\n assert config_file.startswith('configs/'), 'config file setting must start with configs/'\n temp_config_name = osp.basename(config_file)\n temp_module_name = osp.splitext(temp_config_name)[0]\n config = importlib.import_module(\"configs.base\")\n cfg = config.config\n config = importlib.import_module(\"configs.%s\" % temp_module_name)\n job_cfg = config.config\n cfg.update(job_cfg)\n if cfg.output is None:",
+ "detail": "modules.arcface_torch.utils.utils_config",
+ "documentation": {}
+ },
+ {
+ "label": "DistributedSampler",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "description": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "peekOfCode": "class DistributedSampler(_DistributedSampler):\n def __init__(\n self,\n dataset,\n num_replicas=None, # world_size\n rank=None, # local_rank\n shuffle=True,\n seed=0,\n ):\n super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)",
+ "detail": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "documentation": {}
+ },
+ {
+ "label": "setup_seed",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "description": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "peekOfCode": "def setup_seed(seed, cuda_deterministic=True):\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n np.random.seed(seed)\n random.seed(seed)\n os.environ[\"PYTHONHASHSEED\"] = str(seed)\n if cuda_deterministic: # slower, more reproducible\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n else: # faster, less reproducible",
+ "detail": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "documentation": {}
+ },
+ {
+ "label": "worker_init_fn",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "description": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "peekOfCode": "def worker_init_fn(worker_id, num_workers, rank, seed):\n # The seed of each worker equals to\n # num_worker * rank + worker_id + user_seed\n worker_seed = num_workers * rank + worker_id + seed\n np.random.seed(worker_seed)\n random.seed(worker_seed)\n torch.manual_seed(worker_seed)\ndef get_dist_info():\n if dist.is_available() and dist.is_initialized():\n rank = dist.get_rank()",
+ "detail": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "documentation": {}
+ },
+ {
+ "label": "get_dist_info",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "description": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "peekOfCode": "def get_dist_info():\n if dist.is_available() and dist.is_initialized():\n rank = dist.get_rank()\n world_size = dist.get_world_size()\n else:\n rank = 0\n world_size = 1\n return rank, world_size\ndef sync_random_seed(seed=None, device=\"cuda\"):\n \"\"\"Make sure different ranks share the same seed.",
+ "detail": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "documentation": {}
+ },
+ {
+ "label": "sync_random_seed",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "description": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "peekOfCode": "def sync_random_seed(seed=None, device=\"cuda\"):\n \"\"\"Make sure different ranks share the same seed.\n All workers must call this function, otherwise it will deadlock.\n This method is generally used in `DistributedSampler`,\n because the seed should be identical across all processes\n in the distributed group.\n In distributed sampling, different ranks should sample non-overlapped\n data in the dataset. Therefore, this function is used to make sure that\n each rank shuffles the data indices in the same order based\n on the same seed. Then different ranks could use different indices",
+ "detail": "modules.arcface_torch.utils.utils_distributed_sampler",
+ "documentation": {}
+ },
+ {
+ "label": "AverageMeter",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.utils.utils_logging",
+ "description": "modules.arcface_torch.utils.utils_logging",
+ "peekOfCode": "class AverageMeter(object):\n \"\"\"Computes and stores the average and current value\n \"\"\"\n def __init__(self):\n self.val = None\n self.avg = None\n self.sum = None\n self.count = None\n self.reset()\n def reset(self):",
+ "detail": "modules.arcface_torch.utils.utils_logging",
+ "documentation": {}
+ },
+ {
+ "label": "init_logging",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.utils.utils_logging",
+ "description": "modules.arcface_torch.utils.utils_logging",
+ "peekOfCode": "def init_logging(rank, models_root):\n if rank == 0:\n log_root = logging.getLogger()\n log_root.setLevel(logging.INFO)\n formatter = logging.Formatter(\"Training: %(asctime)s-%(message)s\")\n handler_file = logging.FileHandler(os.path.join(models_root, \"training.log\"))\n handler_stream = logging.StreamHandler(sys.stdout)\n handler_file.setFormatter(formatter)\n handler_stream.setFormatter(formatter)\n log_root.addHandler(handler_file)",
+ "detail": "modules.arcface_torch.utils.utils_logging",
+ "documentation": {}
+ },
+ {
+ "label": "BackgroundGenerator",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.dataset",
+ "description": "modules.arcface_torch.dataset",
+ "peekOfCode": "class BackgroundGenerator(threading.Thread):\n def __init__(self, generator, local_rank, max_prefetch=6):\n super(BackgroundGenerator, self).__init__()\n self.queue = Queue.Queue(max_prefetch)\n self.generator = generator\n self.local_rank = local_rank\n self.daemon = True\n self.start()\n def run(self):\n torch.cuda.set_device(self.local_rank)",
+ "detail": "modules.arcface_torch.dataset",
+ "documentation": {}
+ },
+ {
+ "label": "DataLoaderX",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.dataset",
+ "description": "modules.arcface_torch.dataset",
+ "peekOfCode": "class DataLoaderX(DataLoader):\n def __init__(self, local_rank, **kwargs):\n super(DataLoaderX, self).__init__(**kwargs)\n self.stream = torch.cuda.Stream(local_rank)\n self.local_rank = local_rank\n def __iter__(self):\n self.iter = super(DataLoaderX, self).__iter__()\n self.iter = BackgroundGenerator(self.iter, self.local_rank)\n self.preload()\n return self",
+ "detail": "modules.arcface_torch.dataset",
+ "documentation": {}
+ },
+ {
+ "label": "MXFaceDataset",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.dataset",
+ "description": "modules.arcface_torch.dataset",
+ "peekOfCode": "class MXFaceDataset(Dataset):\n def __init__(self, root_dir, local_rank):\n super(MXFaceDataset, self).__init__()\n self.transform = transforms.Compose(\n [transforms.ToPILImage(),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),\n ])\n self.root_dir = root_dir",
+ "detail": "modules.arcface_torch.dataset",
+ "documentation": {}
+ },
+ {
+ "label": "SyntheticDataset",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.dataset",
+ "description": "modules.arcface_torch.dataset",
+ "peekOfCode": "class SyntheticDataset(Dataset):\n def __init__(self):\n super(SyntheticDataset, self).__init__()\n img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32)\n img = np.transpose(img, (2, 0, 1))\n img = torch.from_numpy(img).squeeze(0).float()\n img = ((img / 255) - 0.5) / 0.5\n self.img = img\n self.label = 1\n def __getitem__(self, index):",
+ "detail": "modules.arcface_torch.dataset",
+ "documentation": {}
+ },
+ {
+ "label": "DALIWarper",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.dataset",
+ "description": "modules.arcface_torch.dataset",
+ "peekOfCode": "class DALIWarper(object):\n def __init__(self, dali_iter):\n self.iter = dali_iter\n def __next__(self):\n data_dict = self.iter.__next__()[0]\n tensor_data = data_dict['data'].cuda()\n tensor_label: torch.Tensor = data_dict['label'].cuda().long()\n tensor_label.squeeze_()\n return tensor_data, tensor_label\n def __iter__(self):",
+ "detail": "modules.arcface_torch.dataset",
+ "documentation": {}
+ },
+ {
+ "label": "get_dataloader",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.dataset",
+ "description": "modules.arcface_torch.dataset",
+ "peekOfCode": "def get_dataloader(\n root_dir,\n local_rank,\n batch_size,\n dali = False,\n seed = 2048,\n num_workers = 2,\n ) -> Iterable:\n rec = os.path.join(root_dir, 'train.rec')\n idx = os.path.join(root_dir, 'train.idx')",
+ "detail": "modules.arcface_torch.dataset",
+ "documentation": {}
+ },
+ {
+ "label": "dali_data_iter",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.dataset",
+ "description": "modules.arcface_torch.dataset",
+ "peekOfCode": "def dali_data_iter(\n batch_size: int, rec_file: str, idx_file: str, num_threads: int,\n initial_fill=32768, random_shuffle=True,\n prefetch_queue_depth=1, local_rank=0, name=\"reader\",\n mean=(127.5, 127.5, 127.5), \n std=(127.5, 127.5, 127.5)):\n \"\"\"\n Parameters:\n ----------\n initial_fill: int",
+ "detail": "modules.arcface_torch.dataset",
+ "documentation": {}
+ },
+ {
+ "label": "Embedding",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "class Embedding(object):\n def __init__(self, prefix, data_shape, batch_size=1):\n image_size = (112, 112)\n self.image_size = image_size\n weight = torch.load(prefix)\n resnet = get_model(args.network, dropout=0, fp16=False).cuda()\n resnet.load_state_dict(weight)\n model = torch.nn.DataParallel(resnet)\n self.model = model\n self.model.eval()",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "divideIntoNstrand",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "def divideIntoNstrand(listTemp, n):\n twoList = [[] for i in range(n)]\n for i, e in enumerate(listTemp):\n twoList[i % n].append(e)\n return twoList\ndef read_template_media_list(path):\n # ijb_meta = np.loadtxt(path, dtype=str)\n ijb_meta = pd.read_csv(path, sep=' ', header=None).values\n templates = ijb_meta[:, 1].astype(np.int)\n medias = ijb_meta[:, 2].astype(np.int)",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "read_template_media_list",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "def read_template_media_list(path):\n # ijb_meta = np.loadtxt(path, dtype=str)\n ijb_meta = pd.read_csv(path, sep=' ', header=None).values\n templates = ijb_meta[:, 1].astype(np.int)\n medias = ijb_meta[:, 2].astype(np.int)\n return templates, medias\n# In[ ]:\ndef read_template_pair_list(path):\n # pairs = np.loadtxt(path, dtype=str)\n pairs = pd.read_csv(path, sep=' ', header=None).values",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "read_template_pair_list",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "def read_template_pair_list(path):\n # pairs = np.loadtxt(path, dtype=str)\n pairs = pd.read_csv(path, sep=' ', header=None).values\n # print(pairs.shape)\n # print(pairs[:, 0].astype(np.int))\n t1 = pairs[:, 0].astype(np.int)\n t2 = pairs[:, 1].astype(np.int)\n label = pairs[:, 2].astype(np.int)\n return t1, t2, label\n# In[ ]:",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "read_image_feature",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "def read_image_feature(path):\n with open(path, 'rb') as fid:\n img_feats = pickle.load(fid)\n return img_feats\n# In[ ]:\ndef get_image_feature(img_path, files_list, model_path, epoch, gpu_id):\n batch_size = args.batch_size\n data_shape = (3, 112, 112)\n files = files_list\n print('files:', len(files))",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "get_image_feature",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "def get_image_feature(img_path, files_list, model_path, epoch, gpu_id):\n batch_size = args.batch_size\n data_shape = (3, 112, 112)\n files = files_list\n print('files:', len(files))\n rare_size = len(files) % batch_size\n faceness_scores = []\n batch = 0\n img_feats = np.empty((len(files), 1024), dtype=np.float32)\n batch_data = np.empty((2 * batch_size, 3, 112, 112))",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "image2template_feature",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "def image2template_feature(img_feats=None, templates=None, medias=None):\n # ==========================================================\n # 1. face image feature l2 normalization. img_feats:[number_image x feats_dim]\n # 2. compute media feature.\n # 3. compute template feature.\n # ==========================================================\n unique_templates = np.unique(templates)\n template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))\n for count_template, uqt in enumerate(unique_templates):\n (ind_t,) = np.where(templates == uqt)",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "verification",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "def verification(template_norm_feats=None,\n unique_templates=None,\n p1=None,\n p2=None):\n # ==========================================================\n # Compute set-to-set Similarity Score.\n # ==========================================================\n template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)\n for count_template, uqt in enumerate(unique_templates):\n template2id[uqt] = count_template",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "verification2",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "def verification2(template_norm_feats=None,\n unique_templates=None,\n p1=None,\n p2=None):\n template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)\n for count_template, uqt in enumerate(unique_templates):\n template2id[uqt] = count_template\n score = np.zeros((len(p1),)) # save cosine distance between pairs\n total_pairs = np.array(range(len(p1)))\n batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "read_score",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "def read_score(path):\n with open(path, 'rb') as fid:\n img_feats = pickle.load(fid)\n return img_feats\n# # Step1: Load Meta Data\n# In[ ]:\nassert target == 'IJBC' or target == 'IJBB'\n# =============================================================\n# load image and template relationships for template feature embedding\n# tid --> template id, mid --> media id",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "parser",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "parser = argparse.ArgumentParser(description='do ijb test')\n# general\nparser.add_argument('--model-prefix', default='', help='path to load model.')\nparser.add_argument('--image-path', default='', type=str, help='')\nparser.add_argument('--result-dir', default='.', type=str, help='')\nparser.add_argument('--batch-size', default=128, type=int, help='')\nparser.add_argument('--network', default='iresnet50', type=str, help='')\nparser.add_argument('--job', default='insightface', type=str, help='job name')\nparser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB')\nargs = parser.parse_args()",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "args",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "args = parser.parse_args()\ntarget = args.target\nmodel_path = args.model_prefix\nimage_path = args.image_path\nresult_dir = args.result_dir\ngpu_id = None\nuse_norm_score = True # if Ture, TestMode(N1)\nuse_detector_score = True # if Ture, TestMode(D1)\nuse_flip_test = True # if Ture, TestMode(F1)\njob = args.job",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "target",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "target = args.target\nmodel_path = args.model_prefix\nimage_path = args.image_path\nresult_dir = args.result_dir\ngpu_id = None\nuse_norm_score = True # if Ture, TestMode(N1)\nuse_detector_score = True # if Ture, TestMode(D1)\nuse_flip_test = True # if Ture, TestMode(F1)\njob = args.job\nbatch_size = args.batch_size",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "model_path",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "model_path = args.model_prefix\nimage_path = args.image_path\nresult_dir = args.result_dir\ngpu_id = None\nuse_norm_score = True # if Ture, TestMode(N1)\nuse_detector_score = True # if Ture, TestMode(D1)\nuse_flip_test = True # if Ture, TestMode(F1)\njob = args.job\nbatch_size = args.batch_size\nclass Embedding(object):",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "image_path",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "image_path = args.image_path\nresult_dir = args.result_dir\ngpu_id = None\nuse_norm_score = True # if Ture, TestMode(N1)\nuse_detector_score = True # if Ture, TestMode(D1)\nuse_flip_test = True # if Ture, TestMode(F1)\njob = args.job\nbatch_size = args.batch_size\nclass Embedding(object):\n def __init__(self, prefix, data_shape, batch_size=1):",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "result_dir",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "result_dir = args.result_dir\ngpu_id = None\nuse_norm_score = True # if Ture, TestMode(N1)\nuse_detector_score = True # if Ture, TestMode(D1)\nuse_flip_test = True # if Ture, TestMode(F1)\njob = args.job\nbatch_size = args.batch_size\nclass Embedding(object):\n def __init__(self, prefix, data_shape, batch_size=1):\n image_size = (112, 112)",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "gpu_id",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "gpu_id = None\nuse_norm_score = True # if Ture, TestMode(N1)\nuse_detector_score = True # if Ture, TestMode(D1)\nuse_flip_test = True # if Ture, TestMode(F1)\njob = args.job\nbatch_size = args.batch_size\nclass Embedding(object):\n def __init__(self, prefix, data_shape, batch_size=1):\n image_size = (112, 112)\n self.image_size = image_size",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "use_norm_score",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "use_norm_score = True # if Ture, TestMode(N1)\nuse_detector_score = True # if Ture, TestMode(D1)\nuse_flip_test = True # if Ture, TestMode(F1)\njob = args.job\nbatch_size = args.batch_size\nclass Embedding(object):\n def __init__(self, prefix, data_shape, batch_size=1):\n image_size = (112, 112)\n self.image_size = image_size\n weight = torch.load(prefix)",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "use_detector_score",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "use_detector_score = True # if Ture, TestMode(D1)\nuse_flip_test = True # if Ture, TestMode(F1)\njob = args.job\nbatch_size = args.batch_size\nclass Embedding(object):\n def __init__(self, prefix, data_shape, batch_size=1):\n image_size = (112, 112)\n self.image_size = image_size\n weight = torch.load(prefix)\n resnet = get_model(args.network, dropout=0, fp16=False).cuda()",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "use_flip_test",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "use_flip_test = True # if Ture, TestMode(F1)\njob = args.job\nbatch_size = args.batch_size\nclass Embedding(object):\n def __init__(self, prefix, data_shape, batch_size=1):\n image_size = (112, 112)\n self.image_size = image_size\n weight = torch.load(prefix)\n resnet = get_model(args.network, dropout=0, fp16=False).cuda()\n resnet.load_state_dict(weight)",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "job",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "job = args.job\nbatch_size = args.batch_size\nclass Embedding(object):\n def __init__(self, prefix, data_shape, batch_size=1):\n image_size = (112, 112)\n self.image_size = image_size\n weight = torch.load(prefix)\n resnet = get_model(args.network, dropout=0, fp16=False).cuda()\n resnet.load_state_dict(weight)\n model = torch.nn.DataParallel(resnet)",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "batch_size",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "batch_size = args.batch_size\nclass Embedding(object):\n def __init__(self, prefix, data_shape, batch_size=1):\n image_size = (112, 112)\n self.image_size = image_size\n weight = torch.load(prefix)\n resnet = get_model(args.network, dropout=0, fp16=False).cuda()\n resnet.load_state_dict(weight)\n model = torch.nn.DataParallel(resnet)\n self.model = model",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "start",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "start = timeit.default_timer()\ntemplates, medias = read_template_media_list(\n os.path.join('%s/meta' % image_path,\n '%s_face_tid_mid.txt' % target.lower()))\nstop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))\n# In[ ]:\n# =============================================================\n# load template pairs for template-to-template verification\n# tid : template id, label : 1/0",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "stop",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "stop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))\n# In[ ]:\n# =============================================================\n# load template pairs for template-to-template verification\n# tid : template id, label : 1/0\n# format:\n# tid_1 tid_2 label\n# =============================================================\nstart = timeit.default_timer()",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "start",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "start = timeit.default_timer()\np1, p2, label = read_template_pair_list(\n os.path.join('%s/meta' % image_path,\n '%s_template_pair_label.txt' % target.lower()))\nstop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))\n# # Step 2: Get Image Features\n# In[ ]:\n# =============================================================\n# load image features",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "stop",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "stop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))\n# # Step 2: Get Image Features\n# In[ ]:\n# =============================================================\n# load image features\n# format:\n# img_feats: [image_num x feats_dim] (227630, 512)\n# =============================================================\nstart = timeit.default_timer()",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "start",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "start = timeit.default_timer()\nimg_path = '%s/loose_crop' % image_path\nimg_list_path = '%s/meta/%s_name_5pts_score.txt' % (image_path, target.lower())\nimg_list = open(img_list_path)\nfiles = img_list.readlines()\n# files_list = divideIntoNstrand(files, rank_size)\nfiles_list = files\n# img_feats\n# for i in range(rank_size):\nimg_feats, faceness_scores = get_image_feature(img_path, files_list,",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "img_path",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "img_path = '%s/loose_crop' % image_path\nimg_list_path = '%s/meta/%s_name_5pts_score.txt' % (image_path, target.lower())\nimg_list = open(img_list_path)\nfiles = img_list.readlines()\n# files_list = divideIntoNstrand(files, rank_size)\nfiles_list = files\n# img_feats\n# for i in range(rank_size):\nimg_feats, faceness_scores = get_image_feature(img_path, files_list,\n model_path, 0, gpu_id)",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "img_list_path",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "img_list_path = '%s/meta/%s_name_5pts_score.txt' % (image_path, target.lower())\nimg_list = open(img_list_path)\nfiles = img_list.readlines()\n# files_list = divideIntoNstrand(files, rank_size)\nfiles_list = files\n# img_feats\n# for i in range(rank_size):\nimg_feats, faceness_scores = get_image_feature(img_path, files_list,\n model_path, 0, gpu_id)\nstop = timeit.default_timer()",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "img_list",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "img_list = open(img_list_path)\nfiles = img_list.readlines()\n# files_list = divideIntoNstrand(files, rank_size)\nfiles_list = files\n# img_feats\n# for i in range(rank_size):\nimg_feats, faceness_scores = get_image_feature(img_path, files_list,\n model_path, 0, gpu_id)\nstop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "files",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "files = img_list.readlines()\n# files_list = divideIntoNstrand(files, rank_size)\nfiles_list = files\n# img_feats\n# for i in range(rank_size):\nimg_feats, faceness_scores = get_image_feature(img_path, files_list,\n model_path, 0, gpu_id)\nstop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))\nprint('Feature Shape: ({} , {}) .'.format(img_feats.shape[0],",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "files_list",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "files_list = files\n# img_feats\n# for i in range(rank_size):\nimg_feats, faceness_scores = get_image_feature(img_path, files_list,\n model_path, 0, gpu_id)\nstop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))\nprint('Feature Shape: ({} , {}) .'.format(img_feats.shape[0],\n img_feats.shape[1]))\n# # Step3: Get Template Features",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "stop",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "stop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))\nprint('Feature Shape: ({} , {}) .'.format(img_feats.shape[0],\n img_feats.shape[1]))\n# # Step3: Get Template Features\n# In[ ]:\n# =============================================================\n# compute template features from image features.\n# =============================================================\nstart = timeit.default_timer()",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "start",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "start = timeit.default_timer()\n# ==========================================================\n# Norm feature before aggregation into template feature?\n# Feature norm from embedding network and faceness score are able to decrease weights for noise samples (not face).\n# ==========================================================\n# 1. FaceScore （Feature Norm）\n# 2. FaceScore （Detector）\nif use_flip_test:\n # concat --- F1\n # img_input_feats = img_feats",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "stop",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "stop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))\n# # Step 4: Get Template Similarity Scores\n# In[ ]:\n# =============================================================\n# compute verification scores between template pairs.\n# =============================================================\nstart = timeit.default_timer()\nscore = verification(template_norm_feats, unique_templates, p1, p2)\nstop = timeit.default_timer()",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "start",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "start = timeit.default_timer()\nscore = verification(template_norm_feats, unique_templates, p1, p2)\nstop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))\n# In[ ]:\nsave_path = os.path.join(result_dir, args.job)\n# save_path = result_dir + '/%s_result' % target\nif not os.path.exists(save_path):\n os.makedirs(save_path)\nscore_save_file = os.path.join(save_path, \"%s.npy\" % target.lower())",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "score",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "score = verification(template_norm_feats, unique_templates, p1, p2)\nstop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))\n# In[ ]:\nsave_path = os.path.join(result_dir, args.job)\n# save_path = result_dir + '/%s_result' % target\nif not os.path.exists(save_path):\n os.makedirs(save_path)\nscore_save_file = os.path.join(save_path, \"%s.npy\" % target.lower())\nnp.save(score_save_file, score)",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "stop",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "stop = timeit.default_timer()\nprint('Time: %.2f s. ' % (stop - start))\n# In[ ]:\nsave_path = os.path.join(result_dir, args.job)\n# save_path = result_dir + '/%s_result' % target\nif not os.path.exists(save_path):\n os.makedirs(save_path)\nscore_save_file = os.path.join(save_path, \"%s.npy\" % target.lower())\nnp.save(score_save_file, score)\n# # Step 5: Get ROC Curves and TPR@FPR Table",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "save_path",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "save_path = os.path.join(result_dir, args.job)\n# save_path = result_dir + '/%s_result' % target\nif not os.path.exists(save_path):\n os.makedirs(save_path)\nscore_save_file = os.path.join(save_path, \"%s.npy\" % target.lower())\nnp.save(score_save_file, score)\n# # Step 5: Get ROC Curves and TPR@FPR Table\n# In[ ]:\nfiles = [score_save_file]\nmethods = []",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "score_save_file",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "score_save_file = os.path.join(save_path, \"%s.npy\" % target.lower())\nnp.save(score_save_file, score)\n# # Step 5: Get ROC Curves and TPR@FPR Table\n# In[ ]:\nfiles = [score_save_file]\nmethods = []\nscores = []\nfor file in files:\n methods.append(Path(file).stem)\n scores.append(np.load(file))",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "files",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "files = [score_save_file]\nmethods = []\nscores = []\nfor file in files:\n methods.append(Path(file).stem)\n scores.append(np.load(file))\nmethods = np.array(methods)\nscores = dict(zip(methods, scores))\ncolours = dict(\n zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "methods",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "methods = []\nscores = []\nfor file in files:\n methods.append(Path(file).stem)\n scores.append(np.load(file))\nmethods = np.array(methods)\nscores = dict(zip(methods, scores))\ncolours = dict(\n zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\nx_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "scores",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "scores = []\nfor file in files:\n methods.append(Path(file).stem)\n scores.append(np.load(file))\nmethods = np.array(methods)\nscores = dict(zip(methods, scores))\ncolours = dict(\n zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\nx_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]\ntpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "methods",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "methods = np.array(methods)\nscores = dict(zip(methods, scores))\ncolours = dict(\n zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\nx_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]\ntpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])\nfig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "scores",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "scores = dict(zip(methods, scores))\ncolours = dict(\n zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\nx_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]\ntpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])\nfig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)\n fpr = np.flipud(fpr)",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "colours",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "colours = dict(\n zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))\nx_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]\ntpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])\nfig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)\n fpr = np.flipud(fpr)\n tpr = np.flipud(tpr) # select largest tpr at same fpr",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "x_labels",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]\ntpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])\nfig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)\n fpr = np.flipud(fpr)\n tpr = np.flipud(tpr) # select largest tpr at same fpr\n plt.plot(fpr,\n tpr,",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "tpr_fpr_table",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])\nfig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)\n fpr = np.flipud(fpr)\n tpr = np.flipud(tpr) # select largest tpr at same fpr\n plt.plot(fpr,\n tpr,\n color=colours[method],",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "fig",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.eval_ijbc",
+ "description": "modules.arcface_torch.eval_ijbc",
+ "peekOfCode": "fig = plt.figure()\nfor method in methods:\n fpr, tpr, _ = roc_curve(label, scores[method])\n roc_auc = auc(fpr, tpr)\n fpr = np.flipud(fpr)\n tpr = np.flipud(tpr) # select largest tpr at same fpr\n plt.plot(fpr,\n tpr,\n color=colours[method],\n lw=1,",
+ "detail": "modules.arcface_torch.eval_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "inference",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.inference",
+ "description": "modules.arcface_torch.inference",
+ "peekOfCode": "def inference(weight, name, img):\n if img is None:\n img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.uint8)\n else:\n img = cv2.imread(img)\n img = cv2.resize(img, (112, 112))\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img = np.transpose(img, (2, 0, 1))\n img = torch.from_numpy(img).unsqueeze(0).float()\n img.div_(255).sub_(0.5).div_(0.5)",
+ "detail": "modules.arcface_torch.inference",
+ "documentation": {}
+ },
+ {
+ "label": "CombinedMarginLoss",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.losses",
+ "description": "modules.arcface_torch.losses",
+ "peekOfCode": "class CombinedMarginLoss(torch.nn.Module):\n def __init__(self, \n s, \n m1,\n m2,\n m3,\n interclass_filtering_threshold=0):\n super().__init__()\n self.s = s\n self.m1 = m1",
+ "detail": "modules.arcface_torch.losses",
+ "documentation": {}
+ },
+ {
+ "label": "ArcFace",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.losses",
+ "description": "modules.arcface_torch.losses",
+ "peekOfCode": "class ArcFace(torch.nn.Module):\n \"\"\" ArcFace (https://arxiv.org/pdf/1801.07698v1.pdf):\n \"\"\"\n def __init__(self, s=64.0, margin=0.5):\n super(ArcFace, self).__init__()\n self.scale = s\n self.cos_m = math.cos(margin)\n self.sin_m = math.sin(margin)\n self.theta = math.cos(math.pi - margin)\n self.sinmm = math.sin(math.pi - margin) * margin",
+ "detail": "modules.arcface_torch.losses",
+ "documentation": {}
+ },
+ {
+ "label": "CosFace",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.losses",
+ "description": "modules.arcface_torch.losses",
+ "peekOfCode": "class CosFace(torch.nn.Module):\n def __init__(self, s=64.0, m=0.40):\n super(CosFace, self).__init__()\n self.s = s\n self.m = m\n def forward(self, logits: torch.Tensor, labels: torch.Tensor):\n index = torch.where(labels != -1)[0]\n target_logit = logits[index, labels[index].view(-1)]\n final_target_logit = target_logit - self.m\n logits[index, labels[index].view(-1)] = final_target_logit",
+ "detail": "modules.arcface_torch.losses",
+ "documentation": {}
+ },
+ {
+ "label": "PolyScheduler",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.lr_scheduler",
+ "description": "modules.arcface_torch.lr_scheduler",
+ "peekOfCode": "class PolyScheduler(_LRScheduler):\n def __init__(self, optimizer, base_lr, max_steps, warmup_steps, last_epoch=-1):\n self.base_lr = base_lr\n self.warmup_lr_init = 0.0001\n self.max_steps: int = max_steps\n self.warmup_steps: int = warmup_steps\n self.power = 2\n super(PolyScheduler, self).__init__(optimizer, -1, False)\n self.last_epoch = last_epoch\n def get_warmup_lr(self):",
+ "detail": "modules.arcface_torch.lr_scheduler",
+ "documentation": {}
+ },
+ {
+ "label": "ArcFaceORT",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.onnx_helper",
+ "description": "modules.arcface_torch.onnx_helper",
+ "peekOfCode": "class ArcFaceORT:\n def __init__(self, model_path, cpu=False):\n self.model_path = model_path\n # providers = None will use available provider, for onnxruntime-gpu it will be \"CUDAExecutionProvider\"\n self.providers = ['CPUExecutionProvider'] if cpu else None\n #input_size is (w,h), return error message, return None if success\n def check(self, track='cfat', test_img = None):\n #default is cfat\n max_model_size_mb=1024\n max_feat_dim=512",
+ "detail": "modules.arcface_torch.onnx_helper",
+ "documentation": {}
+ },
+ {
+ "label": "AlignedDataSet",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.onnx_ijbc",
+ "description": "modules.arcface_torch.onnx_ijbc",
+ "peekOfCode": "class AlignedDataSet(mx.gluon.data.Dataset):\n def __init__(self, root, lines, align=True):\n self.lines = lines\n self.root = root\n self.align = align\n def __len__(self):\n return len(self.lines)\n def __getitem__(self, idx):\n each_line = self.lines[idx]\n name_lmk_score = each_line.strip().split(' ')",
+ "detail": "modules.arcface_torch.onnx_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "extract",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.onnx_ijbc",
+ "description": "modules.arcface_torch.onnx_ijbc",
+ "peekOfCode": "def extract(model_root, dataset):\n model = ArcFaceORT(model_path=model_root)\n model.check()\n feat_mat = np.zeros(shape=(len(dataset), 2 * model.feat_dim))\n def collate_fn(data):\n return torch.cat(data, dim=0)\n data_loader = DataLoader(\n dataset, batch_size=128, drop_last=False, num_workers=4, collate_fn=collate_fn, )\n num_iter = 0\n for batch in data_loader:",
+ "detail": "modules.arcface_torch.onnx_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "read_template_media_list",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.onnx_ijbc",
+ "description": "modules.arcface_torch.onnx_ijbc",
+ "peekOfCode": "def read_template_media_list(path):\n ijb_meta = pd.read_csv(path, sep=' ', header=None).values\n templates = ijb_meta[:, 1].astype(np.int)\n medias = ijb_meta[:, 2].astype(np.int)\n return templates, medias\ndef read_template_pair_list(path):\n pairs = pd.read_csv(path, sep=' ', header=None).values\n t1 = pairs[:, 0].astype(np.int)\n t2 = pairs[:, 1].astype(np.int)\n label = pairs[:, 2].astype(np.int)",
+ "detail": "modules.arcface_torch.onnx_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "read_template_pair_list",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.onnx_ijbc",
+ "description": "modules.arcface_torch.onnx_ijbc",
+ "peekOfCode": "def read_template_pair_list(path):\n pairs = pd.read_csv(path, sep=' ', header=None).values\n t1 = pairs[:, 0].astype(np.int)\n t2 = pairs[:, 1].astype(np.int)\n label = pairs[:, 2].astype(np.int)\n return t1, t2, label\ndef read_image_feature(path):\n with open(path, 'rb') as fid:\n img_feats = pickle.load(fid)\n return img_feats",
+ "detail": "modules.arcface_torch.onnx_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "read_image_feature",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.onnx_ijbc",
+ "description": "modules.arcface_torch.onnx_ijbc",
+ "peekOfCode": "def read_image_feature(path):\n with open(path, 'rb') as fid:\n img_feats = pickle.load(fid)\n return img_feats\ndef image2template_feature(img_feats=None,\n templates=None,\n medias=None):\n unique_templates = np.unique(templates)\n template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))\n for count_template, uqt in enumerate(unique_templates):",
+ "detail": "modules.arcface_torch.onnx_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "image2template_feature",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.onnx_ijbc",
+ "description": "modules.arcface_torch.onnx_ijbc",
+ "peekOfCode": "def image2template_feature(img_feats=None,\n templates=None,\n medias=None):\n unique_templates = np.unique(templates)\n template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))\n for count_template, uqt in enumerate(unique_templates):\n (ind_t,) = np.where(templates == uqt)\n face_norm_feats = img_feats[ind_t]\n face_medias = medias[ind_t]\n unique_medias, unique_media_counts = np.unique(face_medias, return_counts=True)",
+ "detail": "modules.arcface_torch.onnx_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "verification",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.onnx_ijbc",
+ "description": "modules.arcface_torch.onnx_ijbc",
+ "peekOfCode": "def verification(template_norm_feats=None,\n unique_templates=None,\n p1=None,\n p2=None):\n template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)\n for count_template, uqt in enumerate(unique_templates):\n template2id[uqt] = count_template\n score = np.zeros((len(p1),))\n total_pairs = np.array(range(len(p1)))\n batchsize = 100000",
+ "detail": "modules.arcface_torch.onnx_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "verification2",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.onnx_ijbc",
+ "description": "modules.arcface_torch.onnx_ijbc",
+ "peekOfCode": "def verification2(template_norm_feats=None,\n unique_templates=None,\n p1=None,\n p2=None):\n template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)\n for count_template, uqt in enumerate(unique_templates):\n template2id[uqt] = count_template\n score = np.zeros((len(p1),)) # save cosine distance between pairs\n total_pairs = np.array(range(len(p1)))\n batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation",
+ "detail": "modules.arcface_torch.onnx_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.onnx_ijbc",
+ "description": "modules.arcface_torch.onnx_ijbc",
+ "peekOfCode": "def main(args):\n use_norm_score = True # if Ture, TestMode(N1)\n use_detector_score = True # if Ture, TestMode(D1)\n use_flip_test = True # if Ture, TestMode(F1)\n assert args.target == 'IJBC' or args.target == 'IJBB'\n start = timeit.default_timer()\n templates, medias = read_template_media_list(\n os.path.join('%s/meta' % args.image_path, '%s_face_tid_mid.txt' % args.target.lower()))\n stop = timeit.default_timer()\n print('Time: %.2f s. ' % (stop - start))",
+ "detail": "modules.arcface_torch.onnx_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "SRC",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.onnx_ijbc",
+ "description": "modules.arcface_torch.onnx_ijbc",
+ "peekOfCode": "SRC = np.array(\n [\n [30.2946, 51.6963],\n [65.5318, 51.5014],\n [48.0252, 71.7366],\n [33.5493, 92.3655],\n [62.7299, 92.2041]]\n , dtype=np.float32)\nSRC[:, 0] += 8.0\n@torch.no_grad()",
+ "detail": "modules.arcface_torch.onnx_ijbc",
+ "documentation": {}
+ },
+ {
+ "label": "PartialFC",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.partial_fc",
+ "description": "modules.arcface_torch.partial_fc",
+ "peekOfCode": "class PartialFC(torch.nn.Module):\n \"\"\"\n https://arxiv.org/abs/2203.15565\n A distributed sparsely updating variant of the FC layer, named Partial FC (PFC).\n When sample rate less than 1, in each iteration, positive class centers and a random subset of\n negative class centers are selected to compute the margin-based softmax loss, all class\n centers are still maintained throughout the whole training process, but only a subset is\n selected and updated in each iteration.\n .. note::\n When sample rate equal to 1, Partial FC is equal to model parallelism(default sample rate is 1).",
+ "detail": "modules.arcface_torch.partial_fc",
+ "documentation": {}
+ },
+ {
+ "label": "PartialFCAdamW",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.partial_fc",
+ "description": "modules.arcface_torch.partial_fc",
+ "peekOfCode": "class PartialFCAdamW(torch.nn.Module):\n def __init__(self,\n margin_loss: Callable,\n embedding_size: int,\n num_classes: int,\n sample_rate: float = 1.0,\n fp16: bool = False,):\n \"\"\"\n Paramenters:\n -----------",
+ "detail": "modules.arcface_torch.partial_fc",
+ "documentation": {}
+ },
+ {
+ "label": "DistCrossEntropyFunc",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.partial_fc",
+ "description": "modules.arcface_torch.partial_fc",
+ "peekOfCode": "class DistCrossEntropyFunc(torch.autograd.Function):\n \"\"\"\n CrossEntropy loss is calculated in parallel, allreduce denominator into single gpu and calculate softmax.\n Implemented of ArcFace (https://arxiv.org/pdf/1801.07698v1.pdf):\n \"\"\"\n @staticmethod\n def forward(ctx, logits: torch.Tensor, label: torch.Tensor):\n \"\"\" \"\"\"\n batch_size = logits.size(0)\n # for numerical stability",
+ "detail": "modules.arcface_torch.partial_fc",
+ "documentation": {}
+ },
+ {
+ "label": "DistCrossEntropy",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.partial_fc",
+ "description": "modules.arcface_torch.partial_fc",
+ "peekOfCode": "class DistCrossEntropy(torch.nn.Module):\n def __init__(self):\n super(DistCrossEntropy, self).__init__()\n def forward(self, logit_part, label_part):\n return DistCrossEntropyFunc.apply(logit_part, label_part)\nclass AllGatherFunc(torch.autograd.Function):\n \"\"\"AllGather op with gradient backward\"\"\"\n @staticmethod\n def forward(ctx, tensor, *gather_list):\n gather_list = list(gather_list)",
+ "detail": "modules.arcface_torch.partial_fc",
+ "documentation": {}
+ },
+ {
+ "label": "AllGatherFunc",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.partial_fc",
+ "description": "modules.arcface_torch.partial_fc",
+ "peekOfCode": "class AllGatherFunc(torch.autograd.Function):\n \"\"\"AllGather op with gradient backward\"\"\"\n @staticmethod\n def forward(ctx, tensor, *gather_list):\n gather_list = list(gather_list)\n distributed.all_gather(gather_list, tensor)\n return tuple(gather_list)\n @staticmethod\n def backward(ctx, *grads):\n grad_list = list(grads)",
+ "detail": "modules.arcface_torch.partial_fc",
+ "documentation": {}
+ },
+ {
+ "label": "AllGather",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.partial_fc",
+ "description": "modules.arcface_torch.partial_fc",
+ "peekOfCode": "AllGather = AllGatherFunc.apply",
+ "detail": "modules.arcface_torch.partial_fc",
+ "documentation": {}
+ },
+ {
+ "label": "PartialFC_V2",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.partial_fc_v2",
+ "description": "modules.arcface_torch.partial_fc_v2",
+ "peekOfCode": "class PartialFC_V2(torch.nn.Module):\n \"\"\"\n https://arxiv.org/abs/2203.15565\n A distributed sparsely updating variant of the FC layer, named Partial FC (PFC).\n When sample rate less than 1, in each iteration, positive class centers and a random subset of\n negative class centers are selected to compute the margin-based softmax loss, all class\n centers are still maintained throughout the whole training process, but only a subset is\n selected and updated in each iteration.\n .. note::\n When sample rate equal to 1, Partial FC is equal to model parallelism(default sample rate is 1).",
+ "detail": "modules.arcface_torch.partial_fc_v2",
+ "documentation": {}
+ },
+ {
+ "label": "DistCrossEntropyFunc",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.partial_fc_v2",
+ "description": "modules.arcface_torch.partial_fc_v2",
+ "peekOfCode": "class DistCrossEntropyFunc(torch.autograd.Function):\n \"\"\"\n CrossEntropy loss is calculated in parallel, allreduce denominator into single gpu and calculate softmax.\n Implemented of ArcFace (https://arxiv.org/pdf/1801.07698v1.pdf):\n \"\"\"\n @staticmethod\n def forward(ctx, logits: torch.Tensor, label: torch.Tensor):\n \"\"\" \"\"\"\n batch_size = logits.size(0)\n # for numerical stability",
+ "detail": "modules.arcface_torch.partial_fc_v2",
+ "documentation": {}
+ },
+ {
+ "label": "DistCrossEntropy",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.partial_fc_v2",
+ "description": "modules.arcface_torch.partial_fc_v2",
+ "peekOfCode": "class DistCrossEntropy(torch.nn.Module):\n def __init__(self):\n super(DistCrossEntropy, self).__init__()\n def forward(self, logit_part, label_part):\n return DistCrossEntropyFunc.apply(logit_part, label_part)\nclass AllGatherFunc(torch.autograd.Function):\n \"\"\"AllGather op with gradient backward\"\"\"\n @staticmethod\n def forward(ctx, tensor, *gather_list):\n gather_list = list(gather_list)",
+ "detail": "modules.arcface_torch.partial_fc_v2",
+ "documentation": {}
+ },
+ {
+ "label": "AllGatherFunc",
+ "kind": 6,
+ "importPath": "modules.arcface_torch.partial_fc_v2",
+ "description": "modules.arcface_torch.partial_fc_v2",
+ "peekOfCode": "class AllGatherFunc(torch.autograd.Function):\n \"\"\"AllGather op with gradient backward\"\"\"\n @staticmethod\n def forward(ctx, tensor, *gather_list):\n gather_list = list(gather_list)\n distributed.all_gather(gather_list, tensor)\n return tuple(gather_list)\n @staticmethod\n def backward(ctx, *grads):\n grad_list = list(grads)",
+ "detail": "modules.arcface_torch.partial_fc_v2",
+ "documentation": {}
+ },
+ {
+ "label": "AllGather",
+ "kind": 5,
+ "importPath": "modules.arcface_torch.partial_fc_v2",
+ "description": "modules.arcface_torch.partial_fc_v2",
+ "peekOfCode": "AllGather = AllGatherFunc.apply",
+ "detail": "modules.arcface_torch.partial_fc_v2",
+ "documentation": {}
+ },
+ {
+ "label": "convert_onnx",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.torch2onnx",
+ "description": "modules.arcface_torch.torch2onnx",
+ "peekOfCode": "def convert_onnx(net, path_module, output, opset=11, simplify=False):\n assert isinstance(net, torch.nn.Module)\n img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32)\n img = img.astype(np.float)\n img = (img / 255. - 0.5) / 0.5 # torch style norm\n img = img.transpose((2, 0, 1))\n img = torch.from_numpy(img).unsqueeze(0).float()\n weight = torch.load(path_module)\n net.load_state_dict(weight, strict=True)\n net.eval()",
+ "detail": "modules.arcface_torch.torch2onnx",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.train",
+ "description": "modules.arcface_torch.train",
+ "peekOfCode": "def main(args):\n # get config\n cfg = get_config(args.config)\n # global control random seed\n setup_seed(seed=cfg.seed, cuda_deterministic=False)\n torch.cuda.set_device(args.local_rank)\n os.makedirs(cfg.output, exist_ok=True)\n init_logging(rank, cfg.output)\n summary_writer = (\n SummaryWriter(log_dir=os.path.join(cfg.output, \"tensorboard\"))",
+ "detail": "modules.arcface_torch.train",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.arcface_torch.train_v2",
+ "description": "modules.arcface_torch.train_v2",
+ "peekOfCode": "def main(args):\n # get config\n cfg = get_config(args.config)\n # global control random seed\n setup_seed(seed=cfg.seed, cuda_deterministic=False)\n torch.cuda.set_device(args.local_rank)\n os.makedirs(cfg.output, exist_ok=True)\n init_logging(rank, cfg.output)\n summary_writer = (\n SummaryWriter(log_dir=os.path.join(cfg.output, \"tensorboard\"))",
+ "detail": "modules.arcface_torch.train_v2",
+ "documentation": {}
+ },
+ {
+ "label": "AFLW2000",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.aflw2000",
+ "description": "modules.DECA.decalib.datasets.aflw2000",
+ "peekOfCode": "class AFLW2000(Dataset):\n def __init__(self, testpath='/ps/scratch/yfeng/Data/AFLW2000/GT', crop_size=224):\n '''\n data class for loading AFLW2000 dataset\n make sure each image has corresponding mat file, which provides cropping infromation\n '''\n if os.path.isdir(testpath): \n self.imagepath_list = glob(testpath + '/*.jpg') + glob(testpath + '/*.png')\n elif isinstance(testpath, list):\n self.imagepath_list = testpath",
+ "detail": "modules.DECA.decalib.datasets.aflw2000",
+ "documentation": {}
+ },
+ {
+ "label": "build_train",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.datasets.build_datasets",
+ "description": "modules.DECA.decalib.datasets.build_datasets",
+ "peekOfCode": "def build_train(config, is_train=True):\n data_list = []\n if 'vox2' in config.training_data:\n data_list.append(VoxelDataset(dataname='vox2', K=config.K, image_size=config.image_size, scale=[config.scale_min, config.scale_max], trans_scale=config.trans_scale, isSingle=config.isSingle))\n if 'vggface2' in config.training_data:\n data_list.append(VGGFace2Dataset(K=config.K, image_size=config.image_size, scale=[config.scale_min, config.scale_max], trans_scale=config.trans_scale, isSingle=config.isSingle))\n if 'vggface2hq' in config.training_data:\n data_list.append(VGGFace2HQDataset(K=config.K, image_size=config.image_size, scale=[config.scale_min, config.scale_max], trans_scale=config.trans_scale, isSingle=config.isSingle))\n if 'ethnicity' in config.training_data:\n data_list.append(EthnicityDataset(K=config.K, image_size=config.image_size, scale=[config.scale_min, config.scale_max], trans_scale=config.trans_scale, isSingle=config.isSingle))",
+ "detail": "modules.DECA.decalib.datasets.build_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "build_val",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.datasets.build_datasets",
+ "description": "modules.DECA.decalib.datasets.build_datasets",
+ "peekOfCode": "def build_val(config, is_train=True):\n data_list = []\n if 'vggface2' in config.eval_data:\n data_list.append(VGGFace2Dataset(isEval=True, K=config.K, image_size=config.image_size, scale=[config.scale_min, config.scale_max], trans_scale=config.trans_scale, isSingle=config.isSingle))\n if 'now' in config.eval_data:\n data_list.append(NoWDataset())\n if 'aflw2000' in config.eval_data:\n data_list.append(AFLW2000())\n dataset = ConcatDataset(data_list)\n return dataset",
+ "detail": "modules.DECA.decalib.datasets.build_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "TestData",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.datasets",
+ "description": "modules.DECA.decalib.datasets.datasets",
+ "peekOfCode": "class TestData(Dataset):\n def __init__(self, testpath=None, iscrop=True, crop_size=224, scale=1.25, face_detector='fan'):\n '''\n testpath: folder, imagepath_list, image path, video path\n '''\n def glob_exts_in_path(path, img_ext=['png', 'jpg']):\n from functools import reduce\n return reduce(\n lambda before, ext: before+glob(\n os.path.join(path, f'*.{ext}')",
+ "detail": "modules.DECA.decalib.datasets.datasets",
+ "documentation": {}
+ },
+ {
+ "label": "video2sequence",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.datasets.datasets",
+ "description": "modules.DECA.decalib.datasets.datasets",
+ "peekOfCode": "def video2sequence(video_path):\n videofolder = os.path.splitext(video_path)[0]\n os.makedirs(videofolder, exist_ok=True)\n video_name = os.path.splitext(os.path.split(video_path)[-1])[0]\n vidcap = cv2.VideoCapture(video_path)\n success,image = vidcap.read()\n count = 0\n imagepath_list = []\n while success:\n imagepath = os.path.join(videofolder, f'{video_name}_frame{count:04d}.jpg')",
+ "detail": "modules.DECA.decalib.datasets.datasets",
+ "documentation": {}
+ },
+ {
+ "label": "FAN",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.detectors",
+ "description": "modules.DECA.decalib.datasets.detectors",
+ "peekOfCode": "class FAN(object):\n def __init__(self):\n import face_alignment\n self.model = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False)\n def run(self, image,**kwargs):\n '''\n image: 0-255, uint8, rgb, [h, w, 3]\n return: detected box list\n '''\n h,w,_=image.shape",
+ "detail": "modules.DECA.decalib.datasets.detectors",
+ "documentation": {}
+ },
+ {
+ "label": "MTCNN",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.detectors",
+ "description": "modules.DECA.decalib.datasets.detectors",
+ "peekOfCode": "class MTCNN(object):\n def __init__(self, device = 'cpu'):\n '''\n https://github.com/timesler/facenet-pytorch/blob/master/examples/infer.ipynb\n '''\n from facenet_pytorch import MTCNN as mtcnn\n self.device = device\n self.model = mtcnn(keep_all=True)\n def run(self, input):\n '''",
+ "detail": "modules.DECA.decalib.datasets.detectors",
+ "documentation": {}
+ },
+ {
+ "label": "EthnicityDataset",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.ethnicity",
+ "description": "modules.DECA.decalib.datasets.ethnicity",
+ "peekOfCode": "class EthnicityDataset(Dataset):\n def __init__(self, K, image_size, scale, trans_scale = 0, isTemporal=False, isEval=False, isSingle=False):\n '''\n K must be less than 6\n '''\n self.K = K\n self.image_size = image_size\n self.imagefolder = '/ps/scratch/face2d3d/train'\n self.kptfolder = '/ps/scratch/face2d3d/train_annotated_torch7/'\n self.segfolder = '/ps/scratch/face2d3d/texture_in_the_wild_code/VGGFace2_seg/test_crop_size_400_batch/'",
+ "detail": "modules.DECA.decalib.datasets.ethnicity",
+ "documentation": {}
+ },
+ {
+ "label": "NoWDataset",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.now",
+ "description": "modules.DECA.decalib.datasets.now",
+ "peekOfCode": "class NoWDataset(Dataset):\n def __init__(self, ring_elements=6, crop_size=224, scale=1.6):\n folder = '/ps/scratch/yfeng/other-github/now_evaluation/data/NoW_Dataset'\n self.data_path = os.path.join(folder, 'imagepathsvalidation.txt')\n with open(self.data_path) as f:\n self.data_lines = f.readlines()\n self.imagefolder = os.path.join(folder, 'final_release_version', 'iphone_pictures')\n self.bbxfolder = os.path.join(folder, 'final_release_version', 'detected_face')\n # self.data_path = '/ps/scratch/face2d3d/ringnetpp/eccv/test_data/evaluation/NoW_Dataset/final_release_version/test_image_paths_ring_6_elements.npy'\n # self.imagepath = '/ps/scratch/face2d3d/ringnetpp/eccv/test_data/evaluation/NoW_Dataset/final_release_version/iphone_pictures/'",
+ "detail": "modules.DECA.decalib.datasets.now",
+ "documentation": {}
+ },
+ {
+ "label": "VoxelDataset",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.train_datasets",
+ "description": "modules.DECA.decalib.datasets.train_datasets",
+ "peekOfCode": "class VoxelDataset(Dataset):\n def __init__(self, K, image_size, scale, trans_scale = 0, dataname='vox2', n_train=100000, isTemporal=False, isEval=False, isSingle=False):\n self.K = K\n self.image_size = image_size\n if dataname == 'vox1':\n self.kpt_suffix = '.txt'\n self.imagefolder = '/ps/project/face2d3d/VoxCeleb/vox1/dev/images_cropped'\n self.kptfolder = '/ps/scratch/yfeng/Data/VoxCeleb/vox1/landmark_2d'\n self.face_dict = {}\n for person_id in sorted(os.listdir(self.kptfolder)):",
+ "detail": "modules.DECA.decalib.datasets.train_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "COCODataset",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.train_datasets",
+ "description": "modules.DECA.decalib.datasets.train_datasets",
+ "peekOfCode": "class COCODataset(Dataset):\n def __init__(self, image_size, scale, trans_scale = 0, isEval=False):\n '''\n # 53877 faces\n K must be less than 6\n '''\n self.image_size = image_size\n self.imagefolder = '/ps/scratch/yfeng/Data/COCO/raw/train2017'\n self.kptfolder = '/ps/scratch/yfeng/Data/COCO/face/train2017_kpt'\n self.kptpath_list = os.listdir(self.kptfolder)",
+ "detail": "modules.DECA.decalib.datasets.train_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "CelebAHQDataset",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.train_datasets",
+ "description": "modules.DECA.decalib.datasets.train_datasets",
+ "peekOfCode": "class CelebAHQDataset(Dataset):\n def __init__(self, image_size, scale, trans_scale = 0, isEval=False):\n '''\n # 53877 faces\n K must be less than 6\n '''\n self.image_size = image_size\n self.imagefolder = '/ps/project/face2d3d/faceHQ_100K/celebA-HQ/celebahq_resized_256'\n self.kptfolder = '/ps/project/face2d3d/faceHQ_100K/celebA-HQ/celebahq_resized_256_torch'\n self.kptpath_list = os.listdir(self.kptfolder)",
+ "detail": "modules.DECA.decalib.datasets.train_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "TestData",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.train_datasets",
+ "description": "modules.DECA.decalib.datasets.train_datasets",
+ "peekOfCode": "class TestData(Dataset):\n def __init__(self, testpath, iscrop=True, crop_size=224, scale=1.25, face_detector='fan', face_detector_model=None):\n '''\n testpath: folder, imagepath_list, image path, video path\n '''\n if isinstance(testpath, list):\n self.imagepath_list = testpath\n elif os.path.isdir(testpath): \n self.imagepath_list = glob(testpath + '/*.jpg') + glob(testpath + '/*.png') + glob(testpath + '/*.bmp')\n elif os.path.isfile(testpath) and (testpath[-3:] in ['jpg', 'png', 'bmp']):",
+ "detail": "modules.DECA.decalib.datasets.train_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "EvalData",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.train_datasets",
+ "description": "modules.DECA.decalib.datasets.train_datasets",
+ "peekOfCode": "class EvalData(Dataset):\n def __init__(self, testpath, kptfolder, iscrop=True, crop_size=224, scale=1.25, face_detector='fan', face_detector_model=None):\n '''\n testpath: folder, imagepath_list, image path, video path\n '''\n if isinstance(testpath, list):\n self.imagepath_list = testpath\n elif os.path.isdir(testpath): \n self.imagepath_list = glob(testpath + '/*.jpg') + glob(testpath + '/*.png')\n elif os.path.isfile(testpath) and (testpath[-3:] in ['jpg', 'png']):",
+ "detail": "modules.DECA.decalib.datasets.train_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "build_dataloader",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.datasets.train_datasets",
+ "description": "modules.DECA.decalib.datasets.train_datasets",
+ "peekOfCode": "def build_dataloader(config, is_train=True):\n data_list = []\n if 'vox1' in config.training_data:\n data_list.append(VoxelDataset(K=config.K, image_size=config.image_size, scale=[config.scale_min, config.scale_max], n_train=config.n_train, isSingle=config.isSingle))\n if 'vox2' in config.training_data:\n data_list.append(VoxelDataset(dataname='vox2', K=config.K, image_size=config.image_size, scale=[config.scale_min, config.scale_max], n_train=config.n_train, isSingle=config.isSingle))\n if 'vggface2' in config.training_data:\n data_list.append(VGGFace2Dataset(K=config.K, image_size=config.image_size, scale=[config.scale_min, config.scale_max], trans_scale=config.trans_scale, isSingle=config.isSingle))\n if 'vggface2hq' in config.training_data:\n data_list.append(VGGFace2HQDataset(K=config.K, image_size=config.image_size, scale=[config.scale_min, config.scale_max], trans_scale=config.trans_scale, isSingle=config.isSingle))",
+ "detail": "modules.DECA.decalib.datasets.train_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "video2sequence",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.datasets.train_datasets",
+ "description": "modules.DECA.decalib.datasets.train_datasets",
+ "peekOfCode": "def video2sequence(video_path):\n videofolder = video_path.split('.')[0] \n os.makedirs(videofolder, exist_ok=True)\n video_name = video_path.split('/')[-1].split('.')[0]\n # import ipdb; ipdb.set_trace()\n vidcap = cv2.VideoCapture(video_path)\n success,image = vidcap.read()\n count = 0\n imagepath_list = []\n while success:",
+ "detail": "modules.DECA.decalib.datasets.train_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "VGGFace2Dataset",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.vggface",
+ "description": "modules.DECA.decalib.datasets.vggface",
+ "peekOfCode": "class VGGFace2Dataset(Dataset):\n def __init__(self, K, image_size, scale, trans_scale = 0, isTemporal=False, isEval=False, isSingle=False):\n '''\n K must be less than 6\n '''\n self.K = K\n self.image_size = image_size\n self.imagefolder = '/ps/scratch/face2d3d/train'\n self.kptfolder = '/ps/scratch/face2d3d/train_annotated_torch7'\n self.segfolder = '/ps/scratch/face2d3d/texture_in_the_wild_code/VGGFace2_seg/test_crop_size_400_batch'",
+ "detail": "modules.DECA.decalib.datasets.vggface",
+ "documentation": {}
+ },
+ {
+ "label": "VGGFace2HQDataset",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.vggface",
+ "description": "modules.DECA.decalib.datasets.vggface",
+ "peekOfCode": "class VGGFace2HQDataset(Dataset):\n def __init__(self, K, image_size, scale, trans_scale = 0, isTemporal=False, isEval=False, isSingle=False):\n '''\n K must be less than 6\n '''\n self.K = K\n self.image_size = image_size\n self.imagefolder = '/ps/scratch/face2d3d/train'\n self.kptfolder = '/ps/scratch/face2d3d/train_annotated_torch7'\n self.segfolder = '/ps/scratch/face2d3d/texture_in_the_wild_code/VGGFace2_seg/test_crop_size_400_batch'",
+ "detail": "modules.DECA.decalib.datasets.vggface",
+ "documentation": {}
+ },
+ {
+ "label": "VoxelDataset",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.datasets.vox",
+ "description": "modules.DECA.decalib.datasets.vox",
+ "peekOfCode": "class VoxelDataset(Dataset):\n def __init__(self, K, image_size, scale, trans_scale = 0, dataname='vox2', n_train=100000, isTemporal=False, isEval=False, isSingle=False):\n self.K = K\n self.image_size = image_size\n if dataname == 'vox1':\n self.kpt_suffix = '.txt'\n self.imagefolder = '/ps/project/face2d3d/VoxCeleb/vox1/dev/images_cropped'\n self.kptfolder = '/ps/scratch/yfeng/Data/VoxCeleb/vox1/landmark_2d'\n self.face_dict = {}\n for person_id in sorted(os.listdir(self.kptfolder)):",
+ "detail": "modules.DECA.decalib.datasets.vox",
+ "documentation": {}
+ },
+ {
+ "label": "Generator",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.decoders",
+ "description": "modules.DECA.decalib.models.decoders",
+ "peekOfCode": "class Generator(nn.Module):\n def __init__(self, latent_dim=100, out_channels=1, out_scale=0.01, sample_mode = 'bilinear'):\n super(Generator, self).__init__()\n self.out_scale = out_scale\n self.init_size = 32 // 4 # Initial size before upsampling\n self.l1 = nn.Sequential(nn.Linear(latent_dim, 128 * self.init_size ** 2))\n self.conv_blocks = nn.Sequential(\n nn.BatchNorm2d(128),\n nn.Upsample(scale_factor=2, mode=sample_mode), #16\n nn.Conv2d(128, 128, 3, stride=1, padding=1),",
+ "detail": "modules.DECA.decalib.models.decoders",
+ "documentation": {}
+ },
+ {
+ "label": "ResnetEncoder",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.encoders",
+ "description": "modules.DECA.decalib.models.encoders",
+ "peekOfCode": "class ResnetEncoder(nn.Module):\n def __init__(self, outsize, last_op=None):\n super(ResnetEncoder, self).__init__()\n feature_size = 2048\n self.encoder = resnet.load_ResNet50Model() #out: 2048\n ### regressor\n self.layers = nn.Sequential(\n nn.Linear(feature_size, 1024),\n nn.ReLU(),\n nn.Linear(1024, outsize)",
+ "detail": "modules.DECA.decalib.models.encoders",
+ "documentation": {}
+ },
+ {
+ "label": "Struct",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.FLAME",
+ "description": "modules.DECA.decalib.models.FLAME",
+ "peekOfCode": "class Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)\nclass FLAME(nn.Module):\n \"\"\"\n borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py\n Given flame parameters this class generates a differentiable FLAME function\n which outputs the a mesh and 2D/3D facial landmarks\n \"\"\"",
+ "detail": "modules.DECA.decalib.models.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "FLAME",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.FLAME",
+ "description": "modules.DECA.decalib.models.FLAME",
+ "peekOfCode": "class FLAME(nn.Module):\n \"\"\"\n borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py\n Given flame parameters this class generates a differentiable FLAME function\n which outputs the a mesh and 2D/3D facial landmarks\n \"\"\"\n def __init__(self, config):\n super(FLAME, self).__init__()\n print(\"creating the FLAME Decoder\")\n with open(config.flame_model_path, 'rb') as f:",
+ "detail": "modules.DECA.decalib.models.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "FLAMETex",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.FLAME",
+ "description": "modules.DECA.decalib.models.FLAME",
+ "peekOfCode": "class FLAMETex(nn.Module):\n \"\"\"\n FLAME texture:\n https://github.com/TimoBolkart/TF_FLAME/blob/ade0ab152300ec5f0e8555d6765411555c5ed43d/sample_texture.py#L64\n FLAME texture converted from BFM:\n https://github.com/TimoBolkart/BFM_to_FLAME\n \"\"\"\n def __init__(self, config):\n super(FLAMETex, self).__init__()\n if config.tex_type == 'BFM':",
+ "detail": "modules.DECA.decalib.models.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "to_tensor",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.FLAME",
+ "description": "modules.DECA.decalib.models.FLAME",
+ "peekOfCode": "def to_tensor(array, dtype=torch.float32):\n if 'torch.tensor' not in str(type(array)):\n return torch.tensor(array, dtype=dtype)\ndef to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():",
+ "detail": "modules.DECA.decalib.models.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "to_np",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.FLAME",
+ "description": "modules.DECA.decalib.models.FLAME",
+ "peekOfCode": "def to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)\nclass FLAME(nn.Module):\n \"\"\"",
+ "detail": "modules.DECA.decalib.models.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "BasicBlock",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.frnet",
+ "description": "modules.DECA.decalib.models.frnet",
+ "peekOfCode": "class BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(BasicBlock, self).__init__()\n self.conv1 = conv3x3(inplanes, planes, stride)\n self.bn1 = nn.BatchNorm2d(planes)\n self.relu = nn.ReLU(inplace=True)\n self.conv2 = conv3x3(planes, planes)\n self.bn2 = nn.BatchNorm2d(planes)\n self.downsample = downsample",
+ "detail": "modules.DECA.decalib.models.frnet",
+ "documentation": {}
+ },
+ {
+ "label": "Bottleneck",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.frnet",
+ "description": "modules.DECA.decalib.models.frnet",
+ "peekOfCode": "class Bottleneck(nn.Module):\n expansion = 4\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(Bottleneck, self).__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(planes)\n self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n self.bn3 = nn.BatchNorm2d(planes * 4)",
+ "detail": "modules.DECA.decalib.models.frnet",
+ "documentation": {}
+ },
+ {
+ "label": "ResNet",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.frnet",
+ "description": "modules.DECA.decalib.models.frnet",
+ "peekOfCode": "class ResNet(nn.Module):\n def __init__(self, block, layers, num_classes=1000, include_top=True):\n self.inplanes = 64\n super(ResNet, self).__init__()\n self.include_top = include_top\n self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n self.bn1 = nn.BatchNorm2d(64)\n self.relu = nn.ReLU(inplace=True)\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=0, ceil_mode=True)\n self.layer1 = self._make_layer(block, 64, layers[0])",
+ "detail": "modules.DECA.decalib.models.frnet",
+ "documentation": {}
+ },
+ {
+ "label": "conv3x3",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.frnet",
+ "description": "modules.DECA.decalib.models.frnet",
+ "peekOfCode": "def conv3x3(in_planes, out_planes, stride=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n padding=1, bias=False)\nclass BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(BasicBlock, self).__init__()\n self.conv1 = conv3x3(inplanes, planes, stride)\n self.bn1 = nn.BatchNorm2d(planes)",
+ "detail": "modules.DECA.decalib.models.frnet",
+ "documentation": {}
+ },
+ {
+ "label": "resnet50",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.frnet",
+ "description": "modules.DECA.decalib.models.frnet",
+ "peekOfCode": "def resnet50(**kwargs):\n \"\"\"Constructs a ResNet-50 model.\n \"\"\"\n model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)\n return model\nimport pickle\ndef load_state_dict(model, fname):\n \"\"\"\n Set parameters converted from Caffe models authors of VGGFace2 provide.\n See https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/.",
+ "detail": "modules.DECA.decalib.models.frnet",
+ "documentation": {}
+ },
+ {
+ "label": "load_state_dict",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.frnet",
+ "description": "modules.DECA.decalib.models.frnet",
+ "peekOfCode": "def load_state_dict(model, fname):\n \"\"\"\n Set parameters converted from Caffe models authors of VGGFace2 provide.\n See https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/.\n Arguments:\n model: model\n fname: file name of parameters converted from a Caffe model, assuming the file format is Pickle.\n \"\"\"\n with open(fname, 'rb') as f:\n weights = pickle.load(f, encoding='latin1')",
+ "detail": "modules.DECA.decalib.models.frnet",
+ "documentation": {}
+ },
+ {
+ "label": "rot_mat_to_euler",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.lbs",
+ "description": "modules.DECA.decalib.models.lbs",
+ "peekOfCode": "def rot_mat_to_euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +\n rot_mats[:, 1, 0] * rot_mats[:, 1, 0])\n return torch.atan2(-rot_mats[:, 2, 0], sy)\ndef find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx,\n dynamic_lmk_b_coords,\n neck_kin_chain, dtype=torch.float32):\n ''' Compute the faces, barycentric coordinates for the dynamic landmarks",
+ "detail": "modules.DECA.decalib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "find_dynamic_lmk_idx_and_bcoords",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.lbs",
+ "description": "modules.DECA.decalib.models.lbs",
+ "peekOfCode": "def find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx,\n dynamic_lmk_b_coords,\n neck_kin_chain, dtype=torch.float32):\n ''' Compute the faces, barycentric coordinates for the dynamic landmarks\n To do so, we first compute the rotation of the neck around the y-axis\n and then use a pre-computed look-up table to find the faces and the\n barycentric coordinates that will be used.\n Special thanks to Soubhik Sanyal (soubhik.sanyal@tuebingen.mpg.de)\n for providing the original TensorFlow implementation and for the LUT.\n Parameters",
+ "detail": "modules.DECA.decalib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "vertices2landmarks",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.lbs",
+ "description": "modules.DECA.decalib.models.lbs",
+ "peekOfCode": "def vertices2landmarks(vertices, faces, lmk_faces_idx, lmk_bary_coords):\n ''' Calculates landmarks by barycentric interpolation\n Parameters\n ----------\n vertices: torch.tensor BxVx3, dtype = torch.float32\n The tensor of input vertices\n faces: torch.tensor Fx3, dtype = torch.long\n The faces of the mesh\n lmk_faces_idx: torch.tensor L, dtype = torch.long\n The tensor with the indices of the faces used to calculate the",
+ "detail": "modules.DECA.decalib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "lbs",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.lbs",
+ "description": "modules.DECA.decalib.models.lbs",
+ "peekOfCode": "def lbs(betas, pose, v_template, shapedirs, posedirs, J_regressor, parents,\n lbs_weights, pose2rot=True, dtype=torch.float32):\n ''' Performs Linear Blend Skinning with the given shape and pose parameters\n Parameters\n ----------\n betas : torch.tensor BxNB\n The tensor of shape parameters\n pose : torch.tensor Bx(J + 1) * 3\n The pose parameters in axis-angle format\n v_template torch.tensor BxVx3",
+ "detail": "modules.DECA.decalib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "vertices2joints",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.lbs",
+ "description": "modules.DECA.decalib.models.lbs",
+ "peekOfCode": "def vertices2joints(J_regressor, vertices):\n ''' Calculates the 3D joint locations from the vertices\n Parameters\n ----------\n J_regressor : torch.tensor JxV\n The regressor array that is used to calculate the joints from the\n position of the vertices\n vertices : torch.tensor BxVx3\n The tensor of mesh vertices\n Returns",
+ "detail": "modules.DECA.decalib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "blend_shapes",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.lbs",
+ "description": "modules.DECA.decalib.models.lbs",
+ "peekOfCode": "def blend_shapes(betas, shape_disps):\n ''' Calculates the per vertex displacement due to the blend shapes\n Parameters\n ----------\n betas : torch.tensor Bx(num_betas)\n Blend shape coefficients\n shape_disps: torch.tensor Vx3x(num_betas)\n Blend shapes\n Returns\n -------",
+ "detail": "modules.DECA.decalib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "batch_rodrigues",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.lbs",
+ "description": "modules.DECA.decalib.models.lbs",
+ "peekOfCode": "def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):\n ''' Calculates the rotation matrices for a batch of rotation vectors\n Parameters\n ----------\n rot_vecs: torch.tensor Nx3\n array of N axis-angle vectors\n Returns\n -------\n R: torch.tensor Nx3x3\n The rotation matrices for the given axis-angle parameters",
+ "detail": "modules.DECA.decalib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "transform_mat",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.lbs",
+ "description": "modules.DECA.decalib.models.lbs",
+ "peekOfCode": "def transform_mat(R, t):\n ''' Creates a batch of transformation matrices\n Args:\n - R: Bx3x3 array of a batch of rotation matrices\n - t: Bx3x1 array of a batch of translation vectors\n Returns:\n - T: Bx4x4 Transformation matrix\n '''\n # No padding left or right, only add an extra row\n return torch.cat([F.pad(R, [0, 0, 0, 1]),",
+ "detail": "modules.DECA.decalib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "batch_rigid_transform",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.lbs",
+ "description": "modules.DECA.decalib.models.lbs",
+ "peekOfCode": "def batch_rigid_transform(rot_mats, joints, parents, dtype=torch.float32):\n \"\"\"\n Applies a batch of rigid transformations to the joints\n Parameters\n ----------\n rot_mats : torch.tensor BxNx3x3\n Tensor of rotation matrices\n joints : torch.tensor BxNx3\n Locations of joints\n parents : torch.tensor BxN",
+ "detail": "modules.DECA.decalib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "ResNet",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "class ResNet(nn.Module):\n def __init__(self, block, layers, num_classes=1000):\n self.inplanes = 64\n super(ResNet, self).__init__()\n self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,\n bias=False)\n self.bn1 = nn.BatchNorm2d(64)\n self.relu = nn.ReLU(inplace=True)\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n self.layer1 = self._make_layer(block, 64, layers[0])",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "Bottleneck",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "class Bottleneck(nn.Module):\n expansion = 4\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(Bottleneck, self).__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(planes)\n self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "BasicBlock",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "class BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(BasicBlock, self).__init__()\n self.conv1 = conv3x3(inplanes, planes, stride)\n self.bn1 = nn.BatchNorm2d(planes)\n self.relu = nn.ReLU(inplace=True)\n self.conv2 = conv3x3(planes, planes)\n self.bn2 = nn.BatchNorm2d(planes)\n self.downsample = downsample",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "DoubleConv",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "class DoubleConv(nn.Module):\n \"\"\"(convolution => [BN] => ReLU) * 2\"\"\"\n def __init__(self, in_channels, out_channels):\n super().__init__()\n self.double_conv = nn.Sequential(\n nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n nn.BatchNorm2d(out_channels),\n nn.ReLU(inplace=True),\n nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n nn.BatchNorm2d(out_channels),",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "Down",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "class Down(nn.Module):\n \"\"\"Downscaling with maxpool then double conv\"\"\"\n def __init__(self, in_channels, out_channels):\n super().__init__()\n self.maxpool_conv = nn.Sequential(\n nn.MaxPool2d(2),\n DoubleConv(in_channels, out_channels)\n )\n def forward(self, x):\n return self.maxpool_conv(x)",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "Up",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "class Up(nn.Module):\n \"\"\"Upscaling then double conv\"\"\"\n def __init__(self, in_channels, out_channels, bilinear=True):\n super().__init__()\n # if bilinear, use the normal convolutions to reduce the number of channels\n if bilinear:\n self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)\n else:\n self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)\n self.conv = DoubleConv(in_channels, out_channels)",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "OutConv",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "class OutConv(nn.Module):\n def __init__(self, in_channels, out_channels):\n super(OutConv, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)\n def forward(self, x):\n return self.conv(x)",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "conv3x3",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "def conv3x3(in_planes, out_planes, stride=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n padding=1, bias=False)\nclass BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(BasicBlock, self).__init__()\n self.conv1 = conv3x3(inplanes, planes, stride)\n self.bn1 = nn.BatchNorm2d(planes)",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "copy_parameter_from_resnet",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "def copy_parameter_from_resnet(model, resnet_dict):\n cur_state_dict = model.state_dict()\n # import ipdb; ipdb.set_trace()\n for name, param in list(resnet_dict.items())[0:None]:\n if name not in cur_state_dict:\n # print(name, ' not available in reconstructed resnet')\n continue\n if isinstance(param, Parameter):\n param = param.data\n try:",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "load_ResNet50Model",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "def load_ResNet50Model():\n model = ResNet(Bottleneck, [3, 4, 6, 3])\n copy_parameter_from_resnet(model, torchvision.models.resnet50(pretrained = False).state_dict())\n return model\ndef load_ResNet101Model():\n model = ResNet(Bottleneck, [3, 4, 23, 3])\n copy_parameter_from_resnet(model, torchvision.models.resnet101(pretrained = True).state_dict())\n return model\ndef load_ResNet152Model():\n model = ResNet(Bottleneck, [3, 8, 36, 3])",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "load_ResNet101Model",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "def load_ResNet101Model():\n model = ResNet(Bottleneck, [3, 4, 23, 3])\n copy_parameter_from_resnet(model, torchvision.models.resnet101(pretrained = True).state_dict())\n return model\ndef load_ResNet152Model():\n model = ResNet(Bottleneck, [3, 8, 36, 3])\n copy_parameter_from_resnet(model, torchvision.models.resnet152(pretrained = True).state_dict())\n return model\n# model.load_state_dict(checkpoint['model_state_dict'])\n######## Unet",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "load_ResNet152Model",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.models.resnet",
+ "description": "modules.DECA.decalib.models.resnet",
+ "peekOfCode": "def load_ResNet152Model():\n model = ResNet(Bottleneck, [3, 8, 36, 3])\n copy_parameter_from_resnet(model, torchvision.models.resnet152(pretrained = True).state_dict())\n return model\n# model.load_state_dict(checkpoint['model_state_dict'])\n######## Unet\nclass DoubleConv(nn.Module):\n \"\"\"(convolution => [BN] => ReLU) * 2\"\"\"\n def __init__(self, in_channels, out_channels):\n super().__init__()",
+ "detail": "modules.DECA.decalib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "os.environ[\"CC\"]",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.rasterizer.setup",
+ "description": "modules.DECA.decalib.utils.rasterizer.setup",
+ "peekOfCode": "os.environ[\"CC\"] = \"gcc-7\"\nos.environ[\"CXX\"] = \"gcc-7\"\nUSE_NINJA = os.getenv('USE_NINJA') == '1'\nsetup(\n name='standard_rasterize_cuda',\n ext_modules=[\n\tCUDAExtension('standard_rasterize_cuda', [\n 'standard_rasterize_cuda.cpp',\n 'standard_rasterize_cuda_kernel.cu',\n ])",
+ "detail": "modules.DECA.decalib.utils.rasterizer.setup",
+ "documentation": {}
+ },
+ {
+ "label": "os.environ[\"CXX\"]",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.rasterizer.setup",
+ "description": "modules.DECA.decalib.utils.rasterizer.setup",
+ "peekOfCode": "os.environ[\"CXX\"] = \"gcc-7\"\nUSE_NINJA = os.getenv('USE_NINJA') == '1'\nsetup(\n name='standard_rasterize_cuda',\n ext_modules=[\n\tCUDAExtension('standard_rasterize_cuda', [\n 'standard_rasterize_cuda.cpp',\n 'standard_rasterize_cuda_kernel.cu',\n ])\n\t],",
+ "detail": "modules.DECA.decalib.utils.rasterizer.setup",
+ "documentation": {}
+ },
+ {
+ "label": "USE_NINJA",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.rasterizer.setup",
+ "description": "modules.DECA.decalib.utils.rasterizer.setup",
+ "peekOfCode": "USE_NINJA = os.getenv('USE_NINJA') == '1'\nsetup(\n name='standard_rasterize_cuda',\n ext_modules=[\n\tCUDAExtension('standard_rasterize_cuda', [\n 'standard_rasterize_cuda.cpp',\n 'standard_rasterize_cuda_kernel.cu',\n ])\n\t],\n cmdclass={'build_ext': BuildExtension.with_options(use_ninja=USE_NINJA)}",
+ "detail": "modules.DECA.decalib.utils.rasterizer.setup",
+ "documentation": {}
+ },
+ {
+ "label": "get_cfg_defaults",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "def get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)\n return cfg.clone()\ndef parse_args():\n parser = argparse.ArgumentParser()",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "update_cfg",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "def update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)\n return cfg.clone()\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--cfg', type=str, help='cfg file path')\n parser.add_argument('--mode', type=str, default = 'train', help='deca mode')\n args = parser.parse_args()\n print(args, end='\\n\\n')\n cfg = get_cfg_defaults()",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "parse_args",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--cfg', type=str, help='cfg file path')\n parser.add_argument('--mode', type=str, default = 'train', help='deca mode')\n args = parser.parse_args()\n print(args, end='\\n\\n')\n cfg = get_cfg_defaults()\n cfg.cfg_file = None\n cfg.mode = args.mode\n # import ipdb; ipdb.set_trace()",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg = CN()\n#TODO model path here\n# deca_dir-->\n# data-->\n# deca_model.tar\n# ......\nabs_deca_dir = os.path.abspath(os.path.join(\nos.path.dirname(__file__), '../../../../../models/models_deca'))\n# print(f'abs_deca_dir:{abs_deca_dir}')\ncfg.deca_dir = abs_deca_dir",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "abs_deca_dir",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "abs_deca_dir = os.path.abspath(os.path.join(\nos.path.dirname(__file__), '../../../../../models/models_deca'))\n# print(f'abs_deca_dir:{abs_deca_dir}')\ncfg.deca_dir = abs_deca_dir\ncfg.device = 'cuda'\ncfg.device_id = '0'\ncfg.pretrained_modelpath = os.path.join(cfg.deca_dir, 'data', 'deca_model.tar')\ncfg.output_dir = ''\ncfg.rasterizer_type = 'pytorch3d'\n# ---------------------------------------------------------------------------- #",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.deca_dir",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.deca_dir = abs_deca_dir\ncfg.device = 'cuda'\ncfg.device_id = '0'\ncfg.pretrained_modelpath = os.path.join(cfg.deca_dir, 'data', 'deca_model.tar')\ncfg.output_dir = ''\ncfg.rasterizer_type = 'pytorch3d'\n# ---------------------------------------------------------------------------- #\n# Options for Face model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.device",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.device = 'cuda'\ncfg.device_id = '0'\ncfg.pretrained_modelpath = os.path.join(cfg.deca_dir, 'data', 'deca_model.tar')\ncfg.output_dir = ''\ncfg.rasterizer_type = 'pytorch3d'\n# ---------------------------------------------------------------------------- #\n# Options for Face model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()\ncfg.model.topology_path = os.path.join(cfg.deca_dir, 'data', 'head_template.obj')",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.device_id",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.device_id = '0'\ncfg.pretrained_modelpath = os.path.join(cfg.deca_dir, 'data', 'deca_model.tar')\ncfg.output_dir = ''\ncfg.rasterizer_type = 'pytorch3d'\n# ---------------------------------------------------------------------------- #\n# Options for Face model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()\ncfg.model.topology_path = os.path.join(cfg.deca_dir, 'data', 'head_template.obj')\n# texture data original from http://files.is.tue.mpg.de/tbolkart/FLAME/FLAME_texture_data.zip",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.pretrained_modelpath",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.pretrained_modelpath = os.path.join(cfg.deca_dir, 'data', 'deca_model.tar')\ncfg.output_dir = ''\ncfg.rasterizer_type = 'pytorch3d'\n# ---------------------------------------------------------------------------- #\n# Options for Face model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()\ncfg.model.topology_path = os.path.join(cfg.deca_dir, 'data', 'head_template.obj')\n# texture data original from http://files.is.tue.mpg.de/tbolkart/FLAME/FLAME_texture_data.zip\ncfg.model.dense_template_path = os.path.join(cfg.deca_dir, 'data', 'texture_data_256.npy')",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.output_dir",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.output_dir = ''\ncfg.rasterizer_type = 'pytorch3d'\n# ---------------------------------------------------------------------------- #\n# Options for Face model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()\ncfg.model.topology_path = os.path.join(cfg.deca_dir, 'data', 'head_template.obj')\n# texture data original from http://files.is.tue.mpg.de/tbolkart/FLAME/FLAME_texture_data.zip\ncfg.model.dense_template_path = os.path.join(cfg.deca_dir, 'data', 'texture_data_256.npy')\ncfg.model.fixed_displacement_path = os.path.join(cfg.deca_dir, 'data', 'fixed_displacement_256.npy')",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.rasterizer_type",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.rasterizer_type = 'pytorch3d'\n# ---------------------------------------------------------------------------- #\n# Options for Face model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()\ncfg.model.topology_path = os.path.join(cfg.deca_dir, 'data', 'head_template.obj')\n# texture data original from http://files.is.tue.mpg.de/tbolkart/FLAME/FLAME_texture_data.zip\ncfg.model.dense_template_path = os.path.join(cfg.deca_dir, 'data', 'texture_data_256.npy')\ncfg.model.fixed_displacement_path = os.path.join(cfg.deca_dir, 'data', 'fixed_displacement_256.npy')\ncfg.model.flame_model_path = os.path.join(cfg.deca_dir, 'data', 'generic_model.pkl') ",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.model = CN()\ncfg.model.topology_path = os.path.join(cfg.deca_dir, 'data', 'head_template.obj')\n# texture data original from http://files.is.tue.mpg.de/tbolkart/FLAME/FLAME_texture_data.zip\ncfg.model.dense_template_path = os.path.join(cfg.deca_dir, 'data', 'texture_data_256.npy')\ncfg.model.fixed_displacement_path = os.path.join(cfg.deca_dir, 'data', 'fixed_displacement_256.npy')\ncfg.model.flame_model_path = os.path.join(cfg.deca_dir, 'data', 'generic_model.pkl') \ncfg.model.flame_lmk_embedding_path = os.path.join(cfg.deca_dir, 'data', 'landmark_embedding.npy') \ncfg.model.face_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_mask.png') \ncfg.model.face_eye_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_eye_mask.png') \ncfg.model.mean_tex_path = os.path.join(cfg.deca_dir, 'data', 'mean_texture.jpg') ",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.topology_path",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.model.topology_path = os.path.join(cfg.deca_dir, 'data', 'head_template.obj')\n# texture data original from http://files.is.tue.mpg.de/tbolkart/FLAME/FLAME_texture_data.zip\ncfg.model.dense_template_path = os.path.join(cfg.deca_dir, 'data', 'texture_data_256.npy')\ncfg.model.fixed_displacement_path = os.path.join(cfg.deca_dir, 'data', 'fixed_displacement_256.npy')\ncfg.model.flame_model_path = os.path.join(cfg.deca_dir, 'data', 'generic_model.pkl') \ncfg.model.flame_lmk_embedding_path = os.path.join(cfg.deca_dir, 'data', 'landmark_embedding.npy') \ncfg.model.face_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_mask.png') \ncfg.model.face_eye_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_eye_mask.png') \ncfg.model.mean_tex_path = os.path.join(cfg.deca_dir, 'data', 'mean_texture.jpg') \ncfg.model.tex_path = os.path.join(cfg.deca_dir, 'data', 'FLAME_albedo_from_BFM.npz') ",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.dense_template_path",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.model.dense_template_path = os.path.join(cfg.deca_dir, 'data', 'texture_data_256.npy')\ncfg.model.fixed_displacement_path = os.path.join(cfg.deca_dir, 'data', 'fixed_displacement_256.npy')\ncfg.model.flame_model_path = os.path.join(cfg.deca_dir, 'data', 'generic_model.pkl') \ncfg.model.flame_lmk_embedding_path = os.path.join(cfg.deca_dir, 'data', 'landmark_embedding.npy') \ncfg.model.face_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_mask.png') \ncfg.model.face_eye_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_eye_mask.png') \ncfg.model.mean_tex_path = os.path.join(cfg.deca_dir, 'data', 'mean_texture.jpg') \ncfg.model.tex_path = os.path.join(cfg.deca_dir, 'data', 'FLAME_albedo_from_BFM.npz') \ncfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM\ncfg.model.uv_size = 256",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.fixed_displacement_path",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.model.fixed_displacement_path = os.path.join(cfg.deca_dir, 'data', 'fixed_displacement_256.npy')\ncfg.model.flame_model_path = os.path.join(cfg.deca_dir, 'data', 'generic_model.pkl') \ncfg.model.flame_lmk_embedding_path = os.path.join(cfg.deca_dir, 'data', 'landmark_embedding.npy') \ncfg.model.face_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_mask.png') \ncfg.model.face_eye_mask_path = os.path.join(cfg.deca_dir, 'data', 'uv_face_eye_mask.png') \ncfg.model.mean_tex_path = os.path.join(cfg.deca_dir, 'data', 'mean_texture.jpg') \ncfg.model.tex_path = os.path.join(cfg.deca_dir, 'data', 'FLAME_albedo_from_BFM.npz') \ncfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM\ncfg.model.uv_size = 256\ncfg.model.param_list = ['shape', 'tex', 'exp', 'pose', 'cam', 'light']",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.flame_model_path",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
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+ "label": "cfg.model.param_list",
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+ "description": "modules.DECA.decalib.utils.config",
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+ "label": "cfg.model.n_shape",
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+ "importPath": "modules.DECA.decalib.utils.config",
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+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
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+ "label": "cfg.train.train_detail",
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+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
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+ "label": "cfg.train.max_epochs",
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+ "peekOfCode": "cfg.train.max_epochs = 500\ncfg.train.max_steps = 1000000\ncfg.train.lr = 1e-4\ncfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 500\ncfg.train.val_steps = 500",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
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+ {
+ "label": "cfg.train.max_steps",
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+ "importPath": "modules.DECA.decalib.utils.config",
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+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.lr",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.train.lr = 1e-4\ncfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 500\ncfg.train.val_steps = 500\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.log_dir",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 500\ncfg.train.val_steps = 500\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.resume = True",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.log_steps",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 500\ncfg.train.val_steps = 500\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.resume = True\n# ---------------------------------------------------------------------------- #",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.vis_dir",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 500\ncfg.train.val_steps = 500\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.resume = True\n# ---------------------------------------------------------------------------- #\n# Options for Losses",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.vis_steps",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 500\ncfg.train.val_steps = 500\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.resume = True\n# ---------------------------------------------------------------------------- #\n# Options for Losses\n# ---------------------------------------------------------------------------- #",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.write_summary",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.train.write_summary = True\ncfg.train.checkpoint_steps = 500\ncfg.train.val_steps = 500\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.resume = True\n# ---------------------------------------------------------------------------- #\n# Options for Losses\n# ---------------------------------------------------------------------------- #\ncfg.loss = CN()",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.checkpoint_steps",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.train.checkpoint_steps = 500\ncfg.train.val_steps = 500\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.resume = True\n# ---------------------------------------------------------------------------- #\n# Options for Losses\n# ---------------------------------------------------------------------------- #\ncfg.loss = CN()\ncfg.loss.lmk = 1.0",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.val_steps",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.train.val_steps = 500\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.resume = True\n# ---------------------------------------------------------------------------- #\n# Options for Losses\n# ---------------------------------------------------------------------------- #\ncfg.loss = CN()\ncfg.loss.lmk = 1.0\ncfg.loss.useWlmk = True",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.val_vis_dir",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.resume = True\n# ---------------------------------------------------------------------------- #\n# Options for Losses\n# ---------------------------------------------------------------------------- #\ncfg.loss = CN()\ncfg.loss.lmk = 1.0\ncfg.loss.useWlmk = True\ncfg.loss.eyed = 1.0",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.eval_steps",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.train.eval_steps = 5000\ncfg.train.resume = True\n# ---------------------------------------------------------------------------- #\n# Options for Losses\n# ---------------------------------------------------------------------------- #\ncfg.loss = CN()\ncfg.loss.lmk = 1.0\ncfg.loss.useWlmk = True\ncfg.loss.eyed = 1.0\ncfg.loss.lipd = 0.5",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.resume",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.train.resume = True\n# ---------------------------------------------------------------------------- #\n# Options for Losses\n# ---------------------------------------------------------------------------- #\ncfg.loss = CN()\ncfg.loss.lmk = 1.0\ncfg.loss.useWlmk = True\ncfg.loss.eyed = 1.0\ncfg.loss.lipd = 0.5\ncfg.loss.photo = 2.0",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss = CN()\ncfg.loss.lmk = 1.0\ncfg.loss.useWlmk = True\ncfg.loss.eyed = 1.0\ncfg.loss.lipd = 0.5\ncfg.loss.photo = 2.0\ncfg.loss.useSeg = True\ncfg.loss.id = 0.2\ncfg.loss.id_shape_only = True\ncfg.loss.reg_shape = 1e-04",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.lmk",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.lmk = 1.0\ncfg.loss.useWlmk = True\ncfg.loss.eyed = 1.0\ncfg.loss.lipd = 0.5\ncfg.loss.photo = 2.0\ncfg.loss.useSeg = True\ncfg.loss.id = 0.2\ncfg.loss.id_shape_only = True\ncfg.loss.reg_shape = 1e-04\ncfg.loss.reg_exp = 1e-04",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.useWlmk",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.useWlmk = True\ncfg.loss.eyed = 1.0\ncfg.loss.lipd = 0.5\ncfg.loss.photo = 2.0\ncfg.loss.useSeg = True\ncfg.loss.id = 0.2\ncfg.loss.id_shape_only = True\ncfg.loss.reg_shape = 1e-04\ncfg.loss.reg_exp = 1e-04\ncfg.loss.reg_tex = 1e-04",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.eyed",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.eyed = 1.0\ncfg.loss.lipd = 0.5\ncfg.loss.photo = 2.0\ncfg.loss.useSeg = True\ncfg.loss.id = 0.2\ncfg.loss.id_shape_only = True\ncfg.loss.reg_shape = 1e-04\ncfg.loss.reg_exp = 1e-04\ncfg.loss.reg_tex = 1e-04\ncfg.loss.reg_light = 1.",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.lipd",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.lipd = 0.5\ncfg.loss.photo = 2.0\ncfg.loss.useSeg = True\ncfg.loss.id = 0.2\ncfg.loss.id_shape_only = True\ncfg.loss.reg_shape = 1e-04\ncfg.loss.reg_exp = 1e-04\ncfg.loss.reg_tex = 1e-04\ncfg.loss.reg_light = 1.\ncfg.loss.reg_jaw_pose = 0. #1.",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.photo",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.photo = 2.0\ncfg.loss.useSeg = True\ncfg.loss.id = 0.2\ncfg.loss.id_shape_only = True\ncfg.loss.reg_shape = 1e-04\ncfg.loss.reg_exp = 1e-04\ncfg.loss.reg_tex = 1e-04\ncfg.loss.reg_light = 1.\ncfg.loss.reg_jaw_pose = 0. #1.\ncfg.loss.use_gender_prior = False",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.useSeg",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.useSeg = True\ncfg.loss.id = 0.2\ncfg.loss.id_shape_only = True\ncfg.loss.reg_shape = 1e-04\ncfg.loss.reg_exp = 1e-04\ncfg.loss.reg_tex = 1e-04\ncfg.loss.reg_light = 1.\ncfg.loss.reg_jaw_pose = 0. #1.\ncfg.loss.use_gender_prior = False\ncfg.loss.shape_consistency = True",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.id",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.id = 0.2\ncfg.loss.id_shape_only = True\ncfg.loss.reg_shape = 1e-04\ncfg.loss.reg_exp = 1e-04\ncfg.loss.reg_tex = 1e-04\ncfg.loss.reg_light = 1.\ncfg.loss.reg_jaw_pose = 0. #1.\ncfg.loss.use_gender_prior = False\ncfg.loss.shape_consistency = True\n# loss for detail",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.id_shape_only",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.id_shape_only = True\ncfg.loss.reg_shape = 1e-04\ncfg.loss.reg_exp = 1e-04\ncfg.loss.reg_tex = 1e-04\ncfg.loss.reg_light = 1.\ncfg.loss.reg_jaw_pose = 0. #1.\ncfg.loss.use_gender_prior = False\ncfg.loss.shape_consistency = True\n# loss for detail\ncfg.loss.detail_consistency = True",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.reg_shape",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.reg_shape = 1e-04\ncfg.loss.reg_exp = 1e-04\ncfg.loss.reg_tex = 1e-04\ncfg.loss.reg_light = 1.\ncfg.loss.reg_jaw_pose = 0. #1.\ncfg.loss.use_gender_prior = False\ncfg.loss.shape_consistency = True\n# loss for detail\ncfg.loss.detail_consistency = True\ncfg.loss.useConstraint = True",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.reg_exp",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.reg_exp = 1e-04\ncfg.loss.reg_tex = 1e-04\ncfg.loss.reg_light = 1.\ncfg.loss.reg_jaw_pose = 0. #1.\ncfg.loss.use_gender_prior = False\ncfg.loss.shape_consistency = True\n# loss for detail\ncfg.loss.detail_consistency = True\ncfg.loss.useConstraint = True\ncfg.loss.mrf = 5e-2",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.reg_tex",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.reg_tex = 1e-04\ncfg.loss.reg_light = 1.\ncfg.loss.reg_jaw_pose = 0. #1.\ncfg.loss.use_gender_prior = False\ncfg.loss.shape_consistency = True\n# loss for detail\ncfg.loss.detail_consistency = True\ncfg.loss.useConstraint = True\ncfg.loss.mrf = 5e-2\ncfg.loss.photo_D = 2.",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.reg_light",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.reg_light = 1.\ncfg.loss.reg_jaw_pose = 0. #1.\ncfg.loss.use_gender_prior = False\ncfg.loss.shape_consistency = True\n# loss for detail\ncfg.loss.detail_consistency = True\ncfg.loss.useConstraint = True\ncfg.loss.mrf = 5e-2\ncfg.loss.photo_D = 2.\ncfg.loss.reg_sym = 0.005",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.reg_jaw_pose",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.reg_jaw_pose = 0. #1.\ncfg.loss.use_gender_prior = False\ncfg.loss.shape_consistency = True\n# loss for detail\ncfg.loss.detail_consistency = True\ncfg.loss.useConstraint = True\ncfg.loss.mrf = 5e-2\ncfg.loss.photo_D = 2.\ncfg.loss.reg_sym = 0.005\ncfg.loss.reg_z = 0.005",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.use_gender_prior",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.use_gender_prior = False\ncfg.loss.shape_consistency = True\n# loss for detail\ncfg.loss.detail_consistency = True\ncfg.loss.useConstraint = True\ncfg.loss.mrf = 5e-2\ncfg.loss.photo_D = 2.\ncfg.loss.reg_sym = 0.005\ncfg.loss.reg_z = 0.005\ncfg.loss.reg_diff = 0.005",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.shape_consistency",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.shape_consistency = True\n# loss for detail\ncfg.loss.detail_consistency = True\ncfg.loss.useConstraint = True\ncfg.loss.mrf = 5e-2\ncfg.loss.photo_D = 2.\ncfg.loss.reg_sym = 0.005\ncfg.loss.reg_z = 0.005\ncfg.loss.reg_diff = 0.005\ndef get_cfg_defaults():",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.detail_consistency",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.detail_consistency = True\ncfg.loss.useConstraint = True\ncfg.loss.mrf = 5e-2\ncfg.loss.photo_D = 2.\ncfg.loss.reg_sym = 0.005\ncfg.loss.reg_z = 0.005\ncfg.loss.reg_diff = 0.005\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.useConstraint",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.useConstraint = True\ncfg.loss.mrf = 5e-2\ncfg.loss.photo_D = 2.\ncfg.loss.reg_sym = 0.005\ncfg.loss.reg_z = 0.005\ncfg.loss.reg_diff = 0.005\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.mrf",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.mrf = 5e-2\ncfg.loss.photo_D = 2.\ncfg.loss.reg_sym = 0.005\ncfg.loss.reg_z = 0.005\ncfg.loss.reg_diff = 0.005\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.photo_D",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.photo_D = 2.\ncfg.loss.reg_sym = 0.005\ncfg.loss.reg_z = 0.005\ncfg.loss.reg_diff = 0.005\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.reg_sym",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.reg_sym = 0.005\ncfg.loss.reg_z = 0.005\ncfg.loss.reg_diff = 0.005\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.reg_z",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.reg_z = 0.005\ncfg.loss.reg_diff = 0.005\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)\n return cfg.clone()",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.loss.reg_diff",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.config",
+ "description": "modules.DECA.decalib.utils.config",
+ "peekOfCode": "cfg.loss.reg_diff = 0.005\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)\n return cfg.clone()\ndef parse_args():",
+ "detail": "modules.DECA.decalib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "VGG19FeatLayer",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "class VGG19FeatLayer(nn.Module):\n def __init__(self):\n super(VGG19FeatLayer, self).__init__()\n self.vgg19 = models.vgg19(pretrained=True).features.eval().cuda()\n self.mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).cuda()\n self.std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).cuda()\n def forward(self, x):\n out = {}\n x = x - self.mean\n x = x/self.std",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "IDMRFLoss",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "class IDMRFLoss(nn.Module):\n def __init__(self, featlayer=VGG19FeatLayer):\n super(IDMRFLoss, self).__init__()\n self.featlayer = featlayer()\n self.feat_style_layers = {'relu3_2': 1.0, 'relu4_2': 1.0}\n self.feat_content_layers = {'relu4_2': 1.0}\n self.bias = 1.0\n self.nn_stretch_sigma = 0.5\n self.lambda_style = 1.0\n self.lambda_content = 1.0",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "VGG_16",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "class VGG_16(nn.Module):\n \"\"\"\n Main Class\n \"\"\"\n def __init__(self):\n \"\"\"\n Constructor\n \"\"\"\n super().__init__()\n self.block_size = [2, 2, 3, 3, 3]",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "VGGLoss",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "class VGGLoss(nn.Module):\n def __init__(self):\n super(VGGLoss, self).__init__()\n self.featlayer = VGG_16().float()\n self.featlayer.load_weights(path=\"data/face_recognition_model/vgg_face_torch/VGG_FACE.t7\")\n self.featlayer = self.featlayer.cuda().eval()\n self.feat_style_layers = {'relu3_2': 1.0, 'relu4_2': 1.0}\n self.feat_content_layers = {'relu4_2': 1.0}\n self.bias = 1.0\n self.nn_stretch_sigma = 0.5",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "VGGFace2Loss",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "class VGGFace2Loss(nn.Module):\n def __init__(self, pretrained_model, pretrained_data='vggface2'):\n super(VGGFace2Loss, self).__init__()\n self.reg_model = resnet50(num_classes=8631, include_top=False).eval().cuda()\n load_state_dict(self.reg_model, pretrained_model)\n self.mean_bgr = torch.tensor([91.4953, 103.8827, 131.0912]).cuda()\n def reg_features(self, x):\n # out = []\n margin=10\n x = x[:,:,margin:224-margin,margin:224-margin]",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "l2_distance",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def l2_distance(verts1, verts2):\n return torch.sqrt(((verts1 - verts2)**2).sum(2)).mean(1).mean()\n### VAE\ndef kl_loss(texcode):\n \"\"\"\n recon_x: generating images\n x: origin images\n mu: latent mean\n logvar: latent log variance\n \"\"\"",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "kl_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def kl_loss(texcode):\n \"\"\"\n recon_x: generating images\n x: origin images\n mu: latent mean\n logvar: latent log variance\n \"\"\"\n mu, logvar = texcode[:,:128], texcode[:,128:]\n KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)\n KLD = torch.sum(KLD_element).mul_(-0.5)",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "shading_white_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def shading_white_loss(shading):\n '''\n regularize lighting: assume lights close to white \n '''\n # rgb_diff = (shading[:,0] - shading[:,1])**2 + (shading[:,0] - shading[:,2])**2 + (shading[:,1] - shading[:,2])**2\n # rgb_diff = (shading[:,0].mean([1,2]) - shading[:,1].mean([1,2]))**2 + (shading[:,0].mean([1,2]) - shading[:,2].mean([1,2]))**2 + (shading[:,1].mean([1,2]) - shading[:,2].mean([1,2]))**2\n # rgb_diff = (shading.mean([2, 3]) - torch.ones((shading.shape[0], 3)).float().cuda())**2\n rgb_diff = (shading.mean([0, 2, 3]) - 0.99)**2\n return rgb_diff.mean()\ndef shading_smooth_loss(shading):",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "shading_smooth_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def shading_smooth_loss(shading):\n '''\n assume: shading should be smooth\n ref: Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion\n '''\n dx = shading[:,:,1:-1,1:] - shading[:,:,1:-1,:-1]\n dy = shading[:,:,1:,1:-1] - shading[:,:,:-1,1:-1]\n gradient_image = (dx**2).mean() + (dy**2).mean()\n return gradient_image.mean()\n### ------------------------------------- Losses/Regularizations for albedo",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "albedo_constancy_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def albedo_constancy_loss(albedo, alpha = 15, weight = 1.):\n '''\n for similarity of neighbors\n ref: Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz\n Towards High-fidelity Nonlinear 3D Face Morphable Model\n '''\n albedo_chromaticity = albedo/(torch.sum(albedo, dim=1, keepdim=True) + 1e-6)\n weight_x = torch.exp(-alpha*(albedo_chromaticity[:,:,1:,:] - albedo_chromaticity[:,:,:-1,:])**2).detach()\n weight_y = torch.exp(-alpha*(albedo_chromaticity[:,:,:,1:] - albedo_chromaticity[:,:,:,:-1])**2).detach()\n albedo_const_loss_x = ((albedo[:,:,1:,:] - albedo[:,:,:-1,:])**2)*weight_x",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "albedo_ring_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def albedo_ring_loss(texcode, ring_elements, margin, weight=1.):\n \"\"\"\n computes ring loss for ring_outputs before FLAME decoder\n Inputs:\n ring_outputs = a list containing N streams of the ring; len(ring_outputs) = N\n Each ring_outputs[i] is a tensor of (batch_size X shape_dim_num)\n Aim is to force each row (same subject) of each stream to produce same shape\n Each row of first N-1 strams are of the same subject and\n the Nth stream is the different subject\n \"\"\"",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "albedo_same_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def albedo_same_loss(albedo, ring_elements, weight=1.):\n \"\"\"\n computes ring loss for ring_outputs before FLAME decoder\n Inputs:\n ring_outputs = a list containing N streams of the ring; len(ring_outputs) = N\n Each ring_outputs[i] is a tensor of (batch_size X shape_dim_num)\n Aim is to force each row (same subject) of each stream to produce same shape\n Each row of first N-1 strams are of the same subject and\n the Nth stream is the different subject\n \"\"\"",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "batch_kp_2d_l1_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def batch_kp_2d_l1_loss(real_2d_kp, predicted_2d_kp, weights=None):\n \"\"\"\n Computes the l1 loss between the ground truth keypoints and the predicted keypoints\n Inputs:\n kp_gt : N x K x 3\n kp_pred: N x K x 2\n \"\"\"\n if weights is not None:\n real_2d_kp[:,:,2] = weights[None,:]*real_2d_kp[:,:,2]\n kp_gt = real_2d_kp.view(-1, 3)",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "landmark_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def landmark_loss(predicted_landmarks, landmarks_gt, weight=1.):\n # (predicted_theta, predicted_verts, predicted_landmarks) = ringnet_outputs[-1]\n if torch.is_tensor(landmarks_gt) is not True:\n real_2d = torch.cat(landmarks_gt).cuda()\n else:\n real_2d = torch.cat([landmarks_gt, torch.ones((landmarks_gt.shape[0], 68, 1)).cuda()], dim=-1)\n # real_2d = torch.cat(landmarks_gt).cuda()\n loss_lmk_2d = batch_kp_2d_l1_loss(real_2d, predicted_landmarks)\n return loss_lmk_2d * weight\ndef eye_dis(landmarks):",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "eye_dis",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def eye_dis(landmarks):\n # left eye: [38,42], [39,41] - 1\n # right eye: [44,48], [45,47] -1\n eye_up = landmarks[:,[37, 38, 43, 44], :]\n eye_bottom = landmarks[:,[41, 40, 47, 46], :]\n dis = torch.sqrt(((eye_up - eye_bottom)**2).sum(2)) #[bz, 4]\n return dis\ndef eyed_loss(predicted_landmarks, landmarks_gt, weight=1.):\n if torch.is_tensor(landmarks_gt) is not True:\n real_2d = torch.cat(landmarks_gt).cuda()",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "eyed_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def eyed_loss(predicted_landmarks, landmarks_gt, weight=1.):\n if torch.is_tensor(landmarks_gt) is not True:\n real_2d = torch.cat(landmarks_gt).cuda()\n else:\n real_2d = torch.cat([landmarks_gt, torch.ones((landmarks_gt.shape[0], 68, 1)).cuda()], dim=-1)\n pred_eyed = eye_dis(predicted_landmarks[:,:,:2])\n gt_eyed = eye_dis(real_2d[:,:,:2])\n loss = (pred_eyed - gt_eyed).abs().mean()\n return loss\ndef lip_dis(landmarks):",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "lip_dis",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def lip_dis(landmarks):\n # up inner lip: [62, 63, 64] - 1\n # down innder lip: [68, 67, 66] -1\n lip_up = landmarks[:,[61, 62, 63], :]\n lip_down = landmarks[:,[67, 66, 65], :]\n dis = torch.sqrt(((lip_up - lip_down)**2).sum(2)) #[bz, 4]\n return dis\ndef lipd_loss(predicted_landmarks, landmarks_gt, weight=1.):\n if torch.is_tensor(landmarks_gt) is not True:\n real_2d = torch.cat(landmarks_gt).cuda()",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "lipd_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def lipd_loss(predicted_landmarks, landmarks_gt, weight=1.):\n if torch.is_tensor(landmarks_gt) is not True:\n real_2d = torch.cat(landmarks_gt).cuda()\n else:\n real_2d = torch.cat([landmarks_gt, torch.ones((landmarks_gt.shape[0], 68, 1)).cuda()], dim=-1)\n pred_lipd = lip_dis(predicted_landmarks[:,:,:2])\n gt_lipd = lip_dis(real_2d[:,:,:2])\n loss = (pred_lipd - gt_lipd).abs().mean()\n return loss\ndef weighted_landmark_loss(predicted_landmarks, landmarks_gt, weight=1.):",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "weighted_landmark_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def weighted_landmark_loss(predicted_landmarks, landmarks_gt, weight=1.):\n #smaller inner landmark weights\n # (predicted_theta, predicted_verts, predicted_landmarks) = ringnet_outputs[-1]\n # import ipdb; ipdb.set_trace()\n real_2d = landmarks_gt\n weights = torch.ones((68,)).cuda()\n weights[5:7] = 2\n weights[10:12] = 2\n # nose points\n weights[27:36] = 1.5",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "landmark_loss_tensor",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def landmark_loss_tensor(predicted_landmarks, landmarks_gt, weight=1.):\n # (predicted_theta, predicted_verts, predicted_landmarks) = ringnet_outputs[-1]\n loss_lmk_2d = batch_kp_2d_l1_loss(landmarks_gt, predicted_landmarks)\n return loss_lmk_2d * weight\ndef ring_loss(ring_outputs, ring_type, margin, weight=1.):\n \"\"\"\n computes ring loss for ring_outputs before FLAME decoder\n Inputs:\n ring_outputs = a list containing N streams of the ring; len(ring_outputs) = N\n Each ring_outputs[i] is a tensor of (batch_size X shape_dim_num)",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "ring_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def ring_loss(ring_outputs, ring_type, margin, weight=1.):\n \"\"\"\n computes ring loss for ring_outputs before FLAME decoder\n Inputs:\n ring_outputs = a list containing N streams of the ring; len(ring_outputs) = N\n Each ring_outputs[i] is a tensor of (batch_size X shape_dim_num)\n Aim is to force each row (same subject) of each stream to produce same shape\n Each row of first N-1 strams are of the same subject and\n the Nth stream is the different subject\n \"\"\"",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "gradient_dif_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def gradient_dif_loss(prediction, gt):\n prediction_diff_x = prediction[:,:,1:-1,1:] - prediction[:,:,1:-1,:-1]\n prediction_diff_y = prediction[:,:,1:,1:-1] - prediction[:,:,1:,1:-1]\n gt_x = gt[:,:,1:-1,1:] - gt[:,:,1:-1,:-1]\n gt_y = gt[:,:,1:,1:-1] - gt[:,:,:-1,1:-1]\n diff = torch.mean((prediction_diff_x-gt_x)**2) + torch.mean((prediction_diff_y-gt_y)**2)\n return diff.mean()\ndef get_laplacian_kernel2d(kernel_size: int):\n r\"\"\"Function that returns Gaussian filter matrix coefficients.\n Args:",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "get_laplacian_kernel2d",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def get_laplacian_kernel2d(kernel_size: int):\n r\"\"\"Function that returns Gaussian filter matrix coefficients.\n Args:\n kernel_size (int): filter size should be odd.\n Returns:\n Tensor: 2D tensor with laplacian filter matrix coefficients.\n Shape:\n - Output: :math:`(\\text{kernel_size}_x, \\text{kernel_size}_y)`\n Examples::\n >>> kornia.image.get_laplacian_kernel2d(3)",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "laplacian_hq_loss",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.lossfunc",
+ "description": "modules.DECA.decalib.utils.lossfunc",
+ "peekOfCode": "def laplacian_hq_loss(prediction, gt):\n # https://torchgeometry.readthedocs.io/en/latest/_modules/kornia/filters/laplacian.html\n b, c, h, w = prediction.shape\n kernel_size = 3\n kernel = get_laplacian_kernel2d(kernel_size).to(prediction.device).to(prediction.dtype)\n kernel = kernel.repeat(c, 1, 1, 1)\n padding = (kernel_size - 1) // 2\n lap_pre = F.conv2d(prediction, kernel, padding=padding, stride=1, groups=c)\n lap_gt = F.conv2d(gt, kernel, padding=padding, stride=1, groups=c)\n return ((lap_pre - lap_gt)**2).mean()",
+ "detail": "modules.DECA.decalib.utils.lossfunc",
+ "documentation": {}
+ },
+ {
+ "label": "StandardRasterizer",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.utils.renderer",
+ "description": "modules.DECA.decalib.utils.renderer",
+ "peekOfCode": "class StandardRasterizer(nn.Module):\n \"\"\" Alg: https://www.scratchapixel.com/lessons/3d-basic-rendering/rasterization-practical-implementation\n Notice:\n x,y,z are in image space, normalized to [-1, 1]\n can render non-squared image\n not differentiable\n \"\"\"\n def __init__(self, height, width=None):\n \"\"\"\n use fixed raster_settings for rendering faces",
+ "detail": "modules.DECA.decalib.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "Pytorch3dRasterizer",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.utils.renderer",
+ "description": "modules.DECA.decalib.utils.renderer",
+ "peekOfCode": "class Pytorch3dRasterizer(nn.Module):\n ## TODO: add support for rendering non-squared images, since pytorc3d supports this now\n \"\"\" Borrowed from https://github.com/facebookresearch/pytorch3d\n Notice:\n x,y,z are in image space, normalized\n can only render squared image now\n \"\"\"\n def __init__(self, image_size=224):\n \"\"\"\n use fixed raster_settings for rendering faces",
+ "detail": "modules.DECA.decalib.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "SRenderY",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.utils.renderer",
+ "description": "modules.DECA.decalib.utils.renderer",
+ "peekOfCode": "class SRenderY(nn.Module):\n def __init__(self, image_size, obj_filename, uv_size=256, rasterizer_type='pytorch3d'):\n super(SRenderY, self).__init__()\n self.image_size = image_size\n self.uv_size = uv_size\n if rasterizer_type == 'pytorch3d':\n self.rasterizer = Pytorch3dRasterizer(image_size)\n self.uv_rasterizer = Pytorch3dRasterizer(uv_size)\n verts, faces, aux = load_obj(obj_filename)\n uvcoords = aux.verts_uvs[None, ...] # (N, V, 2)",
+ "detail": "modules.DECA.decalib.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "set_rasterizer",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.renderer",
+ "description": "modules.DECA.decalib.utils.renderer",
+ "peekOfCode": "def set_rasterizer(type = 'pytorch3d'):\n if type == 'pytorch3d':\n global Meshes, load_obj, rasterize_meshes\n from pytorch3d.structures import Meshes\n from pytorch3d.io import load_obj\n from pytorch3d.renderer.mesh import rasterize_meshes\n elif type == 'standard':\n global standard_rasterize, load_obj\n import os\n from .util import load_obj",
+ "detail": "modules.DECA.decalib.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "rad2deg",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def rad2deg(tensor):\n \"\"\"Function that converts angles from radians to degrees.\n See :class:`~torchgeometry.RadToDeg` for details.\n Args:\n tensor (Tensor): Tensor of arbitrary shape.\n Returns:\n Tensor: Tensor with same shape as input.\n Example:\n >>> input = tgm.pi * torch.rand(1, 3, 3)\n >>> output = tgm.rad2deg(input)",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "deg2rad",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def deg2rad(tensor):\n \"\"\"Function that converts angles from degrees to radians.\n See :class:`~torchgeometry.DegToRad` for details.\n Args:\n tensor (Tensor): Tensor of arbitrary shape.\n Returns:\n Tensor: Tensor with same shape as input.\n Examples::\n >>> input = 360. * torch.rand(1, 3, 3)\n >>> output = tgm.deg2rad(input)",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "euler_to_quaternion",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def euler_to_quaternion(r):\n x = r[..., 0]\n y = r[..., 1]\n z = r[..., 2]\n z = z/2.0\n y = y/2.0\n x = x/2.0\n cz = torch.cos(z)\n sz = torch.sin(z)\n cy = torch.cos(y)",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "rotation_matrix_to_quaternion",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6):\n \"\"\"Convert 3x4 rotation matrix to 4d quaternion vector\n This algorithm is based on algorithm described in\n https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201\n Args:\n rotation_matrix (Tensor): the rotation matrix to convert.\n Return:\n Tensor: the rotation in quaternion\n Shape:\n - Input: :math:`(N, 3, 4)`",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "angle_axis_to_quaternion",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def angle_axis_to_quaternion(angle_axis: torch.Tensor) -> torch.Tensor:\n \"\"\"Convert an angle axis to a quaternion.\n Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h\n Args:\n angle_axis (torch.Tensor): tensor with angle axis.\n Return:\n torch.Tensor: tensor with quaternion.\n Shape:\n - Input: :math:`(*, 3)` where `*` means, any number of dimensions\n - Output: :math:`(*, 4)`",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "quaternion_to_rotation_matrix",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def quaternion_to_rotation_matrix(quat):\n \"\"\"Convert quaternion coefficients to rotation matrix.\n Args:\n quat: size = [B, 4] 4 <===>(w, x, y, z)\n Returns:\n Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]\n \"\"\"\n norm_quat = quat\n norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True)\n w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:, 2], norm_quat[:, 3]",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "quaternion_to_angle_axis",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def quaternion_to_angle_axis(quaternion: torch.Tensor):\n \"\"\"Convert quaternion vector to angle axis of rotation. TODO: CORRECT\n Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h\n Args:\n quaternion (torch.Tensor): tensor with quaternions.\n Return:\n torch.Tensor: tensor with angle axis of rotation.\n Shape:\n - Input: :math:`(*, 4)` where `*` means, any number of dimensions\n - Output: :math:`(*, 3)`",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_euler2axis",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def batch_euler2axis(r):\n return quaternion_to_angle_axis(euler_to_quaternion(r))\ndef batch_euler2matrix(r):\n return quaternion_to_rotation_matrix(euler_to_quaternion(r))\ndef batch_matrix2euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n ### only y?\n # TODO:\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_euler2matrix",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def batch_euler2matrix(r):\n return quaternion_to_rotation_matrix(euler_to_quaternion(r))\ndef batch_matrix2euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n ### only y?\n # TODO:\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +\n rot_mats[:, 1, 0] * rot_mats[:, 1, 0])\n return torch.atan2(-rot_mats[:, 2, 0], sy)",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_matrix2euler",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def batch_matrix2euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n ### only y?\n # TODO:\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +\n rot_mats[:, 1, 0] * rot_mats[:, 1, 0])\n return torch.atan2(-rot_mats[:, 2, 0], sy)\ndef batch_matrix2axis(rot_mats):\n return quaternion_to_angle_axis(rotation_matrix_to_quaternion(rot_mats))",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_matrix2axis",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def batch_matrix2axis(rot_mats):\n return quaternion_to_angle_axis(rotation_matrix_to_quaternion(rot_mats))\ndef batch_axis2matrix(theta):\n # angle axis to rotation matrix\n # theta N x 3\n # return quat2mat(quat)\n # batch_rodrigues\n return quaternion_to_rotation_matrix(angle_axis_to_quaternion(theta))\ndef batch_axis2euler(theta):\n return batch_matrix2euler(batch_axis2matrix(theta))",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_axis2matrix",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def batch_axis2matrix(theta):\n # angle axis to rotation matrix\n # theta N x 3\n # return quat2mat(quat)\n # batch_rodrigues\n return quaternion_to_rotation_matrix(angle_axis_to_quaternion(theta))\ndef batch_axis2euler(theta):\n return batch_matrix2euler(batch_axis2matrix(theta))\ndef batch_axis2euler(r):\n return rot_mat_to_euler(batch_rodrigues(r))",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_axis2euler",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def batch_axis2euler(theta):\n return batch_matrix2euler(batch_axis2matrix(theta))\ndef batch_axis2euler(r):\n return rot_mat_to_euler(batch_rodrigues(r))\ndef batch_orth_proj(X, camera):\n '''\n X is N x num_pquaternion_to_angle_axisoints x 3\n '''\n camera = camera.clone().view(-1, 1, 3)\n X_trans = X[:, :, :2] + camera[:, :, 1:]",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_axis2euler",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def batch_axis2euler(r):\n return rot_mat_to_euler(batch_rodrigues(r))\ndef batch_orth_proj(X, camera):\n '''\n X is N x num_pquaternion_to_angle_axisoints x 3\n '''\n camera = camera.clone().view(-1, 1, 3)\n X_trans = X[:, :, :2] + camera[:, :, 1:]\n X_trans = torch.cat([X_trans, X[:,:,2:]], 2)\n Xn = (camera[:, :, 0:1] * X_trans)",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_orth_proj",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def batch_orth_proj(X, camera):\n '''\n X is N x num_pquaternion_to_angle_axisoints x 3\n '''\n camera = camera.clone().view(-1, 1, 3)\n X_trans = X[:, :, :2] + camera[:, :, 1:]\n X_trans = torch.cat([X_trans, X[:,:,2:]], 2)\n Xn = (camera[:, :, 0:1] * X_trans)\n return Xn\ndef batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_rodrigues",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):\n ''' same as batch_matrix2axis\n Calculates the rotation matrices for a batch of rotation vectors\n Parameters\n ----------\n rot_vecs: torch.tensor Nx3\n array of N axis-angle vectors\n Returns\n -------\n R: torch.tensor Nx3x3",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "pi",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.rotation_converter",
+ "description": "modules.DECA.decalib.utils.rotation_converter",
+ "peekOfCode": "pi = torch.Tensor([3.14159265358979323846])\ndef rad2deg(tensor):\n \"\"\"Function that converts angles from radians to degrees.\n See :class:`~torchgeometry.RadToDeg` for details.\n Args:\n tensor (Tensor): Tensor of arbitrary shape.\n Returns:\n Tensor: Tensor with same shape as input.\n Example:\n >>> input = tgm.pi * torch.rand(1, 3, 3)",
+ "detail": "modules.DECA.decalib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "Cropper",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.utils.tensor_cropper",
+ "description": "modules.DECA.decalib.utils.tensor_cropper",
+ "peekOfCode": "class Cropper(object):\n def __init__(self, crop_size, scale=[1,1], trans_scale = 0.):\n self.crop_size = crop_size\n self.scale = scale\n self.trans_scale = trans_scale\n def crop(self, image, points, points_scale=None):\n # points to bbox\n center, bbox_size = points2bbox(points.clone(), points_scale)\n # argument bbox. TODO: add rotation?\n center, bbox_size = augment_bbox(center, bbox_size, scale=self.scale, trans_scale=self.trans_scale)",
+ "detail": "modules.DECA.decalib.utils.tensor_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "points2bbox",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.tensor_cropper",
+ "description": "modules.DECA.decalib.utils.tensor_cropper",
+ "peekOfCode": "def points2bbox(points, points_scale=None):\n if points_scale:\n assert points_scale[0]==points_scale[1]\n points = points.clone()\n points[:,:,:2] = (points[:,:,:2]*0.5 + 0.5)*points_scale[0]\n min_coords, _ = torch.min(points, dim=1)\n xmin, ymin = min_coords[:, 0], min_coords[:, 1]\n max_coords, _ = torch.max(points, dim=1)\n xmax, ymax = max_coords[:, 0], max_coords[:, 1]\n center = torch.stack([xmax + xmin, ymax + ymin], dim=-1) * 0.5",
+ "detail": "modules.DECA.decalib.utils.tensor_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "augment_bbox",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.tensor_cropper",
+ "description": "modules.DECA.decalib.utils.tensor_cropper",
+ "peekOfCode": "def augment_bbox(center, bbox_size, scale=[1.0, 1.0], trans_scale=0.):\n batch_size = center.shape[0]\n trans_scale = (torch.rand([batch_size, 2], device=center.device)*2. -1.) * trans_scale\n center = center + trans_scale*bbox_size # 0.5\n scale = torch.rand([batch_size,1], device=center.device) * (scale[1] - scale[0]) + scale[0]\n size = bbox_size*scale\n return center, size\ndef crop_tensor(image, center, bbox_size, crop_size, interpolation = 'bilinear', align_corners=False):\n ''' for batch image\n Args:",
+ "detail": "modules.DECA.decalib.utils.tensor_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "crop_tensor",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.tensor_cropper",
+ "description": "modules.DECA.decalib.utils.tensor_cropper",
+ "peekOfCode": "def crop_tensor(image, center, bbox_size, crop_size, interpolation = 'bilinear', align_corners=False):\n ''' for batch image\n Args:\n image (torch.Tensor): the reference tensor of shape BXHxWXC.\n center: [bz, 2]\n bboxsize: [bz, 1]\n crop_size;\n interpolation (str): Interpolation flag. Default: 'bilinear'.\n align_corners (bool): mode for grid_generation. Default: False. See\n https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.interpolate for details",
+ "detail": "modules.DECA.decalib.utils.tensor_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "transform_points",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.tensor_cropper",
+ "description": "modules.DECA.decalib.utils.tensor_cropper",
+ "peekOfCode": "def transform_points(points, tform, points_scale=None, out_scale=None):\n points_2d = points[:,:,:2]\n #'input points must use original range'\n if points_scale:\n assert points_scale[0]==points_scale[1]\n points_2d = (points_2d*0.5 + 0.5)*points_scale[0]\n # import ipdb; ipdb.set_trace()\n batch_size, n_points, _ = points.shape\n trans_points_2d = torch.bmm(\n torch.cat([points_2d, torch.ones([batch_size, n_points, 1], device=points.device, dtype=points.dtype)], dim=-1), ",
+ "detail": "modules.DECA.decalib.utils.tensor_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "Trainer",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.utils.trainer",
+ "description": "modules.DECA.decalib.utils.trainer",
+ "peekOfCode": "class Trainer(object):\n def __init__(self, model, config=None, device='cuda:0'):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device\n self.batch_size = self.cfg.dataset.batch_size\n self.image_size = self.cfg.dataset.image_size\n self.uv_size = self.cfg.model.uv_size",
+ "detail": "modules.DECA.decalib.utils.trainer",
+ "documentation": {}
+ },
+ {
+ "label": "torch.backends.cudnn.benchmark",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.trainer",
+ "description": "modules.DECA.decalib.utils.trainer",
+ "peekOfCode": "torch.backends.cudnn.benchmark = True\nfrom .utils import lossfunc\nfrom .datasets import build_datasets\nclass Trainer(object):\n def __init__(self, model, config=None, device='cuda:0'):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device",
+ "detail": "modules.DECA.decalib.utils.trainer",
+ "documentation": {}
+ },
+ {
+ "label": "Struct",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "class Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)\n# original saved file with DataParallel\ndef remove_module(state_dict):\n# create new OrderedDict that does not contain `module.`\n new_state_dict = OrderedDict()\n for k, v in state_dict.items():\n name = k[7:] # remove `module.`",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "upsample_mesh",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def upsample_mesh(vertices, normals, faces, displacement_map, texture_map, dense_template):\n ''' Credit to Timo\n upsampling coarse mesh (with displacment map)\n vertices: vertices of coarse mesh, [nv, 3]\n normals: vertex normals, [nv, 3]\n faces: faces of coarse mesh, [nf, 3]\n texture_map: texture map, [256, 256, 3]\n displacement_map: displacment map, [256, 256]\n dense_template: \n Returns: ",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "write_obj",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def write_obj(obj_name,\n vertices,\n faces,\n colors=None,\n texture=None,\n uvcoords=None,\n uvfaces=None,\n inverse_face_order=False,\n normal_map=None,\n ):",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "load_obj",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def load_obj(obj_filename):\n \"\"\" Ref: https://github.com/facebookresearch/pytorch3d/blob/25c065e9dafa90163e7cec873dbb324a637c68b7/pytorch3d/io/obj_io.py\n Load a mesh from a file-like object.\n \"\"\"\n with open(obj_filename, 'r') as f:\n lines = [line.strip() for line in f]\n verts, uvcoords = [], []\n faces, uv_faces = [], []\n # startswith expects each line to be a string. If the file is read in as\n # bytes then first decode to strings.",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "generate_triangles",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def generate_triangles(h, w, margin_x=2, margin_y=5, mask = None):\n # quad layout:\n # 0 1 ... w-1\n # w w+1\n #.\n # w*h\n triangles = []\n for x in range(margin_x, w-1-margin_x):\n for y in range(margin_y, h-1-margin_y):\n triangle0 = [y*w + x, y*w + x + 1, (y+1)*w + x]",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "face_vertices",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def face_vertices(vertices, faces):\n \"\"\" \n :param vertices: [batch size, number of vertices, 3]\n :param faces: [batch size, number of faces, 3]\n :return: [batch size, number of faces, 3, 3]\n \"\"\"\n assert (vertices.ndimension() == 3)\n assert (faces.ndimension() == 3)\n assert (vertices.shape[0] == faces.shape[0])\n assert (vertices.shape[2] == 3)",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "vertex_normals",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def vertex_normals(vertices, faces):\n \"\"\"\n :param vertices: [batch size, number of vertices, 3]\n :param faces: [batch size, number of faces, 3]\n :return: [batch size, number of vertices, 3]\n \"\"\"\n assert (vertices.ndimension() == 3)\n assert (faces.ndimension() == 3)\n assert (vertices.shape[0] == faces.shape[0])\n assert (vertices.shape[2] == 3)",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "batch_orth_proj",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def batch_orth_proj(X, camera):\n ''' orthgraphic projection\n X: 3d vertices, [bz, n_point, 3]\n camera: scale and translation, [bz, 3], [scale, tx, ty]\n '''\n camera = camera.clone().view(-1, 1, 3)\n X_trans = X[:, :, :2] + camera[:, :, 1:]\n X_trans = torch.cat([X_trans, X[:,:,2:]], 2)\n shape = X_trans.shape\n Xn = (camera[:, :, 0:1] * X_trans)",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "gaussian",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def gaussian(window_size, sigma):\n def gauss_fcn(x):\n return -(x - window_size // 2)**2 / float(2 * sigma**2)\n gauss = torch.stack(\n [torch.exp(torch.tensor(gauss_fcn(x))) for x in range(window_size)])\n return gauss / gauss.sum()\ndef get_gaussian_kernel(kernel_size: int, sigma: float):\n r\"\"\"Function that returns Gaussian filter coefficients.\n Args:\n kernel_size (int): filter size. It should be odd and positive.",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "get_gaussian_kernel",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def get_gaussian_kernel(kernel_size: int, sigma: float):\n r\"\"\"Function that returns Gaussian filter coefficients.\n Args:\n kernel_size (int): filter size. It should be odd and positive.\n sigma (float): gaussian standard deviation.\n Returns:\n Tensor: 1D tensor with gaussian filter coefficients.\n Shape:\n - Output: :math:`(\\text{kernel_size})`\n Examples::",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "get_gaussian_kernel2d",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def get_gaussian_kernel2d(kernel_size, sigma):\n r\"\"\"Function that returns Gaussian filter matrix coefficients.\n Args:\n kernel_size (Tuple[int, int]): filter sizes in the x and y direction.\n Sizes should be odd and positive.\n sigma (Tuple[int, int]): gaussian standard deviation in the x and y\n direction.\n Returns:\n Tensor: 2D tensor with gaussian filter matrix coefficients.\n Shape:",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "gaussian_blur",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def gaussian_blur(x, kernel_size=(3,3), sigma=(0.8,0.8)):\n b, c, h, w = x.shape\n kernel = get_gaussian_kernel2d(kernel_size, sigma).to(x.device).to(x.dtype)\n kernel = kernel.repeat(c, 1, 1, 1)\n padding = [(k - 1) // 2 for k in kernel_size]\n return F.conv2d(x, kernel, padding=padding, stride=1, groups=c)\ndef _compute_binary_kernel(window_size):\n r\"\"\"Creates a binary kernel to extract the patches. If the window size\n is HxW will create a (H*W)xHxW kernel.\n \"\"\"",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "median_blur",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def median_blur(x, kernel_size=(3,3)):\n b, c, h, w = x.shape\n kernel = _compute_binary_kernel(kernel_size).to(x.device).to(x.dtype)\n kernel = kernel.repeat(c, 1, 1, 1)\n padding = [(k - 1) // 2 for k in kernel_size]\n features = F.conv2d(x, kernel, padding=padding, stride=1, groups=c)\n features = features.view(b,c,-1,h,w)\n median = torch.median(features, dim=2)[0]\n return median\ndef get_laplacian_kernel2d(kernel_size: int):",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "get_laplacian_kernel2d",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def get_laplacian_kernel2d(kernel_size: int):\n r\"\"\"Function that returns Gaussian filter matrix coefficients.\n Args:\n kernel_size (int): filter size should be odd.\n Returns:\n Tensor: 2D tensor with laplacian filter matrix coefficients.\n Shape:\n - Output: :math:`(\\text{kernel_size}_x, \\text{kernel_size}_y)`\n Examples::\n >>> kornia.image.get_laplacian_kernel2d(3)",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "laplacian",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def laplacian(x):\n # https://torchgeometry.readthedocs.io/en/latest/_modules/kornia/filters/laplacian.html\n b, c, h, w = x.shape\n kernel_size = 3\n kernel = get_laplacian_kernel2d(kernel_size).to(x.device).to(x.dtype)\n kernel = kernel.repeat(c, 1, 1, 1)\n padding = (kernel_size - 1) // 2\n return F.conv2d(x, kernel, padding=padding, stride=1, groups=c)\ndef angle2matrix(angles):\n ''' get rotation matrix from three rotation angles(degree). right-handed.",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "angle2matrix",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def angle2matrix(angles):\n ''' get rotation matrix from three rotation angles(degree). right-handed.\n Args:\n angles: [batch_size, 3] tensor containing X, Y, and Z angles.\n x: pitch. positive for looking down.\n y: yaw. positive for looking left. \n z: roll. positive for tilting head right. \n Returns:\n R: [batch_size, 3, 3]. rotation matrices.\n '''",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "binary_erosion",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def binary_erosion(tensor, kernel_size=5):\n # tensor: [bz, 1, h, w]. \n device = tensor.device\n mask = tensor.cpu().numpy()\n structure=np.ones((kernel_size,kernel_size))\n new_mask = mask.copy()\n for i in range(mask.shape[0]):\n new_mask[i,0] = morphology.binary_erosion(mask[i,0], structure)\n return torch.from_numpy(new_mask.astype(np.float32)).to(device)\ndef flip_image(src_image, kps):",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "flip_image",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def flip_image(src_image, kps):\n '''\n purpose:\n flip a image given by src_image and the 2d keypoints\n flip_mode: \n 0: horizontal flip\n >0: vertical flip\n <0: horizontal & vertical flip\n '''\n h, w = src_image.shape[0], src_image.shape[1]",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "copy_state_dict",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def copy_state_dict(cur_state_dict, pre_state_dict, prefix='', load_name=None):\n def _get_params(key):\n key = prefix + key\n if key in pre_state_dict:\n return pre_state_dict[key]\n return None\n for k in cur_state_dict.keys():\n if load_name is not None:\n if load_name not in k:\n continue",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "check_mkdir",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def check_mkdir(path):\n if not os.path.exists(path):\n print('creating %s' % path)\n os.makedirs(path)\ndef check_mkdirlist(pathlist):\n for path in pathlist:\n if not os.path.exists(path):\n print('creating %s' % path)\n os.makedirs(path)\ndef tensor2image(tensor):",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "check_mkdirlist",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def check_mkdirlist(pathlist):\n for path in pathlist:\n if not os.path.exists(path):\n print('creating %s' % path)\n os.makedirs(path)\ndef tensor2image(tensor):\n image = tensor.detach().cpu().numpy()\n image = image*255.\n image = np.maximum(np.minimum(image, 255), 0)\n image = image.transpose(1,2,0)[:,:,[2,1,0]]",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "tensor2image",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def tensor2image(tensor):\n image = tensor.detach().cpu().numpy()\n image = image*255.\n image = np.maximum(np.minimum(image, 255), 0)\n image = image.transpose(1,2,0)[:,:,[2,1,0]]\n return image.astype(np.uint8).copy()\ndef dict2obj(d):\n # if isinstance(d, list):\n # d = [dict2obj(x) for x in d]\n if not isinstance(d, dict):",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "dict2obj",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def dict2obj(d):\n # if isinstance(d, list):\n # d = [dict2obj(x) for x in d]\n if not isinstance(d, dict):\n return d\n class C(object):\n pass\n o = C()\n for k in d:\n o.__dict__[k] = dict2obj(d[k])",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "remove_module",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def remove_module(state_dict):\n# create new OrderedDict that does not contain `module.`\n new_state_dict = OrderedDict()\n for k, v in state_dict.items():\n name = k[7:] # remove `module.`\n new_state_dict[name] = v\n return new_state_dict\n# def dict_tensor2npy(tensor_dict):\n# npy_dict = {}\n# for key in tensor_dict:",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "dict_tensor2npy",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def dict_tensor2npy(tensor_dict):\n npy_dict = {}\n for key, value in tensor_dict.items():\n # print(type(value))\n # if type(value)==dict:\n # pass\n # # print('dict')\n # # npy_dict[key] = dict_tensor2npy(value)\n # el\n if type(value)==torch.Tensor:",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "plot_kpts",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def plot_kpts(image, kpts, color = 'r'):\n ''' Draw 68 key points\n Args: \n image: the input image\n kpt: (68, 3).\n '''\n if color == 'r':\n c = (255, 0, 0)\n elif color == 'g':\n c = (0, 255, 0)",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "plot_verts",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def plot_verts(image, kpts, color = 'r'):\n ''' Draw 68 key points\n Args: \n image: the input image\n kpt: (68, 3).\n '''\n if color == 'r':\n c = (255, 0, 0)\n elif color == 'g':\n c = (0, 255, 0)",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "tensor_vis_landmarks",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def tensor_vis_landmarks(images, landmarks, gt_landmarks=None, color = 'g', isScale=True):\n # visualize landmarks\n vis_landmarks = []\n images = images.cpu().numpy()\n predicted_landmarks = landmarks.detach().cpu().numpy()\n if gt_landmarks is not None:\n gt_landmarks_np = gt_landmarks.detach().cpu().numpy()\n for i in range(images.shape[0]):\n image = images[i]\n image = image.transpose(1,2,0)[:,:,[2,1,0]].copy(); image = (image*255)",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "load_local_mask",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def load_local_mask(image_size=256, mode='bbx'):\n if mode == 'bbx':\n # UV space face attributes bbx in size 2048 (l r t b)\n # face = np.array([512, 1536, 512, 1536]) #\n face = np.array([400, 1648, 400, 1648])\n # if image_size == 512:\n # face = np.array([400, 400+512*2, 400, 400+512*2])\n # face = np.array([512, 512+512*2, 512, 512+512*2])\n forehead = np.array([550, 1498, 430, 700+50])\n eye_nose = np.array([490, 1558, 700, 1050+50])",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "visualize_grid",
+ "kind": 2,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "def visualize_grid(visdict, savepath=None, size=224, dim=1, return_gird=True):\n '''\n image range should be [0,1]\n dim: 2 for horizontal. 1 for vertical\n '''\n assert dim == 1 or dim==2\n grids = {}\n for key in visdict:\n _,_,h,w = visdict[key].shape\n if dim == 2:",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "end_list",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.utils.util",
+ "description": "modules.DECA.decalib.utils.util",
+ "peekOfCode": "end_list = np.array([17, 22, 27, 42, 48, 31, 36, 68], dtype = np.int32) - 1\ndef plot_kpts(image, kpts, color = 'r'):\n ''' Draw 68 key points\n Args: \n image: the input image\n kpt: (68, 3).\n '''\n if color == 'r':\n c = (255, 0, 0)\n elif color == 'g':",
+ "detail": "modules.DECA.decalib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "DECA",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.deca",
+ "description": "modules.DECA.decalib.deca",
+ "peekOfCode": "class DECA(nn.Module):\n def __init__(self, config=None, device='cuda'):\n super(DECA, self).__init__()\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device\n self.image_size = self.cfg.dataset.image_size\n self.uv_size = self.cfg.model.uv_size",
+ "detail": "modules.DECA.decalib.deca",
+ "documentation": {}
+ },
+ {
+ "label": "torch.backends.cudnn.benchmark",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.deca",
+ "description": "modules.DECA.decalib.deca",
+ "peekOfCode": "torch.backends.cudnn.benchmark = True\nclass DECA(nn.Module):\n def __init__(self, config=None, device='cuda'):\n super(DECA, self).__init__()\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device\n self.image_size = self.cfg.dataset.image_size",
+ "detail": "modules.DECA.decalib.deca",
+ "documentation": {}
+ },
+ {
+ "label": "Trainer",
+ "kind": 6,
+ "importPath": "modules.DECA.decalib.trainer",
+ "description": "modules.DECA.decalib.trainer",
+ "peekOfCode": "class Trainer(object):\n def __init__(self, model, config=None, device='cuda:0'):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device\n self.batch_size = self.cfg.dataset.batch_size\n self.image_size = self.cfg.dataset.image_size\n self.uv_size = self.cfg.model.uv_size",
+ "detail": "modules.DECA.decalib.trainer",
+ "documentation": {}
+ },
+ {
+ "label": "torch.backends.cudnn.benchmark",
+ "kind": 5,
+ "importPath": "modules.DECA.decalib.trainer",
+ "description": "modules.DECA.decalib.trainer",
+ "peekOfCode": "torch.backends.cudnn.benchmark = True\nfrom .utils import lossfunc\nfrom .datasets import build_datasets\nclass Trainer(object):\n def __init__(self, model, config=None, device='cuda:0'):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device",
+ "detail": "modules.DECA.decalib.trainer",
+ "documentation": {}
+ },
+ {
+ "label": "api_multi_deca",
+ "kind": 2,
+ "importPath": "modules.DECA.demos.api_multi_deca",
+ "description": "modules.DECA.demos.api_multi_deca",
+ "peekOfCode": "def api_multi_deca(\n savefolder,\n visfolder,\n inputpath,\n device='cuda',\n saveVis=True,\n saveMat=True,\n rasterizer_type='pytorch3d',\n face_detector='mtcnn',\n ):",
+ "detail": "modules.DECA.demos.api_multi_deca",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.DECA.demos.demo_multi_persons",
+ "description": "modules.DECA.demos.demo_multi_persons",
+ "peekOfCode": "def main(args):\n savefolder = args.savefolder\n device = args.device\n os.makedirs(savefolder, exist_ok=True)\n # load test images \n testdata = datasets.TestData(args.inputpath, iscrop=args.iscrop, face_detector=args.detector)\n # run DECA\n deca_cfg.model.use_tex = args.useTex\n deca_cfg.rasterizer_type = args.rasterizer_type\n deca = DECA(config = deca_cfg, device=device)",
+ "detail": "modules.DECA.demos.demo_multi_persons",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.DECA.demos.demo_reconstruct",
+ "description": "modules.DECA.demos.demo_reconstruct",
+ "peekOfCode": "def main(args):\n # if args.rasterizer_type != 'standard':\n # args.render_orig = False\n savefolder = args.savefolder\n device = args.device\n os.makedirs(savefolder, exist_ok=True)\n # os.makedirs(os.path.join(savefolder, 'vis'), exist_ok=True)\n # os.makedirs(os.path.join(savefolder, 'org'), exist_ok=True)\n # load test images \n testdata = datasets.TestData(args.inputpath, iscrop=args.iscrop, face_detector=args.detector)",
+ "detail": "modules.DECA.demos.demo_reconstruct",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.DECA.demos.demo_reconstruct_modify",
+ "description": "modules.DECA.demos.demo_reconstruct_modify",
+ "peekOfCode": "def main(args):\n # if args.rasterizer_type != 'standard':\n # args.render_orig = False\n savefolder = args.savefolder\n device = args.device\n os.makedirs(savefolder, exist_ok=True)\n # os.makedirs(os.path.join(savefolder, 'vis'), exist_ok=True)\n # os.makedirs(os.path.join(savefolder, 'org'), exist_ok=True)\n # load test images \n testdata = datasets.TestData(args.inputpath, iscrop=args.iscrop, face_detector=args.detector)",
+ "detail": "modules.DECA.demos.demo_reconstruct_modify",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.DECA.demos.demo_teaser",
+ "description": "modules.DECA.demos.demo_teaser",
+ "peekOfCode": "def main(args):\n savefolder = args.savefolder\n device = args.device\n os.makedirs(savefolder, exist_ok=True)\n # load test images \n testdata = datasets.TestData(args.inputpath, iscrop=args.iscrop, face_detector=args.detector)\n expdata = datasets.TestData(args.exp_path, iscrop=args.iscrop, face_detector=args.detector)\n # DECA\n deca_cfg.rasterizer_type = args.rasterizer_type\n deca = DECA(config=deca_cfg, device=device)",
+ "detail": "modules.DECA.demos.demo_teaser",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.DECA.demos.demo_transfer",
+ "description": "modules.DECA.demos.demo_transfer",
+ "peekOfCode": "def main(args):\n savefolder = args.savefolder\n device = args.device\n os.makedirs(savefolder, exist_ok=True)\n # load test images \n testdata = datasets.TestData(args.image_path, iscrop=args.iscrop, face_detector=args.detector)\n expdata = datasets.TestData(args.exp_path, iscrop=args.iscrop, face_detector=args.detector)\n # run DECA\n deca_cfg.model.use_tex = args.useTex\n deca_cfg.rasterizer_type = args.rasterizer_type",
+ "detail": "modules.DECA.demos.demo_transfer",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.DECA.main_train",
+ "description": "modules.DECA.main_train",
+ "peekOfCode": "def main(cfg):\n # creat folders \n os.makedirs(os.path.join(cfg.output_dir, cfg.train.log_dir), exist_ok=True)\n os.makedirs(os.path.join(cfg.output_dir, cfg.train.vis_dir), exist_ok=True)\n os.makedirs(os.path.join(cfg.output_dir, cfg.train.val_vis_dir), exist_ok=True)\n with open(os.path.join(cfg.output_dir, cfg.train.log_dir, 'full_config.yaml'), 'w') as f:\n yaml.dump(cfg, f, default_flow_style=False)\n shutil.copy(cfg.cfg_file, os.path.join(cfg.output_dir, 'config.yaml'))\n # cudnn related setting\n cudnn.benchmark = True",
+ "detail": "modules.DECA.main_train",
+ "documentation": {}
+ },
+ {
+ "label": "BU3DFE",
+ "kind": 6,
+ "importPath": "modules.MICA.datasets.creation.instances.bu3dfe",
+ "description": "modules.MICA.datasets.creation.instances.bu3dfe",
+ "peekOfCode": "class BU3DFE(Instance, ABC):\n def __init__(self):\n super(BU3DFE, self).__init__()\n self.dst = '/scratch/NFC/OnFlame/BU3DFE/'\n self.src = '/scratch/NFC/BU-3DFE/'\n def get_images(self):\n images = {}\n for actor in sorted(glob(self.get_src().replace('BU-3DFE', 'BU-3DFE_clean') + 'images/*')):\n images[Path(actor).name] = glob(f'{actor}/*.jpg')\n return images",
+ "detail": "modules.MICA.datasets.creation.instances.bu3dfe",
+ "documentation": {}
+ },
+ {
+ "label": "D3DFACS",
+ "kind": 6,
+ "importPath": "modules.MICA.datasets.creation.instances.d3dfacs",
+ "description": "modules.MICA.datasets.creation.instances.d3dfacs",
+ "peekOfCode": "class D3DFACS(Instance, ABC):\n def __init__(self):\n super(D3DFACS, self).__init__()\n self.dst = '/scratch/NFC/OnFlame/D3DFACS/'\n self.src = '/home/wzielonka/datasets/D3DFACS/'\n def get_images(self):\n images = {}\n for file in sorted(glob(self.get_src() + 'processed/images/*')):\n actor = Path(file).stem\n images[actor] = glob(f'{file}/*.jpg')",
+ "detail": "modules.MICA.datasets.creation.instances.d3dfacs",
+ "documentation": {}
+ },
+ {
+ "label": "FaceWarehouse",
+ "kind": 6,
+ "importPath": "modules.MICA.datasets.creation.instances.facewarehouse",
+ "description": "modules.MICA.datasets.creation.instances.facewarehouse",
+ "peekOfCode": "class FaceWarehouse(Instance, ABC):\n def __init__(self):\n super(FaceWarehouse, self).__init__()\n self.dst = '/scratch/NFC/OnFlame/FACEWAREHOUSE/'\n self.src = '/scratch/NFC/FaceWarehouse/'\n def get_images(self):\n images = {}\n for actor in sorted(glob(self.get_src() + 'Images/*')):\n images[Path(actor).stem] = glob(f'{actor}/*.png')\n return images",
+ "detail": "modules.MICA.datasets.creation.instances.facewarehouse",
+ "documentation": {}
+ },
+ {
+ "label": "Florence",
+ "kind": 6,
+ "importPath": "modules.MICA.datasets.creation.instances.florence",
+ "description": "modules.MICA.datasets.creation.instances.florence",
+ "peekOfCode": "class Florence(Instance, ABC):\n def __init__(self):\n super(Florence, self).__init__()\n self.dst = '/scratch/NFC/OnFlame/FLORENCE/'\n self.src = '/scratch/NFC/MICC_Florence/'\n def get_min_det_score(self):\n return 0.85\n def get_images(self):\n images = {}\n for actor in sorted(glob(self.get_src() + 'images/*')):",
+ "detail": "modules.MICA.datasets.creation.instances.florence",
+ "documentation": {}
+ },
+ {
+ "label": "FRGC",
+ "kind": 6,
+ "importPath": "modules.MICA.datasets.creation.instances.frgc",
+ "description": "modules.MICA.datasets.creation.instances.frgc",
+ "peekOfCode": "class FRGC(Instance, ABC):\n def __init__(self):\n super(FRGC, self).__init__()\n self.dst = '/scratch/NFC/OnFlame/FRGC/'\n self.src = '/scratch/NFC/FRGC_v2/'\n def get_images(self):\n images = {}\n for actor in sorted(glob(self.get_src() + 'images/*')):\n imgs = list(filter(lambda f: 'Spring2003range' not in f, glob(f'/{actor}/*/*.jpg')))\n images[Path(actor).name] = imgs",
+ "detail": "modules.MICA.datasets.creation.instances.frgc",
+ "documentation": {}
+ },
+ {
+ "label": "Instance",
+ "kind": 6,
+ "importPath": "modules.MICA.datasets.creation.instances.instance",
+ "description": "modules.MICA.datasets.creation.instances.instance",
+ "peekOfCode": "class Instance:\n def __init__(self):\n self.mount = '/home/wzielonka/Cluster/lustre'\n self.dst = 'empty'\n self.src = 'empty'\n self.device = 'cuda:0'\n self.actors = []\n self.use_mount = os.path.exists(self.mount)\n def get_dst(self):\n return self.dst if not self.use_mount else self.mount + self.dst",
+ "detail": "modules.MICA.datasets.creation.instances.instance",
+ "documentation": {}
+ },
+ {
+ "label": "LYHM",
+ "kind": 6,
+ "importPath": "modules.MICA.datasets.creation.instances.lyhm",
+ "description": "modules.MICA.datasets.creation.instances.lyhm",
+ "peekOfCode": "class LYHM(Instance, ABC):\n def __init__(self):\n super(LYHM, self).__init__()\n self.dst = '/scratch/NFC/MICA/LYHM/'\n self.src = '/scratch/NFC/LYHM/'\n def get_images(self):\n images = {}\n for actor in sorted(glob(self.get_src() + '/*')):\n images[Path(actor).name] = glob(f'/{actor}/*.png')\n return images",
+ "detail": "modules.MICA.datasets.creation.instances.lyhm",
+ "documentation": {}
+ },
+ {
+ "label": "ImageFile.LOAD_TRUNCATED_IMAGES",
+ "kind": 5,
+ "importPath": "modules.MICA.datasets.creation.instances.lyhm",
+ "description": "modules.MICA.datasets.creation.instances.lyhm",
+ "peekOfCode": "ImageFile.LOAD_TRUNCATED_IMAGES = True\nfrom pytorch3d.io import load_objs_as_meshes\nfrom pytorch3d.transforms import RotateAxisAngle\nfrom datasets.creation.instances.instance import Instance\nclass LYHM(Instance, ABC):\n def __init__(self):\n super(LYHM, self).__init__()\n self.dst = '/scratch/NFC/MICA/LYHM/'\n self.src = '/scratch/NFC/LYHM/'\n def get_images(self):",
+ "detail": "modules.MICA.datasets.creation.instances.lyhm",
+ "documentation": {}
+ },
+ {
+ "label": "PB4D",
+ "kind": 6,
+ "importPath": "modules.MICA.datasets.creation.instances.pb4d",
+ "description": "modules.MICA.datasets.creation.instances.pb4d",
+ "peekOfCode": "class PB4D(Instance, ABC):\n def __init__(self):\n super(PB4D, self).__init__()\n self.dst = '/scratch/NFC/OnFlame/BP4D/'\n self.src = '/scratch/NFC/BP4D/'\n def get_images(self):\n images = {}\n for actor in sorted(glob(self.get_src() + 'images/*')):\n imgs = sorted(glob(f'/{actor}/*.jpg'))\n indecies = np.random.choice(len(imgs), 100, replace=False)",
+ "detail": "modules.MICA.datasets.creation.instances.pb4d",
+ "documentation": {}
+ },
+ {
+ "label": "Stirling",
+ "kind": 6,
+ "importPath": "modules.MICA.datasets.creation.instances.stirling",
+ "description": "modules.MICA.datasets.creation.instances.stirling",
+ "peekOfCode": "class Stirling(Instance, ABC):\n def __init__(self):\n super(Stirling, self).__init__()\n self.dst = '/scratch/NFC/OnFlame/STIRLING/'\n self.src = '/scratch/NFC/Stirling/'\n def get_min_det_score(self):\n return 0.75\n def get_images(self):\n images = {}\n for file in sorted(glob(self.get_src() + 'images/Real_images__Subset_2D_FG2018/HQ/*')):",
+ "detail": "modules.MICA.datasets.creation.instances.stirling",
+ "documentation": {}
+ },
+ {
+ "label": "Generator",
+ "kind": 6,
+ "importPath": "modules.MICA.datasets.creation.generator",
+ "description": "modules.MICA.datasets.creation.generator",
+ "peekOfCode": "class Generator:\n def __init__(self, instances):\n self.instances: List[Instance] = instances\n self.ARCFACE = 'arcface_input'\n def copy(self):\n logger.info('Start copying...')\n for instance in tqdm(self.instances):\n payloads = [(instance, instance.get_images, 'images', instance.transform_path)]\n with Pool(processes=len(payloads)) as pool:\n for _ in tqdm(pool.imap_unordered(_copy, payloads), total=len(payloads)):",
+ "detail": "modules.MICA.datasets.creation.generator",
+ "documentation": {}
+ },
+ {
+ "label": "create_folders",
+ "kind": 2,
+ "importPath": "modules.MICA.datasets.creation.util",
+ "description": "modules.MICA.datasets.creation.util",
+ "peekOfCode": "def create_folders(folders):\n if not type(folders) is list:\n folders = folders.split('/')\n parents = '/'\n for folder in folders:\n parents = os.path.join(parents, folder)\n if os.path.exists(parents):\n continue\n Path(parents).mkdir(exist_ok=True)\ndef get_arcface_input(face, img):",
+ "detail": "modules.MICA.datasets.creation.util",
+ "documentation": {}
+ },
+ {
+ "label": "get_arcface_input",
+ "kind": 2,
+ "importPath": "modules.MICA.datasets.creation.util",
+ "description": "modules.MICA.datasets.creation.util",
+ "peekOfCode": "def get_arcface_input(face, img):\n aimg = face_align.norm_crop(img, landmark=face.kps)\n blob = cv2.dnn.blobFromImages([aimg], 1.0 / input_std, (112, 112), (input_mean, input_mean, input_mean), swapRB=True)\n return blob[0], aimg\ndef get_image(name, to_rgb=False):\n images_dir = osp.join(Path(__file__).parent.absolute(), '../images')\n ext_names = ['.jpg', '.png', '.jpeg']\n image_file = None\n for ext_name in ext_names:\n _image_file = osp.join(images_dir, \"%s%s\" % (name, ext_name))",
+ "detail": "modules.MICA.datasets.creation.util",
+ "documentation": {}
+ },
+ {
+ "label": "get_image",
+ "kind": 2,
+ "importPath": "modules.MICA.datasets.creation.util",
+ "description": "modules.MICA.datasets.creation.util",
+ "peekOfCode": "def get_image(name, to_rgb=False):\n images_dir = osp.join(Path(__file__).parent.absolute(), '../images')\n ext_names = ['.jpg', '.png', '.jpeg']\n image_file = None\n for ext_name in ext_names:\n _image_file = osp.join(images_dir, \"%s%s\" % (name, ext_name))\n if osp.exists(_image_file):\n image_file = _image_file\n break\n assert image_file is not None, '%s not found' % name",
+ "detail": "modules.MICA.datasets.creation.util",
+ "documentation": {}
+ },
+ {
+ "label": "dist",
+ "kind": 2,
+ "importPath": "modules.MICA.datasets.creation.util",
+ "description": "modules.MICA.datasets.creation.util",
+ "peekOfCode": "def dist(p1, p2):\n return math.sqrt(((p1[0] - p2[0]) ** 2) + ((p1[1] - p2[1]) ** 2))\ndef get_center(bboxes, img):\n img_center = img.shape[0] // 2, img.shape[1] // 2\n size = bboxes.shape[0]\n distance = np.Inf\n j = 0\n for i in range(size):\n x1, y1, x2, y2 = bboxes[i, 0:4]\n dx = abs(x2 - x1) / 2.0",
+ "detail": "modules.MICA.datasets.creation.util",
+ "documentation": {}
+ },
+ {
+ "label": "get_center",
+ "kind": 2,
+ "importPath": "modules.MICA.datasets.creation.util",
+ "description": "modules.MICA.datasets.creation.util",
+ "peekOfCode": "def get_center(bboxes, img):\n img_center = img.shape[0] // 2, img.shape[1] // 2\n size = bboxes.shape[0]\n distance = np.Inf\n j = 0\n for i in range(size):\n x1, y1, x2, y2 = bboxes[i, 0:4]\n dx = abs(x2 - x1) / 2.0\n dy = abs(y2 - y1) / 2.0\n current = dist((x1 + dx, y1 + dy), img_center)",
+ "detail": "modules.MICA.datasets.creation.util",
+ "documentation": {}
+ },
+ {
+ "label": "bbox2point",
+ "kind": 2,
+ "importPath": "modules.MICA.datasets.creation.util",
+ "description": "modules.MICA.datasets.creation.util",
+ "peekOfCode": "def bbox2point(left, right, top, bottom, type='bbox'):\n if type == 'kpt68':\n old_size = (right - left + bottom - top) / 2 * 1.1\n center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0])\n elif type == 'bbox':\n old_size = (right - left + bottom - top) / 2\n center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0 + old_size * 0.12])\n else:\n raise NotImplementedError\n return old_size, center",
+ "detail": "modules.MICA.datasets.creation.util",
+ "documentation": {}
+ },
+ {
+ "label": "input_mean",
+ "kind": 5,
+ "importPath": "modules.MICA.datasets.creation.util",
+ "description": "modules.MICA.datasets.creation.util",
+ "peekOfCode": "input_mean = 127.5\ninput_std = 127.5\ndef create_folders(folders):\n if not type(folders) is list:\n folders = folders.split('/')\n parents = '/'\n for folder in folders:\n parents = os.path.join(parents, folder)\n if os.path.exists(parents):\n continue",
+ "detail": "modules.MICA.datasets.creation.util",
+ "documentation": {}
+ },
+ {
+ "label": "input_std",
+ "kind": 5,
+ "importPath": "modules.MICA.datasets.creation.util",
+ "description": "modules.MICA.datasets.creation.util",
+ "peekOfCode": "input_std = 127.5\ndef create_folders(folders):\n if not type(folders) is list:\n folders = folders.split('/')\n parents = '/'\n for folder in folders:\n parents = os.path.join(parents, folder)\n if os.path.exists(parents):\n continue\n Path(parents).mkdir(exist_ok=True)",
+ "detail": "modules.MICA.datasets.creation.util",
+ "documentation": {}
+ },
+ {
+ "label": "BaseDataset",
+ "kind": 6,
+ "importPath": "modules.MICA.datasets.base",
+ "description": "modules.MICA.datasets.base",
+ "peekOfCode": "class BaseDataset(Dataset, ABC):\n def __init__(self, name, config, device, isEval):\n self.K = config.K\n self.isEval = isEval\n self.n_train = np.Inf\n self.imagepaths = []\n self.face_dict = {}\n self.name = name\n self.device = device\n self.min_max_K = 0",
+ "detail": "modules.MICA.datasets.base",
+ "documentation": {}
+ },
+ {
+ "label": "IBasicBlock",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.arcface",
+ "description": "modules.MICA.micalib.arcface",
+ "peekOfCode": "class IBasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None,\n groups=1, base_width=64, dilation=1):\n super(IBasicBlock, self).__init__()\n if groups != 1 or base_width != 64:\n raise ValueError('BasicBlock only supports groups=1 and base_width=64')\n if dilation > 1:\n raise NotImplementedError(\"Dilation > 1 not supported in BasicBlock\")\n self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05, )",
+ "detail": "modules.MICA.micalib.arcface",
+ "documentation": {}
+ },
+ {
+ "label": "IResNet",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.arcface",
+ "description": "modules.MICA.micalib.arcface",
+ "peekOfCode": "class IResNet(nn.Module):\n fc_scale = 7 * 7\n def __init__(self,\n block, layers, dropout=0, num_features=512, zero_init_residual=False,\n groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):\n super(IResNet, self).__init__()\n self.fp16 = fp16\n self.inplanes = 64\n self.dilation = 1\n self.block = block",
+ "detail": "modules.MICA.micalib.arcface",
+ "documentation": {}
+ },
+ {
+ "label": "Arcface",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.arcface",
+ "description": "modules.MICA.micalib.arcface",
+ "peekOfCode": "class Arcface(IResNet):\n def __init__(self, pretrained_path=None, **kwargs):\n super(Arcface, self).__init__(IBasicBlock, [3, 13, 30, 3], **kwargs)\n if pretrained_path is not None and os.path.exists(pretrained_path):\n logger.info(f'[Arcface] Initializing from insightface model from {pretrained_path}.')\n self.load_state_dict(torch.load(pretrained_path))\n self.freezer([self.layer1, self.layer2, self.layer3, self.conv1, self.bn1, self.prelu])\n def freezer(self, layers):\n for layer in layers:\n for block in layer.parameters():",
+ "detail": "modules.MICA.micalib.arcface",
+ "documentation": {}
+ },
+ {
+ "label": "conv3x3",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.arcface",
+ "description": "modules.MICA.micalib.arcface",
+ "peekOfCode": "def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes,\n out_planes,\n kernel_size=3,\n stride=stride,\n padding=dilation,\n groups=groups,\n bias=False,\n dilation=dilation)",
+ "detail": "modules.MICA.micalib.arcface",
+ "documentation": {}
+ },
+ {
+ "label": "conv1x1",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.arcface",
+ "description": "modules.MICA.micalib.arcface",
+ "peekOfCode": "def conv1x1(in_planes, out_planes, stride=1):\n \"\"\"1x1 convolution\"\"\"\n return nn.Conv2d(in_planes,\n out_planes,\n kernel_size=1,\n stride=stride,\n bias=False)\nclass IBasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None,",
+ "detail": "modules.MICA.micalib.arcface",
+ "documentation": {}
+ },
+ {
+ "label": "__all__",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.arcface",
+ "description": "modules.MICA.micalib.arcface",
+ "peekOfCode": "__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']\ndef conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes,\n out_planes,\n kernel_size=3,\n stride=stride,\n padding=dilation,\n groups=groups,\n bias=False,",
+ "detail": "modules.MICA.micalib.arcface",
+ "documentation": {}
+ },
+ {
+ "label": "BaseModel",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.base_model",
+ "description": "modules.MICA.micalib.base_model",
+ "peekOfCode": "class BaseModel(nn.Module):\n def __init__(self, config=None, device=None, tag=''):\n super(BaseModel, self).__init__()\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.tag = tag\n self.use_mask = self.cfg.train.use_mask\n self.device = device",
+ "detail": "modules.MICA.micalib.base_model",
+ "documentation": {}
+ },
+ {
+ "label": "BestModel",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.best_model",
+ "description": "modules.MICA.micalib.best_model",
+ "peekOfCode": "class BestModel:\n def __init__(self, trainer):\n self.average = np.Inf\n self.weighted_average = np.Inf\n self.smoothed_average = np.Inf\n self.smoothed_weighted_average = np.Inf\n self.running_average = np.Inf\n self.running_weighted_average = np.Inf\n self.now_mean = None\n self.trainer = trainer",
+ "detail": "modules.MICA.micalib.best_model",
+ "documentation": {}
+ },
+ {
+ "label": "get_cfg_defaults",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "def get_cfg_defaults():\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)\n return cfg.clone()\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--cfg', type=str, help='cfg file path', required=True)\n parser.add_argument('--test_dataset', type=str, help='Test dataset type', default='')\n parser.add_argument('--checkpoint', type=str, help='Checkpoint to load', default='')",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "update_cfg",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "def update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)\n return cfg.clone()\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--cfg', type=str, help='cfg file path', required=True)\n parser.add_argument('--test_dataset', type=str, help='Test dataset type', default='')\n parser.add_argument('--checkpoint', type=str, help='Checkpoint to load', default='')\n args = parser.parse_args()\n print(args, end='\\n\\n')",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "parse_args",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--cfg', type=str, help='cfg file path', required=True)\n parser.add_argument('--test_dataset', type=str, help='Test dataset type', default='')\n parser.add_argument('--checkpoint', type=str, help='Checkpoint to load', default='')\n args = parser.parse_args()\n print(args, end='\\n\\n')\n cfg = get_cfg_defaults()\n if args.cfg is not None:\n cfg_file = args.cfg",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg = CN()\nabs_mica_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../..','models/models_MICA'))\n# abs_mica_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))\ncfg.mica_dir = abs_mica_dir\ncfg.device = 'cuda'\ncfg.device_id = '0'\ncfg.pretrained_model_path = os.path.join(cfg.mica_dir, 'pretrained', 'mica.tar')\ncfg.output_dir = ''\ncfg.FLAME_masks=os.path.join(cfg.mica_dir, 'FLAME2020/FLAME_masks/FLAME_masks.pkl')\ncfg.generic_model=os.path.join(cfg.mica_dir, 'FLAME2020/generic_model.pkl')",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "abs_mica_dir",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "abs_mica_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../..','models/models_MICA'))\n# abs_mica_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))\ncfg.mica_dir = abs_mica_dir\ncfg.device = 'cuda'\ncfg.device_id = '0'\ncfg.pretrained_model_path = os.path.join(cfg.mica_dir, 'pretrained', 'mica.tar')\ncfg.output_dir = ''\ncfg.FLAME_masks=os.path.join(cfg.mica_dir, 'FLAME2020/FLAME_masks/FLAME_masks.pkl')\ncfg.generic_model=os.path.join(cfg.mica_dir, 'FLAME2020/generic_model.pkl')\n# ---------------------------------------------------------------------------- #",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.mica_dir",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.mica_dir = abs_mica_dir\ncfg.device = 'cuda'\ncfg.device_id = '0'\ncfg.pretrained_model_path = os.path.join(cfg.mica_dir, 'pretrained', 'mica.tar')\ncfg.output_dir = ''\ncfg.FLAME_masks=os.path.join(cfg.mica_dir, 'FLAME2020/FLAME_masks/FLAME_masks.pkl')\ncfg.generic_model=os.path.join(cfg.mica_dir, 'FLAME2020/generic_model.pkl')\n# ---------------------------------------------------------------------------- #\n# Options for Face model\n# ---------------------------------------------------------------------------- #",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.device",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.device = 'cuda'\ncfg.device_id = '0'\ncfg.pretrained_model_path = os.path.join(cfg.mica_dir, 'pretrained', 'mica.tar')\ncfg.output_dir = ''\ncfg.FLAME_masks=os.path.join(cfg.mica_dir, 'FLAME2020/FLAME_masks/FLAME_masks.pkl')\ncfg.generic_model=os.path.join(cfg.mica_dir, 'FLAME2020/generic_model.pkl')\n# ---------------------------------------------------------------------------- #\n# Options for Face model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.device_id",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
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+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.running_average",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.running_average = 7\n# ---------------------------------------------------------------------------- #\n# Options for training\n# ---------------------------------------------------------------------------- #\ncfg.train = CN()\ncfg.train.use_mask = False\ncfg.train.max_epochs = 50\ncfg.train.max_steps = 100000\ncfg.train.lr = 1e-4\ncfg.train.arcface_lr = 1e-3",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train = CN()\ncfg.train.use_mask = False\ncfg.train.max_epochs = 50\ncfg.train.max_steps = 100000\ncfg.train.lr = 1e-4\ncfg.train.arcface_lr = 1e-3\ncfg.train.weight_decay = 0.0\ncfg.train.lr_update_step = 100000000\ncfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.use_mask",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.use_mask = False\ncfg.train.max_epochs = 50\ncfg.train.max_steps = 100000\ncfg.train.lr = 1e-4\ncfg.train.arcface_lr = 1e-3\ncfg.train.weight_decay = 0.0\ncfg.train.lr_update_step = 100000000\ncfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.max_epochs",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.max_epochs = 50\ncfg.train.max_steps = 100000\ncfg.train.lr = 1e-4\ncfg.train.arcface_lr = 1e-3\ncfg.train.weight_decay = 0.0\ncfg.train.lr_update_step = 100000000\ncfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.max_steps",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.max_steps = 100000\ncfg.train.lr = 1e-4\ncfg.train.arcface_lr = 1e-3\ncfg.train.weight_decay = 0.0\ncfg.train.lr_update_step = 100000000\ncfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.lr",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.lr = 1e-4\ncfg.train.arcface_lr = 1e-3\ncfg.train.weight_decay = 0.0\ncfg.train.lr_update_step = 100000000\ncfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 1000",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.arcface_lr",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.arcface_lr = 1e-3\ncfg.train.weight_decay = 0.0\ncfg.train.lr_update_step = 100000000\ncfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 1000\ncfg.train.checkpoint_epochs_steps = 2",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.weight_decay",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.weight_decay = 0.0\ncfg.train.lr_update_step = 100000000\ncfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 1000\ncfg.train.checkpoint_epochs_steps = 2\ncfg.train.val_steps = 1000",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.lr_update_step",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.lr_update_step = 100000000\ncfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 1000\ncfg.train.checkpoint_epochs_steps = 2\ncfg.train.val_steps = 1000\ncfg.train.val_vis_dir = 'val_images'",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.log_dir",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.log_dir = 'logs'\ncfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 1000\ncfg.train.checkpoint_epochs_steps = 2\ncfg.train.val_steps = 1000\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.log_steps",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.log_steps = 10\ncfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 1000\ncfg.train.checkpoint_epochs_steps = 2\ncfg.train.val_steps = 1000\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.reset_optimizer = False",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.vis_dir",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.vis_dir = 'train_images'\ncfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 1000\ncfg.train.checkpoint_epochs_steps = 2\ncfg.train.val_steps = 1000\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.reset_optimizer = False\ncfg.train.val_save_img = 5000",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.vis_steps",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.vis_steps = 200\ncfg.train.write_summary = True\ncfg.train.checkpoint_steps = 1000\ncfg.train.checkpoint_epochs_steps = 2\ncfg.train.val_steps = 1000\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.reset_optimizer = False\ncfg.train.val_save_img = 5000\ncfg.test_dataset = 'now'",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.write_summary",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.write_summary = True\ncfg.train.checkpoint_steps = 1000\ncfg.train.checkpoint_epochs_steps = 2\ncfg.train.val_steps = 1000\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.reset_optimizer = False\ncfg.train.val_save_img = 5000\ncfg.test_dataset = 'now'\ndef get_cfg_defaults():",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.checkpoint_steps",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.checkpoint_steps = 1000\ncfg.train.checkpoint_epochs_steps = 2\ncfg.train.val_steps = 1000\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.reset_optimizer = False\ncfg.train.val_save_img = 5000\ncfg.test_dataset = 'now'\ndef get_cfg_defaults():\n return cfg.clone()",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.checkpoint_epochs_steps",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.checkpoint_epochs_steps = 2\ncfg.train.val_steps = 1000\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.reset_optimizer = False\ncfg.train.val_save_img = 5000\ncfg.test_dataset = 'now'\ndef get_cfg_defaults():\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.val_steps",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.val_steps = 1000\ncfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.reset_optimizer = False\ncfg.train.val_save_img = 5000\ncfg.test_dataset = 'now'\ndef get_cfg_defaults():\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.val_vis_dir",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.val_vis_dir = 'val_images'\ncfg.train.eval_steps = 5000\ncfg.train.reset_optimizer = False\ncfg.train.val_save_img = 5000\ncfg.test_dataset = 'now'\ndef get_cfg_defaults():\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)\n return cfg.clone()",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.eval_steps",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.eval_steps = 5000\ncfg.train.reset_optimizer = False\ncfg.train.val_save_img = 5000\ncfg.test_dataset = 'now'\ndef get_cfg_defaults():\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)\n return cfg.clone()\ndef parse_args():",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.reset_optimizer",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.reset_optimizer = False\ncfg.train.val_save_img = 5000\ncfg.test_dataset = 'now'\ndef get_cfg_defaults():\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)\n return cfg.clone()\ndef parse_args():\n parser = argparse.ArgumentParser()",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.train.val_save_img",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.train.val_save_img = 5000\ncfg.test_dataset = 'now'\ndef get_cfg_defaults():\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)\n return cfg.clone()\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--cfg', type=str, help='cfg file path', required=True)",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.test_dataset",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.config",
+ "description": "modules.MICA.micalib.config",
+ "peekOfCode": "cfg.test_dataset = 'now'\ndef get_cfg_defaults():\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n cfg.merge_from_file(cfg_file)\n return cfg.clone()\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--cfg', type=str, help='cfg file path', required=True)\n parser.add_argument('--test_dataset', type=str, help='Test dataset type', default='')",
+ "detail": "modules.MICA.micalib.config",
+ "documentation": {}
+ },
+ {
+ "label": "Struct",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.flame",
+ "description": "modules.MICA.micalib.flame",
+ "peekOfCode": "class Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)\nclass FLAME(nn.Module):\n \"\"\"\n borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py\n Given flame parameters this class generates a differentiable FLAME function\n which outputs the a mesh and 2D/3D facial landmarks\n \"\"\"",
+ "detail": "modules.MICA.micalib.flame",
+ "documentation": {}
+ },
+ {
+ "label": "FLAME",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.flame",
+ "description": "modules.MICA.micalib.flame",
+ "peekOfCode": "class FLAME(nn.Module):\n \"\"\"\n borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py\n Given flame parameters this class generates a differentiable FLAME function\n which outputs the a mesh and 2D/3D facial landmarks\n \"\"\"\n def __init__(self, config, optimize_basis=False):\n super(FLAME, self).__init__()\n loguru.logger.info(\"[FLAME] creating the FLAME Decoder\")\n with open(config.flame_model_path, 'rb') as f:",
+ "detail": "modules.MICA.micalib.flame",
+ "documentation": {}
+ },
+ {
+ "label": "to_tensor",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.flame",
+ "description": "modules.MICA.micalib.flame",
+ "peekOfCode": "def to_tensor(array, dtype=torch.float32):\n if 'torch.tensor' not in str(type(array)):\n return torch.tensor(array, dtype=dtype)\ndef to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():",
+ "detail": "modules.MICA.micalib.flame",
+ "documentation": {}
+ },
+ {
+ "label": "to_np",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.flame",
+ "description": "modules.MICA.micalib.flame",
+ "peekOfCode": "def to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)\nclass FLAME(nn.Module):\n \"\"\"",
+ "detail": "modules.MICA.micalib.flame",
+ "documentation": {}
+ },
+ {
+ "label": "MappingNetwork",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.generator",
+ "description": "modules.MICA.micalib.generator",
+ "peekOfCode": "class MappingNetwork(nn.Module):\n def __init__(self, z_dim, map_hidden_dim, map_output_dim, hidden=2):\n super().__init__()\n if hidden > 5:\n self.skips = [int(hidden / 2)]\n else:\n self.skips = []\n self.network = nn.ModuleList(\n [nn.Linear(z_dim, map_hidden_dim)] +\n [nn.Linear(map_hidden_dim, map_hidden_dim) if i not in self.skips else",
+ "detail": "modules.MICA.micalib.generator",
+ "documentation": {}
+ },
+ {
+ "label": "Generator",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.generator",
+ "description": "modules.MICA.micalib.generator",
+ "peekOfCode": "class Generator(nn.Module):\n def __init__(self, z_dim, map_hidden_dim, map_output_dim, hidden, model_cfg, device, regress=True):\n super().__init__()\n self.device = device\n self.cfg = model_cfg\n self.regress = regress\n if self.regress:\n self.regressor = MappingNetwork(z_dim, map_hidden_dim, map_output_dim, hidden).to(self.device)\n self.generator = FLAME(model_cfg).to(self.device)\n def forward(self, arcface):",
+ "detail": "modules.MICA.micalib.generator",
+ "documentation": {}
+ },
+ {
+ "label": "kaiming_leaky_init",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.generator",
+ "description": "modules.MICA.micalib.generator",
+ "peekOfCode": "def kaiming_leaky_init(m):\n classname = m.__class__.__name__\n if classname.find('Linear') != -1:\n torch.nn.init.kaiming_normal_(m.weight, a=0.2, mode='fan_in', nonlinearity='leaky_relu')\nclass MappingNetwork(nn.Module):\n def __init__(self, z_dim, map_hidden_dim, map_output_dim, hidden=2):\n super().__init__()\n if hidden > 5:\n self.skips = [int(hidden / 2)]\n else:",
+ "detail": "modules.MICA.micalib.generator",
+ "documentation": {}
+ },
+ {
+ "label": "rot_mat_to_euler",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.lbs",
+ "description": "modules.MICA.micalib.lbs",
+ "peekOfCode": "def rot_mat_to_euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +\n rot_mats[:, 1, 0] * rot_mats[:, 1, 0])\n return torch.atan2(-rot_mats[:, 2, 0], sy)\ndef find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx,\n dynamic_lmk_b_coords,\n neck_kin_chain, dtype=torch.float32):\n ''' Compute the faces, barycentric coordinates for the dynamic landmarks",
+ "detail": "modules.MICA.micalib.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "find_dynamic_lmk_idx_and_bcoords",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.lbs",
+ "description": "modules.MICA.micalib.lbs",
+ "peekOfCode": "def find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx,\n dynamic_lmk_b_coords,\n neck_kin_chain, dtype=torch.float32):\n ''' Compute the faces, barycentric coordinates for the dynamic landmarks\n To do so, we first compute the rotation of the neck around the y-axis\n and then use a pre-computed look-up table to find the faces and the\n barycentric coordinates that will be used.\n Special thanks to Soubhik Sanyal (soubhik.sanyal@tuebingen.mpg.de)\n for providing the original TensorFlow implementation and for the LUT.\n Parameters",
+ "detail": "modules.MICA.micalib.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "vertices2landmarks",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.lbs",
+ "description": "modules.MICA.micalib.lbs",
+ "peekOfCode": "def vertices2landmarks(vertices, faces, lmk_faces_idx, lmk_bary_coords):\n ''' Calculates landmarks by barycentric interpolation\n Parameters\n ----------\n vertices: torch.tensor BxVx3, dtype = torch.float32\n The tensor of input vertices\n faces: torch.tensor Fx3, dtype = torch.long\n The faces of the mesh\n lmk_faces_idx: torch.tensor L, dtype = torch.long\n The tensor with the indices of the faces used to calculate the",
+ "detail": "modules.MICA.micalib.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "lbs",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.lbs",
+ "description": "modules.MICA.micalib.lbs",
+ "peekOfCode": "def lbs(betas, pose, v_template, shapedirs, posedirs, J_regressor, parents,\n lbs_weights, pose2rot=True, dtype=torch.float32):\n ''' Performs Linear Blend Skinning with the given shape and pose parameters\n Parameters\n ----------\n betas : torch.tensor BxNB\n The tensor of shape parameters\n pose : torch.tensor Bx(J + 1) * 3\n The pose parameters in axis-angle format\n v_template torch.tensor BxVx3",
+ "detail": "modules.MICA.micalib.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "vertices2joints",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.lbs",
+ "description": "modules.MICA.micalib.lbs",
+ "peekOfCode": "def vertices2joints(J_regressor, vertices):\n ''' Calculates the 3D joint locations from the vertices\n Parameters\n ----------\n J_regressor : torch.tensor JxV\n The regressor array that is used to calculate the joints from the\n position of the vertices\n vertices : torch.tensor BxVx3\n The tensor of mesh vertices\n Returns",
+ "detail": "modules.MICA.micalib.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "blend_shapes",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.lbs",
+ "description": "modules.MICA.micalib.lbs",
+ "peekOfCode": "def blend_shapes(betas, shape_disps):\n ''' Calculates the per vertex displacement due to the blend shapes\n Parameters\n ----------\n betas : torch.tensor Bx(num_betas)\n Blend shape coefficients\n shape_disps: torch.tensor Vx3x(num_betas)\n Blend shapes\n Returns\n -------",
+ "detail": "modules.MICA.micalib.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "batch_rodrigues",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.lbs",
+ "description": "modules.MICA.micalib.lbs",
+ "peekOfCode": "def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):\n ''' Calculates the rotation matrices for a batch of rotation vectors\n Parameters\n ----------\n rot_vecs: torch.tensor Nx3\n array of N axis-angle vectors\n Returns\n -------\n R: torch.tensor Nx3x3\n The rotation matrices for the given axis-angle parameters",
+ "detail": "modules.MICA.micalib.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "transform_mat",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.lbs",
+ "description": "modules.MICA.micalib.lbs",
+ "peekOfCode": "def transform_mat(R, t):\n ''' Creates a batch of transformation matrices\n Args:\n - R: Bx3x3 array of a batch of rotation matrices\n - t: Bx3x1 array of a batch of translation vectors\n Returns:\n - T: Bx4x4 Transformation matrix\n '''\n # No padding left or right, only add an extra row\n return torch.cat([F.pad(R, [0, 0, 0, 1]),",
+ "detail": "modules.MICA.micalib.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "batch_rigid_transform",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.lbs",
+ "description": "modules.MICA.micalib.lbs",
+ "peekOfCode": "def batch_rigid_transform(rot_mats, joints, parents, dtype=torch.float32):\n \"\"\"\n Applies a batch of rigid transformations to the joints\n Parameters\n ----------\n rot_mats : torch.tensor BxNx3x3\n Tensor of rotation matrices\n joints : torch.tensor BxNx3\n Locations of joints\n parents : torch.tensor BxN",
+ "detail": "modules.MICA.micalib.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "Struct",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.masking",
+ "description": "modules.MICA.micalib.masking",
+ "peekOfCode": "class Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)\nimport os\n# FLAME_masks=os.path.join(os.path.dirname(__file__),'..','./data/FLAME2020/FLAME_masks/FLAME_masks.pkl')\n# generic_model=os.path.join(os.path.dirname(__file__),'..','./data/FLAME2020/generic_model.pkl')\n# FLAME_masks=os.path.join(os.path.dirname(__file__),'../../../..','models/models_MICA/FLAME2020/FLAME_masks.pkl')\n# generic_model=os.path.join(os.path.dirname(__file__),'../../../..','models/models_MICA/FLAME2020/generic_model.pkl')\nclass Masking(nn.Module):",
+ "detail": "modules.MICA.micalib.masking",
+ "documentation": {}
+ },
+ {
+ "label": "Masking",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.masking",
+ "description": "modules.MICA.micalib.masking",
+ "peekOfCode": "class Masking(nn.Module):\n def __init__(self, config):\n super(Masking, self).__init__()\n with open(config.FLAME_masks, 'rb') as f:\n ss = pickle.load(f, encoding='latin1')\n self.masks = Struct(**ss)\n with open(config.generic_model, 'rb') as f:\n ss = pickle.load(f, encoding='latin1')\n flame_model = Struct(**ss)\n self.masked_faces = None",
+ "detail": "modules.MICA.micalib.masking",
+ "documentation": {}
+ },
+ {
+ "label": "to_tensor",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.masking",
+ "description": "modules.MICA.micalib.masking",
+ "peekOfCode": "def to_tensor(array, dtype=torch.float32):\n if 'torch.tensor' not in str(type(array)):\n return torch.tensor(array, dtype=dtype)\ndef to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():",
+ "detail": "modules.MICA.micalib.masking",
+ "documentation": {}
+ },
+ {
+ "label": "to_np",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.masking",
+ "description": "modules.MICA.micalib.masking",
+ "peekOfCode": "def to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)\nimport os\n# FLAME_masks=os.path.join(os.path.dirname(__file__),'..','./data/FLAME2020/FLAME_masks/FLAME_masks.pkl')",
+ "detail": "modules.MICA.micalib.masking",
+ "documentation": {}
+ },
+ {
+ "label": "MICA",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.mica",
+ "description": "modules.MICA.micalib.mica",
+ "peekOfCode": "class MICA(BaseModel):\n def __init__(self, config=None, device=None, tag='MICA'):\n super(MICA, self).__init__(config, device, tag)\n self.initialize()\n def create_model(self, model_cfg):\n mapping_layers = model_cfg.mapping_layers\n pretrained_path = None\n if not model_cfg.use_pretrained:\n pretrained_path = model_cfg.arcface_pretrained_model\n self.arcface = Arcface(pretrained_path=pretrained_path).to(self.device)",
+ "detail": "modules.MICA.micalib.mica",
+ "documentation": {}
+ },
+ {
+ "label": "MeshShapeRenderer",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.renderer",
+ "description": "modules.MICA.micalib.renderer",
+ "peekOfCode": "class MeshShapeRenderer(nn.Module):\n def __init__(self, obj_filename):\n super().__init__()\n verts, faces, aux = load_obj(obj_filename)\n faces = faces.verts_idx[None, ...].cuda()\n self.register_buffer('faces', faces)\n R, T = look_at_view_transform(2.7, 10.0, 10.0)\n self.cameras = FoVPerspectiveCameras(device='cuda:0', R=R, T=T, fov=6)\n raster_settings = RasterizationSettings(\n image_size=512,",
+ "detail": "modules.MICA.micalib.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "Tester",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.tester",
+ "description": "modules.MICA.micalib.tester",
+ "peekOfCode": "class Tester(object):\n def __init__(self, nfc_model, config=None, device=None):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device\n self.batch_size = self.cfg.dataset.batch_size\n self.K = self.cfg.dataset.K\n self.render_mesh = True",
+ "detail": "modules.MICA.micalib.tester",
+ "documentation": {}
+ },
+ {
+ "label": "input_mean",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.tester",
+ "description": "modules.MICA.micalib.tester",
+ "peekOfCode": "input_mean = 127.5\ninput_std = 127.5\nNOW_SCANS = '/home/wzielonka/datasets/NoWDataset/final_release_version/scans/'\nNOW_PICTURES = '/home/wzielonka/datasets/NoWDataset/final_release_version/iphone_pictures/'\nNOW_BBOX = '/home/wzielonka/datasets/NoWDataset/final_release_version/detected_face/'\nSTIRLING_PICTURES = '/home/wzielonka/datasets/Stirling/images/'\nclass Tester(object):\n def __init__(self, nfc_model, config=None, device=None):\n if config is None:\n self.cfg = cfg",
+ "detail": "modules.MICA.micalib.tester",
+ "documentation": {}
+ },
+ {
+ "label": "input_std",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.tester",
+ "description": "modules.MICA.micalib.tester",
+ "peekOfCode": "input_std = 127.5\nNOW_SCANS = '/home/wzielonka/datasets/NoWDataset/final_release_version/scans/'\nNOW_PICTURES = '/home/wzielonka/datasets/NoWDataset/final_release_version/iphone_pictures/'\nNOW_BBOX = '/home/wzielonka/datasets/NoWDataset/final_release_version/detected_face/'\nSTIRLING_PICTURES = '/home/wzielonka/datasets/Stirling/images/'\nclass Tester(object):\n def __init__(self, nfc_model, config=None, device=None):\n if config is None:\n self.cfg = cfg\n else:",
+ "detail": "modules.MICA.micalib.tester",
+ "documentation": {}
+ },
+ {
+ "label": "NOW_SCANS",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.tester",
+ "description": "modules.MICA.micalib.tester",
+ "peekOfCode": "NOW_SCANS = '/home/wzielonka/datasets/NoWDataset/final_release_version/scans/'\nNOW_PICTURES = '/home/wzielonka/datasets/NoWDataset/final_release_version/iphone_pictures/'\nNOW_BBOX = '/home/wzielonka/datasets/NoWDataset/final_release_version/detected_face/'\nSTIRLING_PICTURES = '/home/wzielonka/datasets/Stirling/images/'\nclass Tester(object):\n def __init__(self, nfc_model, config=None, device=None):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config",
+ "detail": "modules.MICA.micalib.tester",
+ "documentation": {}
+ },
+ {
+ "label": "NOW_PICTURES",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.tester",
+ "description": "modules.MICA.micalib.tester",
+ "peekOfCode": "NOW_PICTURES = '/home/wzielonka/datasets/NoWDataset/final_release_version/iphone_pictures/'\nNOW_BBOX = '/home/wzielonka/datasets/NoWDataset/final_release_version/detected_face/'\nSTIRLING_PICTURES = '/home/wzielonka/datasets/Stirling/images/'\nclass Tester(object):\n def __init__(self, nfc_model, config=None, device=None):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device",
+ "detail": "modules.MICA.micalib.tester",
+ "documentation": {}
+ },
+ {
+ "label": "NOW_BBOX",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.tester",
+ "description": "modules.MICA.micalib.tester",
+ "peekOfCode": "NOW_BBOX = '/home/wzielonka/datasets/NoWDataset/final_release_version/detected_face/'\nSTIRLING_PICTURES = '/home/wzielonka/datasets/Stirling/images/'\nclass Tester(object):\n def __init__(self, nfc_model, config=None, device=None):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device\n self.batch_size = self.cfg.dataset.batch_size",
+ "detail": "modules.MICA.micalib.tester",
+ "documentation": {}
+ },
+ {
+ "label": "STIRLING_PICTURES",
+ "kind": 5,
+ "importPath": "modules.MICA.micalib.tester",
+ "description": "modules.MICA.micalib.tester",
+ "peekOfCode": "STIRLING_PICTURES = '/home/wzielonka/datasets/Stirling/images/'\nclass Tester(object):\n def __init__(self, nfc_model, config=None, device=None):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device\n self.batch_size = self.cfg.dataset.batch_size\n self.K = self.cfg.dataset.K",
+ "detail": "modules.MICA.micalib.tester",
+ "documentation": {}
+ },
+ {
+ "label": "Trainer",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.trainer",
+ "description": "modules.MICA.micalib.trainer",
+ "peekOfCode": "class Trainer(object):\n def __init__(self, nfc_model, config=None, device=None):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n logger.add(os.path.join(self.cfg.output_dir, self.cfg.train.log_dir, 'train.log'))\n self.device = device\n self.batch_size = self.cfg.dataset.batch_size\n self.K = self.cfg.dataset.K",
+ "detail": "modules.MICA.micalib.trainer",
+ "documentation": {}
+ },
+ {
+ "label": "print_info",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.trainer",
+ "description": "modules.MICA.micalib.trainer",
+ "peekOfCode": "def print_info(rank):\n props = torch.cuda.get_device_properties(rank)\n logger.info(f'[INFO] {torch.cuda.get_device_name(rank)}')\n logger.info(f'[INFO] Rank: {str(rank)}')\n logger.info(f'[INFO] Memory: {round(props.total_memory / 1024 ** 3, 1)} GB')\n logger.info(f'[INFO] Allocated: {round(torch.cuda.memory_allocated(rank) / 1024 ** 3, 1)} GB')\n logger.info(f'[INFO] Cached: {round(torch.cuda.memory_reserved(rank) / 1024 ** 3, 1)} GB')\ndef seed_worker(worker_id):\n worker_seed = torch.initial_seed() % 2 ** 32\n np.random.seed(worker_seed)",
+ "detail": "modules.MICA.micalib.trainer",
+ "documentation": {}
+ },
+ {
+ "label": "seed_worker",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.trainer",
+ "description": "modules.MICA.micalib.trainer",
+ "peekOfCode": "def seed_worker(worker_id):\n worker_seed = torch.initial_seed() % 2 ** 32\n np.random.seed(worker_seed)\n random.seed(worker_seed)\nclass Trainer(object):\n def __init__(self, nfc_model, config=None, device=None):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config",
+ "detail": "modules.MICA.micalib.trainer",
+ "documentation": {}
+ },
+ {
+ "label": "find_model_using_name",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.util",
+ "description": "modules.MICA.micalib.util",
+ "peekOfCode": "def find_model_using_name(model_dir, model_name):\n # adapted from pix2pix framework: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/__init__.py#L25\n # import \"model_dir/modelname.py\"\n model_filename = model_dir + \".\" + model_name\n modellib = importlib.import_module(model_filename, package=model_dir)\n # In the file, the class called ModelName() will\n # be instantiated. It has to be a subclass of BaseModel,\n # and it is case-insensitive.\n model = None\n target_model_name = model_name.replace('_', '')",
+ "detail": "modules.MICA.micalib.util",
+ "documentation": {}
+ },
+ {
+ "label": "visualize_grid",
+ "kind": 2,
+ "importPath": "modules.MICA.micalib.util",
+ "description": "modules.MICA.micalib.util",
+ "peekOfCode": "def visualize_grid(visdict, savepath=None, size=224, dim=1, return_gird=True):\n '''\n image range should be [0,1]\n dim: 2 for horizontal. 1 for vertical\n '''\n assert dim == 1 or dim == 2\n grids = {}\n for key in visdict:\n b, c, h, w = visdict[key].shape\n if dim == 2:",
+ "detail": "modules.MICA.micalib.util",
+ "documentation": {}
+ },
+ {
+ "label": "Validator",
+ "kind": 6,
+ "importPath": "modules.MICA.micalib.validator",
+ "description": "modules.MICA.micalib.validator",
+ "peekOfCode": "class Validator(object):\n def __init__(self, trainer):\n self.trainer = trainer\n self.device = self.trainer.device\n self.nfc = self.trainer.nfc\n self.cfg = deepcopy(self.trainer.cfg)\n self.device = trainer.device\n # Create a separate instance only for predictions\n # nfc = util.find_model_using_name(model_dir='nfclib.models', model_name=self.cfg.model.name)(self.cfg, self.device)\n # self.tester = Tester(nfc, self.cfg, self.device)",
+ "detail": "modules.MICA.micalib.validator",
+ "documentation": {}
+ },
+ {
+ "label": "test",
+ "kind": 2,
+ "importPath": "modules.MICA.testing.now.now",
+ "description": "modules.MICA.testing.now.now",
+ "peekOfCode": "def test():\n global experiments\n if len(experiments) == 0:\n experiments = list(filter(lambda f: 'condor' not in f, os.listdir('../../output/')))\n os.system('rm -rf logs')\n os.system('rm -rf jobs')\n os.makedirs('logs', exist_ok=True)\n os.makedirs('jobs', exist_ok=True)\n for experiment in sorted(experiments):\n print(f'Testing {experiment}')",
+ "detail": "modules.MICA.testing.now.now",
+ "documentation": {}
+ },
+ {
+ "label": "logs",
+ "kind": 5,
+ "importPath": "modules.MICA.testing.now.now",
+ "description": "modules.MICA.testing.now.now",
+ "peekOfCode": "logs = '/home/wzielonka/projects/MICA/testing/now/logs/'\njobs = '/home/wzielonka/projects/MICA/testing/now/jobs/'\nroot = '/home/wzielonka/projects/MICA/output/'\nexperiments = []\ndef test():\n global experiments\n if len(experiments) == 0:\n experiments = list(filter(lambda f: 'condor' not in f, os.listdir('../../output/')))\n os.system('rm -rf logs')\n os.system('rm -rf jobs')",
+ "detail": "modules.MICA.testing.now.now",
+ "documentation": {}
+ },
+ {
+ "label": "jobs",
+ "kind": 5,
+ "importPath": "modules.MICA.testing.now.now",
+ "description": "modules.MICA.testing.now.now",
+ "peekOfCode": "jobs = '/home/wzielonka/projects/MICA/testing/now/jobs/'\nroot = '/home/wzielonka/projects/MICA/output/'\nexperiments = []\ndef test():\n global experiments\n if len(experiments) == 0:\n experiments = list(filter(lambda f: 'condor' not in f, os.listdir('../../output/')))\n os.system('rm -rf logs')\n os.system('rm -rf jobs')\n os.makedirs('logs', exist_ok=True)",
+ "detail": "modules.MICA.testing.now.now",
+ "documentation": {}
+ },
+ {
+ "label": "root",
+ "kind": 5,
+ "importPath": "modules.MICA.testing.now.now",
+ "description": "modules.MICA.testing.now.now",
+ "peekOfCode": "root = '/home/wzielonka/projects/MICA/output/'\nexperiments = []\ndef test():\n global experiments\n if len(experiments) == 0:\n experiments = list(filter(lambda f: 'condor' not in f, os.listdir('../../output/')))\n os.system('rm -rf logs')\n os.system('rm -rf jobs')\n os.makedirs('logs', exist_ok=True)\n os.makedirs('jobs', exist_ok=True)",
+ "detail": "modules.MICA.testing.now.now",
+ "documentation": {}
+ },
+ {
+ "label": "experiments",
+ "kind": 5,
+ "importPath": "modules.MICA.testing.now.now",
+ "description": "modules.MICA.testing.now.now",
+ "peekOfCode": "experiments = []\ndef test():\n global experiments\n if len(experiments) == 0:\n experiments = list(filter(lambda f: 'condor' not in f, os.listdir('../../output/')))\n os.system('rm -rf logs')\n os.system('rm -rf jobs')\n os.makedirs('logs', exist_ok=True)\n os.makedirs('jobs', exist_ok=True)\n for experiment in sorted(experiments):",
+ "detail": "modules.MICA.testing.now.now",
+ "documentation": {}
+ },
+ {
+ "label": "test",
+ "kind": 2,
+ "importPath": "modules.MICA.testing.stirling.stirling",
+ "description": "modules.MICA.testing.stirling.stirling",
+ "peekOfCode": "def test():\n global experiments\n if len(experiments) == 0:\n experiments = list(filter(lambda f: 'condor' not in f and 'resnet' in f, os.listdir('../../output/')))\n # experiments = list(filter(lambda f: 'experiment_' in f, os.listdir('../../output/')))\n os.system('rm -rf logs')\n os.system('rm -rf jobs')\n os.makedirs('logs', exist_ok=True)\n os.makedirs('jobs', exist_ok=True)\n for experiment in sorted(experiments):",
+ "detail": "modules.MICA.testing.stirling.stirling",
+ "documentation": {}
+ },
+ {
+ "label": "logs",
+ "kind": 5,
+ "importPath": "modules.MICA.testing.stirling.stirling",
+ "description": "modules.MICA.testing.stirling.stirling",
+ "peekOfCode": "logs = '/home/wzielonka/projects/MICA/testing/stirling/logs/'\njobs = '/home/wzielonka/projects/MICA/testing/stirling/jobs/'\nroot = '/home/wzielonka/projects/MICA/output/'\nexperiments = []\ndef test():\n global experiments\n if len(experiments) == 0:\n experiments = list(filter(lambda f: 'condor' not in f and 'resnet' in f, os.listdir('../../output/')))\n # experiments = list(filter(lambda f: 'experiment_' in f, os.listdir('../../output/')))\n os.system('rm -rf logs')",
+ "detail": "modules.MICA.testing.stirling.stirling",
+ "documentation": {}
+ },
+ {
+ "label": "jobs",
+ "kind": 5,
+ "importPath": "modules.MICA.testing.stirling.stirling",
+ "description": "modules.MICA.testing.stirling.stirling",
+ "peekOfCode": "jobs = '/home/wzielonka/projects/MICA/testing/stirling/jobs/'\nroot = '/home/wzielonka/projects/MICA/output/'\nexperiments = []\ndef test():\n global experiments\n if len(experiments) == 0:\n experiments = list(filter(lambda f: 'condor' not in f and 'resnet' in f, os.listdir('../../output/')))\n # experiments = list(filter(lambda f: 'experiment_' in f, os.listdir('../../output/')))\n os.system('rm -rf logs')\n os.system('rm -rf jobs')",
+ "detail": "modules.MICA.testing.stirling.stirling",
+ "documentation": {}
+ },
+ {
+ "label": "root",
+ "kind": 5,
+ "importPath": "modules.MICA.testing.stirling.stirling",
+ "description": "modules.MICA.testing.stirling.stirling",
+ "peekOfCode": "root = '/home/wzielonka/projects/MICA/output/'\nexperiments = []\ndef test():\n global experiments\n if len(experiments) == 0:\n experiments = list(filter(lambda f: 'condor' not in f and 'resnet' in f, os.listdir('../../output/')))\n # experiments = list(filter(lambda f: 'experiment_' in f, os.listdir('../../output/')))\n os.system('rm -rf logs')\n os.system('rm -rf jobs')\n os.makedirs('logs', exist_ok=True)",
+ "detail": "modules.MICA.testing.stirling.stirling",
+ "documentation": {}
+ },
+ {
+ "label": "experiments",
+ "kind": 5,
+ "importPath": "modules.MICA.testing.stirling.stirling",
+ "description": "modules.MICA.testing.stirling.stirling",
+ "peekOfCode": "experiments = []\ndef test():\n global experiments\n if len(experiments) == 0:\n experiments = list(filter(lambda f: 'condor' not in f and 'resnet' in f, os.listdir('../../output/')))\n # experiments = list(filter(lambda f: 'experiment_' in f, os.listdir('../../output/')))\n os.system('rm -rf logs')\n os.system('rm -rf jobs')\n os.makedirs('logs', exist_ok=True)\n os.makedirs('jobs', exist_ok=True)",
+ "detail": "modules.MICA.testing.stirling.stirling",
+ "documentation": {}
+ },
+ {
+ "label": "api_MICA",
+ "kind": 6,
+ "importPath": "modules.MICA.api_MICA",
+ "description": "modules.MICA.api_MICA",
+ "peekOfCode": "class api_MICA(object):\n def __init__(self):\n cfg = get_cfg_defaults()\n device = 'cuda:0'\n cfg.model.testing = True\n self.mica = util.find_model_using_name(model_dir='micalib', model_name=cfg.model.name)(cfg, device)\n self.load_checkpoint(self.mica,model_path=cfg.pretrained_model_path)\n self.mica.eval()\n self.app = FaceAnalysis(name='antelopev2', \n providers=['CPUExecutionProvider'],",
+ "detail": "modules.MICA.api_MICA",
+ "documentation": {}
+ },
+ {
+ "label": "deterministic",
+ "kind": 2,
+ "importPath": "modules.MICA.demo",
+ "description": "modules.MICA.demo",
+ "peekOfCode": "def deterministic(rank):\n torch.manual_seed(rank)\n torch.cuda.manual_seed(rank)\n np.random.seed(rank)\n random.seed(rank)\n cudnn.deterministic = True\n cudnn.benchmark = False\ndef process(args, app, image_size=224):\n dst = Path(args.a)\n dst.mkdir(parents=True, exist_ok=True)",
+ "detail": "modules.MICA.demo",
+ "documentation": {}
+ },
+ {
+ "label": "process",
+ "kind": 2,
+ "importPath": "modules.MICA.demo",
+ "description": "modules.MICA.demo",
+ "peekOfCode": "def process(args, app, image_size=224):\n dst = Path(args.a)\n dst.mkdir(parents=True, exist_ok=True)\n processes = []\n image_paths = sorted(glob(args.i + '/*.*'))\n for image_path in tqdm(image_paths):\n name = Path(image_path).stem\n img = cv2.imread(image_path)\n bboxes, kpss = app.det_model.detect(img, max_num=0, metric='default')\n if bboxes.shape[0] == 0:",
+ "detail": "modules.MICA.demo",
+ "documentation": {}
+ },
+ {
+ "label": "to_batch",
+ "kind": 2,
+ "importPath": "modules.MICA.demo",
+ "description": "modules.MICA.demo",
+ "peekOfCode": "def to_batch(path):\n src = path.replace('npy', 'jpg')\n if not os.path.exists(src):\n src = path.replace('npy', 'png')\n image = imread(src)[:, :, :3]\n image = image / 255.\n image = cv2.resize(image, (224, 224)).transpose(2, 0, 1)\n image = torch.tensor(image).cuda()[None]\n arcface = np.load(path)\n arcface = torch.tensor(arcface).cuda()[None]",
+ "detail": "modules.MICA.demo",
+ "documentation": {}
+ },
+ {
+ "label": "load_checkpoint",
+ "kind": 2,
+ "importPath": "modules.MICA.demo",
+ "description": "modules.MICA.demo",
+ "peekOfCode": "def load_checkpoint(args, mica):\n checkpoint = torch.load(args.m)\n if 'arcface' in checkpoint:\n mica.arcface.load_state_dict(checkpoint['arcface'])\n if 'flameModel' in checkpoint:\n mica.flameModel.load_state_dict(checkpoint['flameModel'])\ndef main(cfg, args):\n device = 'cuda:0'\n cfg.model.testing = True\n mica = util.find_model_using_name(model_dir='micalib.models', model_name=cfg.model.name)(cfg, device)",
+ "detail": "modules.MICA.demo",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.MICA.demo",
+ "description": "modules.MICA.demo",
+ "peekOfCode": "def main(cfg, args):\n device = 'cuda:0'\n cfg.model.testing = True\n mica = util.find_model_using_name(model_dir='micalib.models', model_name=cfg.model.name)(cfg, device)\n load_checkpoint(args, mica)\n mica.eval()\n faces = mica.render.faces[0].cpu()\n Path(args.o).mkdir(exist_ok=True, parents=True)\n app = FaceAnalysis(name='antelopev2', providers=['CPUExecutionProvider'])\n # app = FaceAnalysis(name='antelopev2', providers=['CUDAExecutionProvider'])",
+ "detail": "modules.MICA.demo",
+ "documentation": {}
+ },
+ {
+ "label": "setup",
+ "kind": 2,
+ "importPath": "modules.MICA.jobs",
+ "description": "modules.MICA.jobs",
+ "peekOfCode": "def setup(rank, world_size, port):\n os.environ['MASTER_ADDR'] = 'localhost'\n os.environ['MASTER_PORT'] = str(port)\n dist.init_process_group(\"nccl\", rank=rank, world_size=world_size, init_method=\"env://\")\ndef deterministic(rank):\n torch.manual_seed(rank)\n torch.cuda.manual_seed(rank)\n np.random.seed(rank)\n random.seed(rank)\n cudnn.deterministic = True",
+ "detail": "modules.MICA.jobs",
+ "documentation": {}
+ },
+ {
+ "label": "deterministic",
+ "kind": 2,
+ "importPath": "modules.MICA.jobs",
+ "description": "modules.MICA.jobs",
+ "peekOfCode": "def deterministic(rank):\n torch.manual_seed(rank)\n torch.cuda.manual_seed(rank)\n np.random.seed(rank)\n random.seed(rank)\n cudnn.deterministic = True\n cudnn.benchmark = False\ndef test(rank, world_size, cfg, args):\n port = np.random.randint(low=0, high=2000)\n setup(rank, world_size, 12310 + port)",
+ "detail": "modules.MICA.jobs",
+ "documentation": {}
+ },
+ {
+ "label": "test",
+ "kind": 2,
+ "importPath": "modules.MICA.jobs",
+ "description": "modules.MICA.jobs",
+ "peekOfCode": "def test(rank, world_size, cfg, args):\n port = np.random.randint(low=0, high=2000)\n setup(rank, world_size, 12310 + port)\n deterministic(rank)\n cfg.model.testing = True\n mica = util.find_model_using_name(model_dir='micalib.models', model_name=cfg.model.name)(cfg, rank)\n tester = Tester(nfc_model=mica, config=cfg, device=rank)\n tester.render_mesh = True\n if args.test_dataset.upper() == 'STIRLING':\n tester.test_stirling(args.checkpoint)",
+ "detail": "modules.MICA.jobs",
+ "documentation": {}
+ },
+ {
+ "label": "train",
+ "kind": 2,
+ "importPath": "modules.MICA.jobs",
+ "description": "modules.MICA.jobs",
+ "peekOfCode": "def train(rank, world_size, cfg):\n port = np.random.randint(low=0, high=2000)\n setup(rank, world_size, 12310 + port)\n logger.info(f'[MAIN] output_dir: {cfg.output_dir}')\n os.makedirs(os.path.join(cfg.output_dir, cfg.train.log_dir), exist_ok=True)\n os.makedirs(os.path.join(cfg.output_dir, cfg.train.vis_dir), exist_ok=True)\n os.makedirs(os.path.join(cfg.output_dir, cfg.train.val_vis_dir), exist_ok=True)\n with open(os.path.join(cfg.output_dir, cfg.train.log_dir, 'full_config.yaml'), 'w') as f:\n yaml.dump(cfg, f, default_flow_style=False)\n # shutil.copy(cfg.cfg_file, os.path.join(cfg.output_dir, 'config.yaml'))",
+ "detail": "modules.MICA.jobs",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.MICA.render_dataset",
+ "description": "modules.MICA.render_dataset",
+ "peekOfCode": "def main():\n cfg = get_cfg_defaults()\n render = MeshShapeRenderer(obj_filename=cfg.model.topology_path)\n flame = FLAME(cfg.model).to('cuda:0')\n datasets = sorted(glob('/home/wzielonka/datasets/MICA/*'))\n for dataset in tqdm(datasets):\n meshes = sorted(glob(f'{dataset}/FLAME_parameters/*/*.npz'))\n sample_list = np.array(np.random.choice(range(len(meshes)), size=30 * 5))\n dst = Path('./output', Path(dataset).name)\n dst.mkdir(parents=True, exist_ok=True)",
+ "detail": "modules.MICA.render_dataset",
+ "documentation": {}
+ },
+ {
+ "label": "api_multi_body",
+ "kind": 2,
+ "importPath": "modules.PIXIE.demos.api_multi_pixie",
+ "description": "modules.PIXIE.demos.api_multi_pixie",
+ "peekOfCode": "def api_multi_body(\n imgfolder='',\n savefolder='',\n visfolder='',\n focal=5000,\n device='cuda',\n iscrop=True,\n saveVis=True,\n saveMat=True,\n rasterizer_type='pytorch3d'",
+ "detail": "modules.PIXIE.demos.api_multi_pixie",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.PIXIE.demos.demo_animate_body",
+ "description": "modules.PIXIE.demos.demo_animate_body",
+ "peekOfCode": "def main(args):\n savefolder = args.savefolder\n device = args.device\n os.makedirs(savefolder, exist_ok=True)\n # check env\n if not torch.cuda.is_available():\n print('CUDA is not available! use CPU instead')\n else:\n cudnn.benchmark = True\n torch.backends.cudnn.deterministic = False",
+ "detail": "modules.PIXIE.demos.demo_animate_body",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.PIXIE.demos.demo_fit_body",
+ "description": "modules.PIXIE.demos.demo_fit_body",
+ "peekOfCode": "def main(args):\n savefolder = args.savefolder\n device = args.device\n os.makedirs(savefolder, exist_ok=True)\n # check env\n if not torch.cuda.is_available():\n print('CUDA is not available! use CPU instead')\n else:\n cudnn.benchmark = True\n torch.backends.cudnn.deterministic = False",
+ "detail": "modules.PIXIE.demos.demo_fit_body",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.PIXIE.demos.demo_fit_face",
+ "description": "modules.PIXIE.demos.demo_fit_face",
+ "peekOfCode": "def main(args):\n savefolder = args.savefolder\n device = args.device\n os.makedirs(savefolder, exist_ok=True)\n # check env\n if not torch.cuda.is_available():\n print('CUDA is not available! use CPU instead')\n else:\n cudnn.benchmark = True\n torch.backends.cudnn.deterministic = False",
+ "detail": "modules.PIXIE.demos.demo_fit_face",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.PIXIE.demos.demo_fit_hand",
+ "description": "modules.PIXIE.demos.demo_fit_hand",
+ "peekOfCode": "def main(args):\n savefolder = args.savefolder\n device = args.device\n os.makedirs(savefolder, exist_ok=True)\n # check env\n if not torch.cuda.is_available():\n print('CUDA is not available! use CPU instead')\n else:\n cudnn.benchmark = True\n torch.backends.cudnn.deterministic = False",
+ "detail": "modules.PIXIE.demos.demo_fit_hand",
+ "documentation": {}
+ },
+ {
+ "label": "dict_tensor2npy",
+ "kind": 2,
+ "importPath": "modules.PIXIE.demos.demo_multi_bodys",
+ "description": "modules.PIXIE.demos.demo_multi_bodys",
+ "peekOfCode": "def dict_tensor2npy(tensor_dict):\n npy_dict = {}\n for key, value in tensor_dict.items():\n # print(type(value))\n # if type(value)==dict:\n # pass\n # # print('dict')\n # # npy_dict[key] = dict_tensor2npy(value)\n # el\n if type(value)==torch.Tensor:",
+ "detail": "modules.PIXIE.demos.demo_multi_bodys",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.PIXIE.demos.demo_multi_bodys",
+ "description": "modules.PIXIE.demos.demo_multi_bodys",
+ "peekOfCode": "def main(args):\n savefolder = args.savefolder\n device = args.device\n os.makedirs(savefolder, exist_ok=True)\n # check env\n if not torch.cuda.is_available():\n print('CUDA is not available! use CPU instead')\n else:\n cudnn.benchmark = True\n torch.backends.cudnn.deterministic = False",
+ "detail": "modules.PIXIE.demos.demo_multi_bodys",
+ "documentation": {}
+ },
+ {
+ "label": "main",
+ "kind": 2,
+ "importPath": "modules.PIXIE.demos.demo_whole_body",
+ "description": "modules.PIXIE.demos.demo_whole_body",
+ "peekOfCode": "def main(args):\n savefolder = args.savefolder\n device = args.device\n os.makedirs(savefolder, exist_ok=True)\n # check env\n if not torch.cuda.is_available():\n print('CUDA is not available! use CPU instead')\n else:\n cudnn.benchmark = True\n torch.backends.cudnn.deterministic = False",
+ "detail": "modules.PIXIE.demos.demo_whole_body",
+ "documentation": {}
+ },
+ {
+ "label": "TestData",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.datasets.body_datasets",
+ "description": "modules.PIXIE.pixielib.datasets.body_datasets",
+ "peekOfCode": "class TestData(Dataset):\n def __init__(self, testpath, iscrop=False, \n crop_size=224, hd_size = 1024, \n scale=1.1, body_detector='rcnn', \n device='cpu',\n en_multi_person=True\n ):\n '''\n testpath: folder, imagepath_list, image path, video path\n '''",
+ "detail": "modules.PIXIE.pixielib.datasets.body_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "build_dataloader",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.datasets.body_datasets",
+ "description": "modules.PIXIE.pixielib.datasets.body_datasets",
+ "peekOfCode": "def build_dataloader(testpath, batch_size=1):\n data_list = []\n dataset = TestData(testpath = testpath)\n dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False,\n num_workers=1,\n pin_memory=True,\n drop_last =False)\n return dataset, dataloader\ndef video2sequence(video_path):\n print('extract frames from video: {}...'.format(video_path))",
+ "detail": "modules.PIXIE.pixielib.datasets.body_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "video2sequence",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.datasets.body_datasets",
+ "description": "modules.PIXIE.pixielib.datasets.body_datasets",
+ "peekOfCode": "def video2sequence(video_path):\n print('extract frames from video: {}...'.format(video_path))\n videofolder = video_path.split('.')[0]\n os.makedirs(videofolder, exist_ok=True)\n video_name = video_path.split('/')[-1].split('.')[0]\n vidcap = cv2.VideoCapture(video_path)\n success, image = vidcap.read()\n count = 0\n imagepath_list = []\n while success:",
+ "detail": "modules.PIXIE.pixielib.datasets.body_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "FasterRCNN",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.datasets.detectors",
+ "description": "modules.PIXIE.pixielib.datasets.detectors",
+ "peekOfCode": "class FasterRCNN(object):\n ''' detect body\n '''\n def __init__(self, device='cuda:0'): \n '''\n https://pytorch.org/docs/stable/torchvision/models.html#faster-r-cnn\n '''\n import torchvision\n # self.model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n self.model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(pretrained=True)",
+ "detail": "modules.PIXIE.pixielib.datasets.detectors",
+ "documentation": {}
+ },
+ {
+ "label": "Yolov4",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.datasets.detectors",
+ "description": "modules.PIXIE.pixielib.datasets.detectors",
+ "peekOfCode": "class Yolov4(object):\n def __init__(self, device='cuda:0'):\n pass\n @torch.no_grad()\n def run(self, image):\n '''\n image: 0-255, uint8, rgb, [h, w, 3]\n return: detected box list\n '''\n pass",
+ "detail": "modules.PIXIE.pixielib.datasets.detectors",
+ "documentation": {}
+ },
+ {
+ "label": "KeypointRCNN",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.datasets.detectors",
+ "description": "modules.PIXIE.pixielib.datasets.detectors",
+ "peekOfCode": "class KeypointRCNN(object):\n ''' Constructs a Keypoint R-CNN model with a ResNet-50-FPN backbone.\n Ref: https://pytorch.org/docs/stable/torchvision/models.html#keypoint-r-cnn\n 'nose',\n 'left_eye',\n 'right_eye',\n 'left_ear',\n 'right_ear',\n 'left_shoulder',\n 'right_shoulder',",
+ "detail": "modules.PIXIE.pixielib.datasets.detectors",
+ "documentation": {}
+ },
+ {
+ "label": "FAN",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.datasets.detectors",
+ "description": "modules.PIXIE.pixielib.datasets.detectors",
+ "peekOfCode": "class FAN(object):\n def __init__(self):\n import face_alignment\n self.model = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False,device='cpu')\n def run(self, image):\n '''\n image: 0-255, uint8, rgb, [h, w, 3]\n return: detected box list\n '''\n out = self.model.get_landmarks(image)",
+ "detail": "modules.PIXIE.pixielib.datasets.detectors",
+ "documentation": {}
+ },
+ {
+ "label": "COCO_INSTANCE_CATEGORY_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.detectors",
+ "description": "modules.PIXIE.pixielib.datasets.detectors",
+ "peekOfCode": "COCO_INSTANCE_CATEGORY_NAMES = [\n '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',\n 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',\n 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',\n 'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',\n 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',\n 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',\n 'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',\n 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',\n 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',",
+ "detail": "modules.PIXIE.pixielib.datasets.detectors",
+ "documentation": {}
+ },
+ {
+ "label": "TestData",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "description": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "peekOfCode": "class TestData(Dataset):\n def __init__(self, testpath, iscrop=True, crop_size=224, scale=2.0, face_detector='fan'):\n '''\n testpath: folder, imagepath_list, image path, video path\n '''\n if isinstance(testpath, list):\n self.imagepath_list = testpath\n elif os.path.isdir(testpath): \n self.imagepath_list = glob(testpath + '/*.jpg') + glob(testpath + '/*.png') + glob(testpath + '/*.bmp') + glob(testpath + '/*.jpeg')\n elif os.path.isfile(testpath) and (testpath[-3:] in ['jpg', 'png', 'bmp']):",
+ "detail": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "NoWTest",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "description": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "peekOfCode": "class NoWTest(Dataset):\n def __init__(self, iscrop=True, crop_size=224, scale=2.0, ):\n self.iscrop = iscrop\n self.scale = scale\n self.crop_size = crop_size\n # self.data_path = '/ps/scratch/face2d3d/texture_in_the_wild_code/NoW_validation/image_paths_ring_6_elements.npy'\n self.data_path = '/ps/scratch/face2d3d/ringnetpp/eccv/test_data/evaluation/NoW_Dataset/final_release_version/test_image_paths_ring_6_elements.npy'\n self.data_lines = np.load(self.data_path).astype('str').flatten()\n # import ipdb; ipdb.set_trace()\n self.imagepath = '/ps/scratch/face2d3d/ringnetpp/eccv/test_data/evaluation/NoW_Dataset/final_release_version/iphone_pictures/'",
+ "detail": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "NoWTest_body",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "description": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "peekOfCode": "class NoWTest_body(Dataset):\n def __init__(self, iscrop=False, crop_size=224, hd_size = 1024, scale=1.1, body_detector='rcnn', device='cuda:0'):\n self.iscrop = iscrop\n self.scale = scale\n self.crop_size = crop_size\n # self.data_path = '/ps/scratch/face2d3d/texture_in_the_wild_code/NoW_validation/image_paths_ring_6_elements.npy'\n # self.data_path = '/ps/scratch/face2d3d/ringnetpp/eccv/test_data/evaluation/NoW_Dataset/final_release_version/test_image_paths_ring_6_elements.npy'\n self.data_path = '/ps/scratch/face2d3d/texture_in_the_wild_code/NoW_validation/image_paths._ring_6_elements.npy'\n self.data_lines = np.load(self.data_path).astype('str').flatten()\n # import ipdb; ipdb.set_trace()",
+ "detail": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "VGGFace2",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "description": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "peekOfCode": "class VGGFace2(Dataset):\n def __init__(self, crop_size, scale=[1, 1], trans_scale = 0., blur_step=1, split='train'):\n '''\n K must be less than 6\n '''\n self.image_size = crop_size\n self.imagefolder = '/ps/scratch/face2d3d/train'\n self.kptfolder = '/ps/scratch/face2d3d/train_annotated_torch7'\n self.segfolder = '/ps/scratch/face2d3d/texture_in_the_wild_code/VGGFace2_seg/test_crop_size_400_batch'\n self.attfolder = '/ps/scratch/yfeng/Data/vggface2/gender-DEX'",
+ "detail": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "video2sequence",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "description": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "peekOfCode": "def video2sequence(video_path):\n videofolder = video_path.split('.')[0]\n util.check_mkdir(videofolder)\n video_name = video_path.split('/')[-1].split('.')[0]\n vidcap = cv2.VideoCapture(video_path)\n success,image = vidcap.read()\n count = 0\n imagepath_list = []\n while success:\n imagepath = '{}/{}_frame{:04d}.jpg'.format(videofolder, video_name, count)",
+ "detail": "modules.PIXIE.pixielib.datasets.face_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "TestData",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.datasets.hand_datasets",
+ "description": "modules.PIXIE.pixielib.datasets.hand_datasets",
+ "peekOfCode": "class TestData(Dataset):\n def __init__(self, testpath, iscrop=True, crop_size=224, scale=2.4, detector='hand'):\n '''\n testpath: folder, imagepath_list, image path, video path\n '''\n if isinstance(testpath, list):\n self.imagepath_list = testpath\n elif os.path.isdir(testpath): \n self.imagepath_list = glob(testpath + '/*.jpg') + glob(testpath + '/*.png') + glob(testpath + '/*.bmp')\n elif os.path.isfile(testpath) and (testpath[-3:] in ['jpg', 'png', 'bmp']):",
+ "detail": "modules.PIXIE.pixielib.datasets.hand_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "video2sequence",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.datasets.hand_datasets",
+ "description": "modules.PIXIE.pixielib.datasets.hand_datasets",
+ "peekOfCode": "def video2sequence(video_path):\n videofolder = video_path.split('.')[0]\n util.check_mkdir(videofolder)\n video_name = video_path.split('/')[-1].split('.')[0]\n vidcap = cv2.VideoCapture(video_path)\n success,image = vidcap.read()\n count = 0\n imagepath_list = []\n while success:\n imagepath = '{}/{}_frame{:04d}.jpg'.format(videofolder, video_name, count)",
+ "detail": "modules.PIXIE.pixielib.datasets.hand_datasets",
+ "documentation": {}
+ },
+ {
+ "label": "map_keypoints",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "def map_keypoints(keypoints, conf, source_names, target_names = 'smplx',\n dim=2):\n ''' Maps the keypoints from the source to a target dataset format\n '''\n source_dataset = source_names\n target_dataset = target_names\n names_dict = KEYPOINT_NAMES_DICT\n mapping = {}\n source_names = names_dict.get(source_dataset)\n target_names = names_dict.get(target_dataset)",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "__all__",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "__all__ = [\n 'KEYPOINT_PARTS',\n 'KEYPOINT_CONNECTIONS',\n 'KEYPOINT_NAMES_DICT',\n 'KEYPOINT_CONNECTIONS_DICT',\n 'KEYPOINT_PARTS_DICT',\n 'PART_NAMES',\n 'KEYPOINT_PART_CONNECTION_DICTS',\n]\nKEYPOINT_PARTS = {'pelvis': 'body,torso',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "KEYPOINT_PARTS",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "KEYPOINT_PARTS = {'pelvis': 'body,torso',\n 'left_hip': 'body,torso',\n 'right_hip': 'body,torso',\n 'spine1': 'body,torso',\n 'left_knee': 'body',\n 'right_knee': 'body',\n 'spine2': 'body,torso,upper',\n 'left_ankle': 'body',\n 'right_ankle': 'body',\n 'spine3': 'body,torso,upper',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "PART_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "PART_NAMES = {\n 'body',\n 'left_hand',\n 'right_hand',\n 'face',\n 'head',\n 'upper',\n 'torso',\n}\nKEYPOINT_CONNECTIONS = [",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "KEYPOINT_CONNECTIONS",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "KEYPOINT_CONNECTIONS = [\n ['pelvis', 'spine1'],\n ['spine1', 'spine2'],\n ['spine2', 'spine3'],\n ['spine3', 'left_collar'],\n ['spine3', 'right_collar'],\n ['left_collar', 'left_shoulder'],\n ['right_collar', 'right_shoulder'],\n ['spine3', 'neck'],\n ['neck', 'head'],",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "FACIAL_LANDMARKS",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "FACIAL_LANDMARKS = [\n 'right_eye_brow1',\n 'right_eye_brow2',\n 'right_eye_brow3',\n 'right_eye_brow4',\n 'right_eye_brow5',\n 'left_eye_brow5',\n 'left_eye_brow4',\n 'left_eye_brow3',\n 'left_eye_brow2',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "SMPL_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "SMPL_KEYPOINT_NAMES = [\n 'pelvis',\n 'left_hip',\n 'right_hip',\n 'spine1',\n 'left_knee',\n 'right_knee',\n 'spine2',\n 'left_ankle',\n 'right_ankle',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "SMPLH_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "SMPLH_KEYPOINT_NAMES = SMPL_KEYPOINT_NAMES[:-2] + [\n 'left_index1',\n 'left_index2',\n 'left_index3',\n 'left_middle1',\n 'left_middle2',\n 'left_middle3',\n 'left_pinky1',\n 'left_pinky2',\n 'left_pinky3',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "SMPLX_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "SMPLX_KEYPOINT_NAMES = (SMPL_KEYPOINT_NAMES[:-2] +\n ['jaw', 'left_eye_smplx', 'right_eye_smplx'] +\n SMPLH_KEYPOINT_NAMES[22:] +\n FACIAL_LANDMARKS)\nMANO_NAMES = [\n 'wrist',\n 'index1',\n 'index2',\n 'index3',\n 'middle1',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "MANO_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "MANO_NAMES = [\n 'wrist',\n 'index1',\n 'index2',\n 'index3',\n 'middle1',\n 'middle2',\n 'middle3',\n 'pinky1',\n 'pinky2',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "HO3D_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "HO3D_NAMES = MANO_NAMES + ['thumb', 'index', 'middle', 'ring', 'pinky']\nAGORA_NAMES = [\n 'pelvis',\n 'left_hip',\n 'right_hip',\n 'spine1',\n 'left_knee',\n 'right_knee',\n 'spine2',\n 'left_ankle',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "AGORA_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "AGORA_NAMES = [\n 'pelvis',\n 'left_hip',\n 'right_hip',\n 'spine1',\n 'left_knee',\n 'right_knee',\n 'spine2',\n 'left_ankle',\n 'right_ankle',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "EHF_KEYPOINTS",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "EHF_KEYPOINTS = [\n 'pelvis',\n 'left_hip',\n 'right_hip',\n 'spine1',\n 'left_knee',\n 'right_knee',\n 'spine2',\n 'left_ankle',\n 'right_ankle',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "OPENPOSE19_KEYPOINT_NAMES_v1",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "OPENPOSE19_KEYPOINT_NAMES_v1 = [\n 'nose', 'neck',\n 'right_shoulder', 'right_elbow', 'right_wrist',\n 'left_shoulder', 'left_elbow', 'left_wrist',\n 'pelvis',\n 'right_hip', 'right_knee', 'right_ankle',\n 'left_hip', 'left_knee', 'left_ankle',\n 'right_eye', 'left_eye', 'right_ear', 'left_ear',\n 'left_wrist',\n 'left_thumb1', 'left_thumb2', 'left_thumb3', 'left_thumb',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "FEET_KEYPS_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "FEET_KEYPS_NAMES = ['left_big_toe', 'left_small_toe', 'left_heel',\n 'right_big_toe', 'right_small_toe', 'right_heel']\nOPENPOSE25_KEYPOINT_NAMES_V1 = deepcopy(OPENPOSE19_KEYPOINT_NAMES_v1)\nstart = 19\nfor feet_name in FEET_KEYPS_NAMES:\n OPENPOSE25_KEYPOINT_NAMES_V1.insert(start, feet_name)\n start += 1\nMPII_KEYPOINT_NAMES = [\n 'right_ankle',\n 'right_knee',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "OPENPOSE25_KEYPOINT_NAMES_V1",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "OPENPOSE25_KEYPOINT_NAMES_V1 = deepcopy(OPENPOSE19_KEYPOINT_NAMES_v1)\nstart = 19\nfor feet_name in FEET_KEYPS_NAMES:\n OPENPOSE25_KEYPOINT_NAMES_V1.insert(start, feet_name)\n start += 1\nMPII_KEYPOINT_NAMES = [\n 'right_ankle',\n 'right_knee',\n 'right_hip',\n 'left_hip',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "start",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "start = 19\nfor feet_name in FEET_KEYPS_NAMES:\n OPENPOSE25_KEYPOINT_NAMES_V1.insert(start, feet_name)\n start += 1\nMPII_KEYPOINT_NAMES = [\n 'right_ankle',\n 'right_knee',\n 'right_hip',\n 'left_hip',\n 'left_knee',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "MPII_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "MPII_KEYPOINT_NAMES = [\n 'right_ankle',\n 'right_knee',\n 'right_hip',\n 'left_hip',\n 'left_knee',\n 'left_ankle',\n 'pelvis',\n 'thorax',\n 'upper_neck',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "FLAME_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "FLAME_KEYPOINT_NAMES = [\n 'global', 'neck', 'jaw', 'left_eye', 'right_eye'] + FACIAL_LANDMARKS\nFFHQ_KEYPOINTS = FLAME_KEYPOINT_NAMES\nCOCO_KEYPOINTS = ['nose',\n 'left_eye',\n 'right_eye',\n 'left_ear',\n 'right_ear',\n 'left_shoulder',\n 'right_shoulder',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "FFHQ_KEYPOINTS",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "FFHQ_KEYPOINTS = FLAME_KEYPOINT_NAMES\nCOCO_KEYPOINTS = ['nose',\n 'left_eye',\n 'right_eye',\n 'left_ear',\n 'right_ear',\n 'left_shoulder',\n 'right_shoulder',\n 'left_elbow',\n 'right_elbow',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "COCO_KEYPOINTS",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "COCO_KEYPOINTS = ['nose',\n 'left_eye',\n 'right_eye',\n 'left_ear',\n 'right_ear',\n 'left_shoulder',\n 'right_shoulder',\n 'left_elbow',\n 'right_elbow',\n 'left_wrist',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "COCO_WHOLE_BODY_KEYPOINTS",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "COCO_WHOLE_BODY_KEYPOINTS = COCO_KEYPOINTS + FEET_KEYPS_NAMES + [\n 'left_wrist',\n 'left_thumb1', 'left_thumb2', 'left_thumb3', 'left_thumb',\n 'left_index1', 'left_index2', 'left_index3', 'left_index',\n 'left_middle1', 'left_middle2', 'left_middle3', 'left_middle',\n 'left_ring1', 'left_ring2', 'left_ring3', 'left_ring',\n 'left_pinky1', 'left_pinky2', 'left_pinky3', 'left_pinky',\n 'right_wrist',\n 'right_thumb1', 'right_thumb2', 'right_thumb3', 'right_thumb',\n 'right_index1', 'right_index2', 'right_index3', 'right_index',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "THREEDPW_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "THREEDPW_KEYPOINT_NAMES = [\n 'nose',\n 'neck',\n 'right_shoulder',\n 'right_elbow',\n 'right_wrist',\n 'left_shoulder',\n 'left_elbow',\n 'left_wrist',\n 'right_hip',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "POSETRACK_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "POSETRACK_KEYPOINT_NAMES = [\n 'nose',\n 'neck',\n 'head_top',\n 'left_ear',\n 'right_ear',\n 'left_shoulder',\n 'right_shoulder',\n 'left_elbow',\n 'right_elbow',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "AICH_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "AICH_KEYPOINT_NAMES = [\n 'right_shoulder',\n 'right_elbow',\n 'right_wrist',\n 'left_shoulder',\n 'left_elbow',\n 'left_wrist',\n 'right_hip',\n 'right_knee',\n 'right_ankle',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "SPIN_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "SPIN_KEYPOINT_NAMES = [\n 'right_ankle',\n 'right_knee',\n 'right_hip',\n 'left_hip',\n 'left_knee',\n 'left_ankle',\n 'right_wrist',\n 'right_elbow',\n 'right_shoulder',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "SPINX_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "SPINX_KEYPOINT_NAMES = [\n 'right_ankle',\n 'right_knee',\n 'right_hip',\n 'left_hip',\n 'left_knee',\n 'left_ankle',\n 'right_wrist',\n 'right_elbow',\n 'right_shoulder',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "PANOPTIC_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "PANOPTIC_KEYPOINT_NAMES = [\n 'neck',\n 'nose',\n 'pelvis',\n 'left_shoulder',\n 'left_elbow',\n 'left_wrist',\n 'left_hip',\n 'left_knee',\n 'left_ankle',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "FREIHAND_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "FREIHAND_NAMES = [\n 'right_wrist',\n 'right_thumb1',\n 'right_thumb2',\n 'right_thumb3',\n 'right_thumb',\n 'right_index1',\n 'right_index2',\n 'right_index3',\n 'right_index',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "LSP_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "LSP_NAMES = [\n 'right_ankle',\n 'right_knee',\n 'right_hip',\n 'left_hip',\n 'left_knee',\n 'left_ankle',\n 'right_wrist',\n 'right_elbow',\n 'right_shoulder',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "RAW_H36M_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "RAW_H36M_NAMES = [\n 'pelvis',\n 'left_hip',\n 'left_knee',\n 'left_ankle',\n 'right_hip',\n 'right_knee',\n 'right_ankle',\n 'spine',\n 'neck', # 'thorax',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "H36M_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "H36M_NAMES = [\n 'right_ankle',\n 'right_knee',\n 'right_hip',\n 'left_hip',\n 'left_knee',\n 'left_ankle',\n 'right_wrist',\n 'right_elbow',\n 'right_shoulder',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "PANOPTIC_HAND_KEYPOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "PANOPTIC_HAND_KEYPOINT_NAMES = [\n 'right_wrist',\n 'right_thumb1', 'right_thumb2', 'right_thumb3', 'right_thumb',\n 'right_index1', 'right_index2', 'right_index3', 'right_index',\n 'right_middle1', 'right_middle2', 'right_middle3', 'right_middle',\n 'right_ring1', 'right_ring2', 'right_ring3', 'right_ring',\n 'right_pinky1', 'right_pinky2', 'right_pinky3', 'right_pinky'\n]\nAGORA_NAMES = [\n 'pelvis',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "AGORA_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "AGORA_NAMES = [\n 'pelvis',\n 'left_hip',\n 'right_hip',\n 'spine1',\n 'left_knee',\n 'right_knee',\n 'spine2',\n 'left_ankle',\n 'right_ankle',",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "KEYPOINT_NAMES_DICT",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "KEYPOINT_NAMES_DICT = {\n 'smpl': SMPL_KEYPOINT_NAMES,\n 'smplh': SMPLH_KEYPOINT_NAMES,\n 'smplx': SMPLX_KEYPOINT_NAMES,\n 'mano': MANO_NAMES,\n 'mano-from-smplx': SMPLX_KEYPOINT_NAMES,\n 'flame-from-smplx': SMPLX_KEYPOINT_NAMES,\n 'flame': FLAME_KEYPOINT_NAMES,\n 'openpose19_v1': OPENPOSE19_KEYPOINT_NAMES_v1,\n 'openpose25_v1': OPENPOSE25_KEYPOINT_NAMES_V1,",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "extra_names",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "description": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "peekOfCode": "extra_names = ['head_top', 'left_big_toe', 'left_ear', 'left_eye', 'left_heel', 'left_index', 'left_middle', 'left_pinky', 'left_ring', 'left_small_toe', 'left_thumb', 'nose', 'right_big_toe', 'right_ear', 'right_eye', 'right_heel', 'right_index', 'right_middle', 'right_pinky', 'right_ring', 'right_small_toe', 'right_thumb']\nKEYPOINT_NAMES_DICT['smplx'] += extra_names\nimport numpy as np\nimport torch\ndef map_keypoints(keypoints, conf, source_names, target_names = 'smplx',\n dim=2):\n ''' Maps the keypoints from the source to a target dataset format\n '''\n source_dataset = source_names\n target_dataset = target_names",
+ "detail": "modules.PIXIE.pixielib.datasets.keypoint_names",
+ "documentation": {}
+ },
+ {
+ "label": "ResnetEncoder",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.encoders",
+ "description": "modules.PIXIE.pixielib.models.encoders",
+ "peekOfCode": "class ResnetEncoder(nn.Module):\n def __init__(self, append_layers = None):\n super(ResnetEncoder, self).__init__()\n from . import resnet\n # feature_size = 2048\n self.feature_dim = 2048\n self.encoder = resnet.load_ResNet50Model() #out: 2048\n # regressor\n self.append_layers = append_layers\n # for normalize input images",
+ "detail": "modules.PIXIE.pixielib.models.encoders",
+ "documentation": {}
+ },
+ {
+ "label": "MLP",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.encoders",
+ "description": "modules.PIXIE.pixielib.models.encoders",
+ "peekOfCode": "class MLP(nn.Module):\n def __init__(self, channels = [2048, 1024, 1], last_op = None):\n super(MLP, self).__init__()\n layers = []\n for l in range(0, len(channels) - 1):\n layers.append(nn.Linear(channels[l], channels[l+1]))\n if l < len(channels) - 2:\n layers.append(nn.ReLU())\n if last_op:\n layers.append(last_op)",
+ "detail": "modules.PIXIE.pixielib.models.encoders",
+ "documentation": {}
+ },
+ {
+ "label": "HRNEncoder",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.encoders",
+ "description": "modules.PIXIE.pixielib.models.encoders",
+ "peekOfCode": "class HRNEncoder(nn.Module):\n def __init__(self, append_layers = None):\n super(HRNEncoder, self).__init__()\n from . import hrnet\n self.feature_dim = 2048\n self.encoder = hrnet.load_HRNet(pretrained=True) #out: 2048\n # regressor\n self.append_layers = append_layers\n # for normalize input images\n MEAN = [0.485, 0.456, 0.406]",
+ "detail": "modules.PIXIE.pixielib.models.encoders",
+ "documentation": {}
+ },
+ {
+ "label": "FLAMETex",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.FLAME",
+ "description": "modules.PIXIE.pixielib.models.FLAME",
+ "peekOfCode": "class FLAMETex(nn.Module):\n \"\"\"\n FLAME texture:\n https://github.com/TimoBolkart/TF_FLAME/blob/ade0ab152300ec5f0e8555d6765411555c5ed43d/sample_texture.py#L64\n FLAME texture converted from BFM:\n https://github.com/TimoBolkart/BFM_to_FLAME\n \"\"\"\n def __init__(self, config):\n super(FLAMETex, self).__init__()\n if config.tex_type == 'BFM':",
+ "detail": "modules.PIXIE.pixielib.models.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "texture_flame2smplx",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.FLAME",
+ "description": "modules.PIXIE.pixielib.models.FLAME",
+ "peekOfCode": "def texture_flame2smplx(cached_data, flame_texture, smplx_texture):\n ''' Convert flame texture map (face-only) into smplx texture map (includes body texture)\n TODO: pytorch version ==> grid sample\n '''\n if smplx_texture.shape[0] != smplx_texture.shape[1]:\n print('SMPL-X texture not squared (%d != %d)' % (smplx_texture[0], smplx_texture[1]))\n return\n if smplx_texture.shape[0] != cached_data['target_resolution']:\n print('SMPL-X texture size does not match cached image resolution (%d != %d)' % (smplx_texture.shape[0], cached_data['target_resolution']))\n return",
+ "detail": "modules.PIXIE.pixielib.models.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "HighResolutionModule",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.hrnet",
+ "description": "modules.PIXIE.pixielib.models.hrnet",
+ "peekOfCode": "class HighResolutionModule(nn.Module):\n def __init__(self, num_branches, blocks, num_blocks, num_inchannels,\n num_channels, fuse_method, multi_scale_output=True):\n super(HighResolutionModule, self).__init__()\n self._check_branches(\n num_branches, blocks, num_blocks, num_inchannels, num_channels)\n self.num_inchannels = num_inchannels\n self.fuse_method = fuse_method\n self.num_branches = num_branches\n self.multi_scale_output = multi_scale_output",
+ "detail": "modules.PIXIE.pixielib.models.hrnet",
+ "documentation": {}
+ },
+ {
+ "label": "HighResolutionNet",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.hrnet",
+ "description": "modules.PIXIE.pixielib.models.hrnet",
+ "peekOfCode": "class HighResolutionNet(nn.Module):\n def __init__(self, cfg, **kwargs):\n self.inplanes = 64\n super(HighResolutionNet, self).__init__()\n use_old_impl = cfg.get('use_old_impl')\n self.use_old_impl = use_old_impl\n # stem net\n self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,\n bias=False)\n self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)",
+ "detail": "modules.PIXIE.pixielib.models.hrnet",
+ "documentation": {}
+ },
+ {
+ "label": "load_HRNet",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.hrnet",
+ "description": "modules.PIXIE.pixielib.models.hrnet",
+ "peekOfCode": "def load_HRNet(pretrained=False):\n hr_net_cfg_dict = {\n 'use_old_impl': False,\n 'pretrained_layers': ['*'], \n 'stage1': \n {'num_modules': 1, 'num_branches': 1, 'num_blocks': [4], 'num_channels': [64], 'block': 'BOTTLENECK', 'fuse_method': 'SUM'}, \n 'stage2': \n {'num_modules': 1, 'num_branches': 2, 'num_blocks': [4, 4], 'num_channels': [48, 96], 'block': 'BASIC', 'fuse_method': 'SUM'}, \n 'stage3': \n {'num_modules': 4, 'num_branches': 3, 'num_blocks': [4, 4, 4], 'num_channels': [48, 96, 192], 'block': 'BASIC', 'fuse_method': 'SUM'}, ",
+ "detail": "modules.PIXIE.pixielib.models.hrnet",
+ "documentation": {}
+ },
+ {
+ "label": "BN_MOMENTUM",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.hrnet",
+ "description": "modules.PIXIE.pixielib.models.hrnet",
+ "peekOfCode": "BN_MOMENTUM = 0.1\ndef load_HRNet(pretrained=False):\n hr_net_cfg_dict = {\n 'use_old_impl': False,\n 'pretrained_layers': ['*'], \n 'stage1': \n {'num_modules': 1, 'num_branches': 1, 'num_blocks': [4], 'num_channels': [64], 'block': 'BOTTLENECK', 'fuse_method': 'SUM'}, \n 'stage2': \n {'num_modules': 1, 'num_branches': 2, 'num_blocks': [4, 4], 'num_channels': [48, 96], 'block': 'BASIC', 'fuse_method': 'SUM'}, \n 'stage3': ",
+ "detail": "modules.PIXIE.pixielib.models.hrnet",
+ "documentation": {}
+ },
+ {
+ "label": "blocks_dict",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.hrnet",
+ "description": "modules.PIXIE.pixielib.models.hrnet",
+ "peekOfCode": "blocks_dict = {\n 'BASIC': BasicBlock,\n 'BOTTLENECK': Bottleneck\n}\nclass HighResolutionNet(nn.Module):\n def __init__(self, cfg, **kwargs):\n self.inplanes = 64\n super(HighResolutionNet, self).__init__()\n use_old_impl = cfg.get('use_old_impl')\n self.use_old_impl = use_old_impl",
+ "detail": "modules.PIXIE.pixielib.models.hrnet",
+ "documentation": {}
+ },
+ {
+ "label": "JointsFromVerticesSelector",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "class JointsFromVerticesSelector(nn.Module):\n def __init__(self, fname):\n ''' Selects extra joints from vertices\n '''\n super(JointsFromVerticesSelector, self).__init__()\n err_msg = (\n 'Either pass a filename or triangle face ids, names and'\n ' barycentrics')\n assert fname is not None or (\n face_ids is not None and bcs is not None and names is not None",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "Struct",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "class Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "rot_mat_to_euler",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "def rot_mat_to_euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +\n rot_mats[:, 1, 0] * rot_mats[:, 1, 0])\n return torch.atan2(-rot_mats[:, 2, 0], sy)\ndef find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx,\n dynamic_lmk_b_coords,\n head_kin_chain, dtype=torch.float32):\n ''' Compute the faces, barycentric coordinates for the dynamic landmarks",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "find_dynamic_lmk_idx_and_bcoords",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "def find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx,\n dynamic_lmk_b_coords,\n head_kin_chain, dtype=torch.float32):\n ''' Compute the faces, barycentric coordinates for the dynamic landmarks\n To do so, we first compute the rotation of the neck around the y-axis\n and then use a pre-computed look-up table to find the faces and the\n barycentric coordinates that will be used.\n Special thanks to Soubhik Sanyal (soubhik.sanyal@tuebingen.mpg.de)\n for providing the original TensorFlow implementation and for the LUT.\n Parameters",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "vertices2landmarks",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "def vertices2landmarks(vertices, faces, lmk_faces_idx, lmk_bary_coords):\n ''' Calculates landmarks by barycentric interpolation\n Parameters\n ----------\n vertices: torch.tensor BxVx3, dtype = torch.float32\n The tensor of input vertices\n faces: torch.tensor Fx3, dtype = torch.long\n The faces of the mesh\n lmk_faces_idx: torch.tensor L, dtype = torch.long\n The tensor with the indices of the faces used to calculate the",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "lbs",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "def lbs(betas, pose, v_template, shapedirs, posedirs, J_regressor, parents,\n lbs_weights, pose2rot=True, dtype=torch.float32):\n ''' Performs Linear Blend Skinning with the given shape and pose parameters\n Parameters\n ----------\n betas : torch.tensor BxNB\n The tensor of shape parameters\n pose : torch.tensor Bx(J + 1) * 3\n The pose parameters in axis-angle format\n v_template torch.tensor BxVx3",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "vertices2joints",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "def vertices2joints(J_regressor, vertices):\n ''' Calculates the 3D joint locations from the vertices\n Parameters\n ----------\n J_regressor : torch.tensor JxV\n The regressor array that is used to calculate the joints from the\n position of the vertices\n vertices : torch.tensor BxVx3\n The tensor of mesh vertices\n Returns",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "blend_shapes",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "def blend_shapes(betas, shape_disps):\n ''' Calculates the per vertex displacement due to the blend shapes\n Parameters\n ----------\n betas : torch.tensor Bx(num_betas)\n Blend shape coefficients\n shape_disps: torch.tensor Vx3x(num_betas)\n Blend shapes\n Returns\n -------",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "batch_rodrigues",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):\n ''' Calculates the rotation matrices for a batch of rotation vectors\n Parameters\n ----------\n rot_vecs: torch.tensor Nx3\n array of N axis-angle vectors\n Returns\n -------\n R: torch.tensor Nx3x3\n The rotation matrices for the given axis-angle parameters",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "transform_mat",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "def transform_mat(R, t):\n ''' Creates a batch of transformation matrices\n Args:\n - R: Bx3x3 array of a batch of rotation matrices\n - t: Bx3x1 array of a batch of translation vectors\n Returns:\n - T: Bx4x4 Transformation matrix\n '''\n # No padding left or right, only add an extra row\n return torch.cat([F.pad(R, [0, 0, 0, 1]),",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "batch_rigid_transform",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "def batch_rigid_transform(rot_mats, joints, parents, dtype=torch.float32):\n \"\"\"\n Applies a batch of rigid transformations to the joints\n Parameters\n ----------\n rot_mats : torch.tensor BxNx3x3\n Tensor of rotation matrices\n joints : torch.tensor BxNx3\n Locations of joints\n parents : torch.tensor BxN",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "to_tensor",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "def to_tensor(array, dtype=torch.float32):\n if 'torch.tensor' not in str(type(array)):\n return torch.tensor(array, dtype=dtype)\ndef to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "to_np",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.lbs",
+ "description": "modules.PIXIE.pixielib.models.lbs",
+ "peekOfCode": "def to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)",
+ "detail": "modules.PIXIE.pixielib.models.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "TempSoftmaxFusion",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.moderators",
+ "description": "modules.PIXIE.pixielib.models.moderators",
+ "peekOfCode": "class TempSoftmaxFusion(nn.Module):\n def __init__(self, channels = [2048*2, 1024, 1], detach_inputs=False, detach_feature=False):\n super(TempSoftmaxFusion, self).__init__()\n self.detach_inputs = detach_inputs\n self.detach_feature = detach_feature\n # weight\n layers = []\n for l in range(0, len(channels) - 1):\n layers.append(nn.Linear(channels[l], channels[l+1]))\n if l < len(channels) - 2:",
+ "detail": "modules.PIXIE.pixielib.models.moderators",
+ "documentation": {}
+ },
+ {
+ "label": "GumbelSoftmaxFusion",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.moderators",
+ "description": "modules.PIXIE.pixielib.models.moderators",
+ "peekOfCode": "class GumbelSoftmaxFusion(nn.Module):\n def __init__(self, channels = [2048*2, 1024, 1], detach_inputs=False, detach_feature=False):\n super(GumbelSoftmaxFusion, self).__init__()\n self.detach_inputs = detach_inputs\n self.detach_feature = detach_feature\n # weight\n layers = []\n for l in range(0, len(channels) - 1):\n layers.append(nn.Linear(channels[l], channels[l+1]))\n if l < len(channels) - 2:",
+ "detail": "modules.PIXIE.pixielib.models.moderators",
+ "documentation": {}
+ },
+ {
+ "label": "ResNet",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "class ResNet(nn.Module):\n def __init__(self, block, layers, num_classes=1000):\n self.inplanes = 64\n super(ResNet, self).__init__()\n self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,\n bias=False)\n self.bn1 = nn.BatchNorm2d(64)\n self.relu = nn.ReLU(inplace=True)\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n self.layer1 = self._make_layer(block, 64, layers[0])",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "Bottleneck",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "class Bottleneck(nn.Module):\n expansion = 4\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(Bottleneck, self).__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(planes)\n self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "BasicBlock",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "class BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(BasicBlock, self).__init__()\n self.conv1 = conv3x3(inplanes, planes, stride)\n self.bn1 = nn.BatchNorm2d(planes)\n self.relu = nn.ReLU(inplace=True)\n self.conv2 = conv3x3(planes, planes)\n self.bn2 = nn.BatchNorm2d(planes)\n self.downsample = downsample",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "DoubleConv",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "class DoubleConv(nn.Module):\n \"\"\"(convolution => [BN] => ReLU) * 2\"\"\"\n def __init__(self, in_channels, out_channels):\n super().__init__()\n self.double_conv = nn.Sequential(\n nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n nn.BatchNorm2d(out_channels),\n nn.ReLU(inplace=True),\n nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n nn.BatchNorm2d(out_channels),",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "Down",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "class Down(nn.Module):\n \"\"\"Downscaling with maxpool then double conv\"\"\"\n def __init__(self, in_channels, out_channels):\n super().__init__()\n self.maxpool_conv = nn.Sequential(\n nn.MaxPool2d(2),\n DoubleConv(in_channels, out_channels)\n )\n def forward(self, x):\n return self.maxpool_conv(x)",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "Up",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "class Up(nn.Module):\n \"\"\"Upscaling then double conv\"\"\"\n def __init__(self, in_channels, out_channels, bilinear=True):\n super().__init__()\n # if bilinear, use the normal convolutions to reduce the number of channels\n if bilinear:\n self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)\n else:\n self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)\n self.conv = DoubleConv(in_channels, out_channels)",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "OutConv",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "class OutConv(nn.Module):\n def __init__(self, in_channels, out_channels):\n super(OutConv, self).__init__()\n self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)\n def forward(self, x):\n return self.conv(x)\nclass UNet(nn.Module):\n def __init__(self, n_channels, n_classes, bilinear=True):\n super(UNet, self).__init__()\n self.n_channels = n_channels",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "UNet",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "class UNet(nn.Module):\n def __init__(self, n_channels, n_classes, bilinear=True):\n super(UNet, self).__init__()\n self.n_channels = n_channels\n self.n_classes = n_classes\n self.bilinear = bilinear\n self.inc = DoubleConv(n_channels, 64)\n self.down1 = Down(64, 128)\n self.down2 = Down(128, 256)\n self.down3 = Down(256, 512)",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "conv3x3",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "def conv3x3(in_planes, out_planes, stride=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n padding=1, bias=False)\nclass BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(BasicBlock, self).__init__()\n self.conv1 = conv3x3(inplanes, planes, stride)\n self.bn1 = nn.BatchNorm2d(planes)",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "copy_parameter_from_resnet",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "def copy_parameter_from_resnet(model, resnet_dict):\n cur_state_dict = model.state_dict()\n # import ipdb; ipdb.set_trace()\n for name, param in list(resnet_dict.items())[0:None]:\n if name not in cur_state_dict:\n # print(name, ' not available in reconstructed resnet')\n continue\n if isinstance(param, Parameter):\n param = param.data\n try:",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "load_ResNet50Model",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "def load_ResNet50Model():\n model = ResNet(Bottleneck, [3, 4, 6, 3])\n copy_parameter_from_resnet(model, torchvision.models.resnet50(pretrained = True).state_dict())\n return model\ndef load_ResNet101Model():\n model = ResNet(Bottleneck, [3, 4, 23, 3])\n copy_parameter_from_resnet(model, torchvision.models.resnet101(pretrained = True).state_dict())\n return model\ndef load_ResNet152Model():\n model = ResNet(Bottleneck, [3, 8, 36, 3])",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "load_ResNet101Model",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "def load_ResNet101Model():\n model = ResNet(Bottleneck, [3, 4, 23, 3])\n copy_parameter_from_resnet(model, torchvision.models.resnet101(pretrained = True).state_dict())\n return model\ndef load_ResNet152Model():\n model = ResNet(Bottleneck, [3, 8, 36, 3])\n copy_parameter_from_resnet(model, torchvision.models.resnet152(pretrained = True).state_dict())\n return model\n# model.load_state_dict(checkpoint['model_state_dict'])\n######## Unet",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "load_ResNet152Model",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.models.resnet",
+ "description": "modules.PIXIE.pixielib.models.resnet",
+ "peekOfCode": "def load_ResNet152Model():\n model = ResNet(Bottleneck, [3, 8, 36, 3])\n copy_parameter_from_resnet(model, torchvision.models.resnet152(pretrained = True).state_dict())\n return model\n# model.load_state_dict(checkpoint['model_state_dict'])\n######## Unet\nclass DoubleConv(nn.Module):\n \"\"\"(convolution => [BN] => ReLU) * 2\"\"\"\n def __init__(self, in_channels, out_channels):\n super().__init__()",
+ "detail": "modules.PIXIE.pixielib.models.resnet",
+ "documentation": {}
+ },
+ {
+ "label": "SMPLX",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "class SMPLX(nn.Module):\n \"\"\"\n Given smplx parameters, this class generates a differentiable SMPLX function\n which outputs a mesh and 3D joints\n \"\"\"\n def __init__(self, config):\n super(SMPLX, self).__init__()\n print(\"creating the SMPLX Decoder\")\n ss = np.load(config.smplx_model_path, allow_pickle=True)\n smplx_model = Struct(**ss)",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "J14_NAMES",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "J14_NAMES = [\n 'right_ankle',\n 'right_knee',\n 'right_hip',\n 'left_hip',\n 'left_knee',\n 'left_ankle',\n 'right_wrist',\n 'right_elbow',\n 'right_shoulder',",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "SMPLX_names",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "SMPLX_names = ['pelvis', 'left_hip', 'right_hip', 'spine1', 'left_knee', 'right_knee', 'spine2', 'left_ankle', 'right_ankle', 'spine3', 'left_foot', 'right_foot', 'neck', 'left_collar', 'right_collar', 'head', 'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow', 'left_wrist', 'right_wrist', 'jaw', 'left_eye_smplx', 'right_eye_smplx', 'left_index1', 'left_index2', 'left_index3', 'left_middle1', 'left_middle2', 'left_middle3', 'left_pinky1', 'left_pinky2', 'left_pinky3', 'left_ring1', 'left_ring2', 'left_ring3', 'left_thumb1', 'left_thumb2', 'left_thumb3', 'right_index1', 'right_index2', 'right_index3', 'right_middle1', 'right_middle2', 'right_middle3', 'right_pinky1', 'right_pinky2', 'right_pinky3', 'right_ring1', 'right_ring2', 'right_ring3', 'right_thumb1', 'right_thumb2', 'right_thumb3', 'right_eye_brow1', 'right_eye_brow2', 'right_eye_brow3', 'right_eye_brow4', 'right_eye_brow5', 'left_eye_brow5', 'left_eye_brow4', 'left_eye_brow3', 'left_eye_brow2', 'left_eye_brow1', 'nose1', 'nose2', 'nose3', 'nose4', 'right_nose_2', 'right_nose_1', 'nose_middle', 'left_nose_1', 'left_nose_2', 'right_eye1', 'right_eye2', 'right_eye3', 'right_eye4', 'right_eye5', 'right_eye6', 'left_eye4', 'left_eye3', 'left_eye2', 'left_eye1', 'left_eye6', 'left_eye5', 'right_mouth_1', 'right_mouth_2', 'right_mouth_3', 'mouth_top', 'left_mouth_3', 'left_mouth_2', 'left_mouth_1', 'left_mouth_5', 'left_mouth_4', 'mouth_bottom', 'right_mouth_4', 'right_mouth_5', 'right_lip_1', 'right_lip_2', 'lip_top', 'left_lip_2', 'left_lip_1', 'left_lip_3', 'lip_bottom', 'right_lip_3', 'right_contour_1', 'right_contour_2', 'right_contour_3', 'right_contour_4', 'right_contour_5', 'right_contour_6', 'right_contour_7', 'right_contour_8', 'contour_middle', 'left_contour_8', 'left_contour_7', 'left_contour_6', 'left_contour_5', 'left_contour_4', 'left_contour_3', 'left_contour_2', 'left_contour_1', 'head_top', 'left_big_toe', 'left_ear', 'left_eye', 'left_heel', 'left_index', 'left_middle', 'left_pinky', 'left_ring', 'left_small_toe', 'left_thumb', 'nose', 'right_big_toe', 'right_ear', 'right_eye', 'right_heel', 'right_index', 'right_middle', 'right_pinky', 'right_ring', 'right_small_toe', 'right_thumb']\nextra_names = ['head_top', 'left_big_toe', 'left_ear', 'left_eye', 'left_heel', 'left_index', 'left_middle', 'left_pinky', 'left_ring', 'left_small_toe', 'left_thumb', 'nose', 'right_big_toe', 'right_ear', 'right_eye', 'right_heel', 'right_index', 'right_middle', 'right_pinky', 'right_ring', 'right_small_toe', 'right_thumb']\nSMPLX_names += extra_names\npart_indices = {}\npart_indices['body'] = np.array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 123,\n 124, 125, 126, 127, 132, 134, 135, 136, 137, 138, 143])\npart_indices['torso'] = np.array([ 0, 1, 2, 3, 6, 9, 12, 13, 14, 15, 16, 17, 18,\n 19, 22, 23, 24, 55, 56, 57, 58, 59, 76, 77, 78, 79,\n 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "extra_names",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "extra_names = ['head_top', 'left_big_toe', 'left_ear', 'left_eye', 'left_heel', 'left_index', 'left_middle', 'left_pinky', 'left_ring', 'left_small_toe', 'left_thumb', 'nose', 'right_big_toe', 'right_ear', 'right_eye', 'right_heel', 'right_index', 'right_middle', 'right_pinky', 'right_ring', 'right_small_toe', 'right_thumb']\nSMPLX_names += extra_names\npart_indices = {}\npart_indices['body'] = np.array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 123,\n 124, 125, 126, 127, 132, 134, 135, 136, 137, 138, 143])\npart_indices['torso'] = np.array([ 0, 1, 2, 3, 6, 9, 12, 13, 14, 15, 16, 17, 18,\n 19, 22, 23, 24, 55, 56, 57, 58, 59, 76, 77, 78, 79,\n 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "part_indices",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "part_indices = {}\npart_indices['body'] = np.array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 123,\n 124, 125, 126, 127, 132, 134, 135, 136, 137, 138, 143])\npart_indices['torso'] = np.array([ 0, 1, 2, 3, 6, 9, 12, 13, 14, 15, 16, 17, 18,\n 19, 22, 23, 24, 55, 56, 57, 58, 59, 76, 77, 78, 79,\n 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,\n 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,\n 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "part_indices['body']",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "part_indices['body'] = np.array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 123,\n 124, 125, 126, 127, 132, 134, 135, 136, 137, 138, 143])\npart_indices['torso'] = np.array([ 0, 1, 2, 3, 6, 9, 12, 13, 14, 15, 16, 17, 18,\n 19, 22, 23, 24, 55, 56, 57, 58, 59, 76, 77, 78, 79,\n 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,\n 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,\n 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,\n 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144])",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "part_indices['torso']",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "part_indices['torso'] = np.array([ 0, 1, 2, 3, 6, 9, 12, 13, 14, 15, 16, 17, 18,\n 19, 22, 23, 24, 55, 56, 57, 58, 59, 76, 77, 78, 79,\n 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,\n 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,\n 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,\n 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144])\npart_indices['head'] = np.array([ 12, 15, 22, 23, 24, 55, 56, 57, 58, 59, 60, 61, 62,\n 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,\n 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "part_indices['head']",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "part_indices['head'] = np.array([ 12, 15, 22, 23, 24, 55, 56, 57, 58, 59, 60, 61, 62,\n 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,\n 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,\n 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101,\n 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114,\n 115, 116, 117, 118, 119, 120, 121, 122, 123, 125, 126, 134, 136,\n 137])\npart_indices['face'] = np.array([ 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,\n 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,\n 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "part_indices['face']",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "part_indices['face'] = np.array([ 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,\n 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,\n 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,\n 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,\n 119, 120, 121, 122])\npart_indices['upper'] = np.array([ 12, 13, 14, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,\n 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,\n 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "part_indices['upper']",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "part_indices['upper'] = np.array([ 12, 13, 14, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,\n 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,\n 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,\n 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,\n 119, 120, 121, 122])\npart_indices['hand'] = np.array([ 20, 21, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,\n 49, 50, 51, 52, 53, 54, 128, 129, 130, 131, 133, 139, 140,\n 141, 142, 144])",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "part_indices['hand']",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "part_indices['hand'] = np.array([ 20, 21, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,\n 49, 50, 51, 52, 53, 54, 128, 129, 130, 131, 133, 139, 140,\n 141, 142, 144])\npart_indices['left_hand'] = np.array([ 20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n 37, 38, 39, 128, 129, 130, 131, 133])\npart_indices['right_hand'] = np.array([ 21, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n 52, 53, 54, 139, 140, 141, 142, 144])\n# kinematic tree \nhead_kin_chain = [15,12,9,6,3,0]",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "part_indices['left_hand']",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "part_indices['left_hand'] = np.array([ 20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\n 37, 38, 39, 128, 129, 130, 131, 133])\npart_indices['right_hand'] = np.array([ 21, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n 52, 53, 54, 139, 140, 141, 142, 144])\n# kinematic tree \nhead_kin_chain = [15,12,9,6,3,0]\n#--smplx joints\n# 00 - Global\n# 01 - L_Thigh\n# 02 - R_Thigh",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "part_indices['right_hand']",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "part_indices['right_hand'] = np.array([ 21, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n 52, 53, 54, 139, 140, 141, 142, 144])\n# kinematic tree \nhead_kin_chain = [15,12,9,6,3,0]\n#--smplx joints\n# 00 - Global\n# 01 - L_Thigh\n# 02 - R_Thigh\n# 03 - Spine\n# 04 - L_Calf",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "head_kin_chain",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.models.SMPLX",
+ "description": "modules.PIXIE.pixielib.models.SMPLX",
+ "peekOfCode": "head_kin_chain = [15,12,9,6,3,0]\n#--smplx joints\n# 00 - Global\n# 01 - L_Thigh\n# 02 - R_Thigh\n# 03 - Spine\n# 04 - L_Calf\n# 05 - R_Calf\n# 06 - Spine1\n# 07 - L_Foot",
+ "detail": "modules.PIXIE.pixielib.models.SMPLX",
+ "documentation": {}
+ },
+ {
+ "label": "os.environ[\"CC\"]",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.rasterizer.setup",
+ "description": "modules.PIXIE.pixielib.utils.rasterizer.setup",
+ "peekOfCode": "os.environ[\"CC\"] = \"gcc-7\"\nos.environ[\"CXX\"] = \"gcc-7\"\nUSE_NINJA = os.getenv('USE_NINJA') == '1'\nsetup(\n name='standard_rasterize_cuda',\n ext_modules=[\n\tCUDAExtension('standard_rasterize_cuda', [\n 'standard_rasterize_cuda.cpp',\n 'standard_rasterize_cuda_kernel.cu',\n ])",
+ "detail": "modules.PIXIE.pixielib.utils.rasterizer.setup",
+ "documentation": {}
+ },
+ {
+ "label": "os.environ[\"CXX\"]",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.rasterizer.setup",
+ "description": "modules.PIXIE.pixielib.utils.rasterizer.setup",
+ "peekOfCode": "os.environ[\"CXX\"] = \"gcc-7\"\nUSE_NINJA = os.getenv('USE_NINJA') == '1'\nsetup(\n name='standard_rasterize_cuda',\n ext_modules=[\n\tCUDAExtension('standard_rasterize_cuda', [\n 'standard_rasterize_cuda.cpp',\n 'standard_rasterize_cuda_kernel.cu',\n ])\n\t],",
+ "detail": "modules.PIXIE.pixielib.utils.rasterizer.setup",
+ "documentation": {}
+ },
+ {
+ "label": "USE_NINJA",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.rasterizer.setup",
+ "description": "modules.PIXIE.pixielib.utils.rasterizer.setup",
+ "peekOfCode": "USE_NINJA = os.getenv('USE_NINJA') == '1'\nsetup(\n name='standard_rasterize_cuda',\n ext_modules=[\n\tCUDAExtension('standard_rasterize_cuda', [\n 'standard_rasterize_cuda.cpp',\n 'standard_rasterize_cuda_kernel.cu',\n ])\n\t],\n cmdclass={'build_ext': BuildExtension.with_options(use_ninja=USE_NINJA)}",
+ "detail": "modules.PIXIE.pixielib.utils.rasterizer.setup",
+ "documentation": {}
+ },
+ {
+ "label": "Cropper",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.utils.array_cropper",
+ "description": "modules.PIXIE.pixielib.utils.array_cropper",
+ "peekOfCode": "class Cropper(object):\n def __init__(self, crop_size, scale=[1,1], trans_scale = 0.):\n self.crop_size = crop_size\n self.scale = scale\n self.trans_scale = trans_scale\n def crop(self, image, points, points_scale=None):\n # points to bbox\n center, bbox_size = points2bbox(points, points_scale)\n # argument bbox. \n center, bbox_size = augment_bbox(center, bbox_size, scale=self.scale, trans_scale=self.trans_scale)",
+ "detail": "modules.PIXIE.pixielib.utils.array_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "points2bbox",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.array_cropper",
+ "description": "modules.PIXIE.pixielib.utils.array_cropper",
+ "peekOfCode": "def points2bbox(points, points_scale=None):\n # recover range\n if points_scale:\n points[:,0] = points[:,0]*points_scale[1]/2 + points_scale[1]/2\n points[:,1] = points[:,1]*points_scale[0]/2 + points_scale[0]/2\n left = np.min(points[:,0]); right = np.max(points[:,0]); \n top = np.min(points[:,1]); bottom = np.max(points[:,1])\n size = max(right - left, bottom - top)\n center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0 ])#+ old_size*0.1])\n return center, size",
+ "detail": "modules.PIXIE.pixielib.utils.array_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "augment_bbox",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.array_cropper",
+ "description": "modules.PIXIE.pixielib.utils.array_cropper",
+ "peekOfCode": "def augment_bbox(center, bbox_size, scale=[1.0, 1.0], trans_scale=0.):\n trans_scale = (np.random.rand(2)*2 -1) * trans_scale\n center = center + trans_scale*bbox_size # 0.5\n scale = np.random.rand() * (scale[1] - scale[0]) + scale[0]\n size = int(bbox_size*scale)\n return center, size\ndef crop_array(image, center, bboxsize, crop_size):\n ''' for single image only\n Args:\n image (numpy.Array): the reference array of shape HxWXC.",
+ "detail": "modules.PIXIE.pixielib.utils.array_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "crop_array",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.array_cropper",
+ "description": "modules.PIXIE.pixielib.utils.array_cropper",
+ "peekOfCode": "def crop_array(image, center, bboxsize, crop_size):\n ''' for single image only\n Args:\n image (numpy.Array): the reference array of shape HxWXC.\n size (Tuple[int, int]): a tuple with the height and width that will be\n used to resize the extracted patches.\n Returns:\n cropped_image\n tform: 3x3 affine matrix\n '''",
+ "detail": "modules.PIXIE.pixielib.utils.array_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "get_cfg_defaults",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "def get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n # cfg.merge_from_file(cfg_file, allow_unsafe=True)\n cfg.merge_from_file(cfg_file)\n return cfg.clone()\ndef parse_args():",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "update_cfg",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "def update_cfg(cfg, cfg_file):\n # cfg.merge_from_file(cfg_file, allow_unsafe=True)\n cfg.merge_from_file(cfg_file)\n return cfg.clone()\ndef parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--cfg', type=str, help='cfg file path')\n args = parser.parse_args()\n cfg = get_cfg_defaults()\n if args.cfg is not None:",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "parse_args",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--cfg', type=str, help='cfg file path')\n args = parser.parse_args()\n cfg = get_cfg_defaults()\n if args.cfg is not None:\n cfg_file = args.cfg\n cfg = update_cfg(cfg, args.cfg)\n cfg.cfg_file = cfg_file\n return cfg",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg = CN()\n# abs_pixie_dir-->\n# data-->\n# deca_model.tar\n# ......\nabs_pixie_dir = os.path.abspath(os.path.join(\n os.path.dirname(__file__), '../../../../../models/models_pixie'))\ncfg.pixie_dir = abs_pixie_dir\ncfg.device = 'cuda'\ncfg.device_id = '0'",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "abs_pixie_dir",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "abs_pixie_dir = os.path.abspath(os.path.join(\n os.path.dirname(__file__), '../../../../../models/models_pixie'))\ncfg.pixie_dir = abs_pixie_dir\ncfg.device = 'cuda'\ncfg.device_id = '0'\ncfg.pretrained_modelpath = os.path.join(cfg.pixie_dir, 'data', 'pixie_model.tar')\n# cfg.pretrained_modelpath = '/ps/scratch/yfeng/Data/Projects-data/BodyHead/cluster_training/1113_final_newcoco_all_2_together/all[1.0]_betas[0.0]_pose[5.0]_pose[0.1]_3d[0.5]_body[5.0]_head[1.0]_hand[1.0]_shape[0.001]_pose[0.1]_prior[True]_reg[12.0]/model.tar'\n# cfg.pretrained_modelpath = '/ps/scratch/yfeng/Data/Projects-data/BodyHead/cluster_training/1204_newtex_head_only/size[8]_min[1.8]_max[2.2]_scale[0.3]_step[2]_eyed[1.0]_lipd[0.5]_exp[0.0001]_shape[0.0001]_tex[5e-05]_prior[True]_useWlmk[False]_/model.tar'\n## smplx parameter settings\ncfg.params = CN()",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.pixie_dir",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.pixie_dir = abs_pixie_dir\ncfg.device = 'cuda'\ncfg.device_id = '0'\ncfg.pretrained_modelpath = os.path.join(cfg.pixie_dir, 'data', 'pixie_model.tar')\n# cfg.pretrained_modelpath = '/ps/scratch/yfeng/Data/Projects-data/BodyHead/cluster_training/1113_final_newcoco_all_2_together/all[1.0]_betas[0.0]_pose[5.0]_pose[0.1]_3d[0.5]_body[5.0]_head[1.0]_hand[1.0]_shape[0.001]_pose[0.1]_prior[True]_reg[12.0]/model.tar'\n# cfg.pretrained_modelpath = '/ps/scratch/yfeng/Data/Projects-data/BodyHead/cluster_training/1204_newtex_head_only/size[8]_min[1.8]_max[2.2]_scale[0.3]_step[2]_eyed[1.0]_lipd[0.5]_exp[0.0001]_shape[0.0001]_tex[5e-05]_prior[True]_useWlmk[False]_/model.tar'\n## smplx parameter settings\ncfg.params = CN()\ncfg.params.body_list = ['body_cam', 'global_pose', 'partbody_pose', 'neck_pose']\ncfg.params.head_list = ['head_cam', 'tex', 'light']",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.device",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.device = 'cuda'\ncfg.device_id = '0'\ncfg.pretrained_modelpath = os.path.join(cfg.pixie_dir, 'data', 'pixie_model.tar')\n# cfg.pretrained_modelpath = '/ps/scratch/yfeng/Data/Projects-data/BodyHead/cluster_training/1113_final_newcoco_all_2_together/all[1.0]_betas[0.0]_pose[5.0]_pose[0.1]_3d[0.5]_body[5.0]_head[1.0]_hand[1.0]_shape[0.001]_pose[0.1]_prior[True]_reg[12.0]/model.tar'\n# cfg.pretrained_modelpath = '/ps/scratch/yfeng/Data/Projects-data/BodyHead/cluster_training/1204_newtex_head_only/size[8]_min[1.8]_max[2.2]_scale[0.3]_step[2]_eyed[1.0]_lipd[0.5]_exp[0.0001]_shape[0.0001]_tex[5e-05]_prior[True]_useWlmk[False]_/model.tar'\n## smplx parameter settings\ncfg.params = CN()\ncfg.params.body_list = ['body_cam', 'global_pose', 'partbody_pose', 'neck_pose']\ncfg.params.head_list = ['head_cam', 'tex', 'light']\ncfg.params.head_share_list = ['shape', 'exp', 'head_pose', 'jaw_pose']",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.device_id",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.device_id = '0'\ncfg.pretrained_modelpath = os.path.join(cfg.pixie_dir, 'data', 'pixie_model.tar')\n# cfg.pretrained_modelpath = '/ps/scratch/yfeng/Data/Projects-data/BodyHead/cluster_training/1113_final_newcoco_all_2_together/all[1.0]_betas[0.0]_pose[5.0]_pose[0.1]_3d[0.5]_body[5.0]_head[1.0]_hand[1.0]_shape[0.001]_pose[0.1]_prior[True]_reg[12.0]/model.tar'\n# cfg.pretrained_modelpath = '/ps/scratch/yfeng/Data/Projects-data/BodyHead/cluster_training/1204_newtex_head_only/size[8]_min[1.8]_max[2.2]_scale[0.3]_step[2]_eyed[1.0]_lipd[0.5]_exp[0.0001]_shape[0.0001]_tex[5e-05]_prior[True]_useWlmk[False]_/model.tar'\n## smplx parameter settings\ncfg.params = CN()\ncfg.params.body_list = ['body_cam', 'global_pose', 'partbody_pose', 'neck_pose']\ncfg.params.head_list = ['head_cam', 'tex', 'light']\ncfg.params.head_share_list = ['shape', 'exp', 'head_pose', 'jaw_pose']\ncfg.params.hand_list = ['hand_cam']",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.pretrained_modelpath",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.pretrained_modelpath = os.path.join(cfg.pixie_dir, 'data', 'pixie_model.tar')\n# cfg.pretrained_modelpath = '/ps/scratch/yfeng/Data/Projects-data/BodyHead/cluster_training/1113_final_newcoco_all_2_together/all[1.0]_betas[0.0]_pose[5.0]_pose[0.1]_3d[0.5]_body[5.0]_head[1.0]_hand[1.0]_shape[0.001]_pose[0.1]_prior[True]_reg[12.0]/model.tar'\n# cfg.pretrained_modelpath = '/ps/scratch/yfeng/Data/Projects-data/BodyHead/cluster_training/1204_newtex_head_only/size[8]_min[1.8]_max[2.2]_scale[0.3]_step[2]_eyed[1.0]_lipd[0.5]_exp[0.0001]_shape[0.0001]_tex[5e-05]_prior[True]_useWlmk[False]_/model.tar'\n## smplx parameter settings\ncfg.params = CN()\ncfg.params.body_list = ['body_cam', 'global_pose', 'partbody_pose', 'neck_pose']\ncfg.params.head_list = ['head_cam', 'tex', 'light']\ncfg.params.head_share_list = ['shape', 'exp', 'head_pose', 'jaw_pose']\ncfg.params.hand_list = ['hand_cam']\ncfg.params.hand_share_list = ['right_wrist_pose', 'right_hand_pose'] #only for right hand",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.params",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.params = CN()\ncfg.params.body_list = ['body_cam', 'global_pose', 'partbody_pose', 'neck_pose']\ncfg.params.head_list = ['head_cam', 'tex', 'light']\ncfg.params.head_share_list = ['shape', 'exp', 'head_pose', 'jaw_pose']\ncfg.params.hand_list = ['hand_cam']\ncfg.params.hand_share_list = ['right_wrist_pose', 'right_hand_pose'] #only for right hand\n# ---------------------------------------------------------------------------- #\n# Options for Body model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.params.body_list",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.params.body_list = ['body_cam', 'global_pose', 'partbody_pose', 'neck_pose']\ncfg.params.head_list = ['head_cam', 'tex', 'light']\ncfg.params.head_share_list = ['shape', 'exp', 'head_pose', 'jaw_pose']\ncfg.params.hand_list = ['hand_cam']\ncfg.params.hand_share_list = ['right_wrist_pose', 'right_hand_pose'] #only for right hand\n# ---------------------------------------------------------------------------- #\n# Options for Body model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()\ncfg.model.tex_path = os.path.join(cfg.pixie_dir, 'data', 'FLAME_albedo_from_BFM.npz')",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.params.head_list",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.params.head_list = ['head_cam', 'tex', 'light']\ncfg.params.head_share_list = ['shape', 'exp', 'head_pose', 'jaw_pose']\ncfg.params.hand_list = ['hand_cam']\ncfg.params.hand_share_list = ['right_wrist_pose', 'right_hand_pose'] #only for right hand\n# ---------------------------------------------------------------------------- #\n# Options for Body model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()\ncfg.model.tex_path = os.path.join(cfg.pixie_dir, 'data', 'FLAME_albedo_from_BFM.npz')\ncfg.model.smplx_model_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_NEUTRAL_2020.npz')",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.params.head_share_list",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.params.head_share_list = ['shape', 'exp', 'head_pose', 'jaw_pose']\ncfg.params.hand_list = ['hand_cam']\ncfg.params.hand_share_list = ['right_wrist_pose', 'right_hand_pose'] #only for right hand\n# ---------------------------------------------------------------------------- #\n# Options for Body model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()\ncfg.model.tex_path = os.path.join(cfg.pixie_dir, 'data', 'FLAME_albedo_from_BFM.npz')\ncfg.model.smplx_model_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_NEUTRAL_2020.npz')\ncfg.model.topology_path = os.path.join(cfg.pixie_dir, 'data', 'SMPL_X_template_FLAME_uv.obj') ",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.params.hand_list",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.params.hand_list = ['hand_cam']\ncfg.params.hand_share_list = ['right_wrist_pose', 'right_hand_pose'] #only for right hand\n# ---------------------------------------------------------------------------- #\n# Options for Body model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()\ncfg.model.tex_path = os.path.join(cfg.pixie_dir, 'data', 'FLAME_albedo_from_BFM.npz')\ncfg.model.smplx_model_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_NEUTRAL_2020.npz')\ncfg.model.topology_path = os.path.join(cfg.pixie_dir, 'data', 'SMPL_X_template_FLAME_uv.obj') \ncfg.model.topology_smplxtex_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_tex.obj')",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.params.hand_share_list",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.params.hand_share_list = ['right_wrist_pose', 'right_hand_pose'] #only for right hand\n# ---------------------------------------------------------------------------- #\n# Options for Body model\n# ---------------------------------------------------------------------------- #\ncfg.model = CN()\ncfg.model.tex_path = os.path.join(cfg.pixie_dir, 'data', 'FLAME_albedo_from_BFM.npz')\ncfg.model.smplx_model_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_NEUTRAL_2020.npz')\ncfg.model.topology_path = os.path.join(cfg.pixie_dir, 'data', 'SMPL_X_template_FLAME_uv.obj') \ncfg.model.topology_smplxtex_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_tex.obj')\ncfg.model.topology_smplx_hand_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_hand.obj')",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model = CN()\ncfg.model.tex_path = os.path.join(cfg.pixie_dir, 'data', 'FLAME_albedo_from_BFM.npz')\ncfg.model.smplx_model_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_NEUTRAL_2020.npz')\ncfg.model.topology_path = os.path.join(cfg.pixie_dir, 'data', 'SMPL_X_template_FLAME_uv.obj') \ncfg.model.topology_smplxtex_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_tex.obj')\ncfg.model.topology_smplx_hand_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_hand.obj')\ncfg.model.face_mask_path = os.path.join(cfg.pixie_dir, 'data', 'uv_face_mask.png')\ncfg.model.face_eye_mask_path = os.path.join(cfg.pixie_dir, 'data', 'uv_face_eye_mask.png')\ncfg.model.extra_joint_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_extra_joints.yaml')\ncfg.model.j14_regressor_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_to_J14.pkl')",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.tex_path",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.tex_path = os.path.join(cfg.pixie_dir, 'data', 'FLAME_albedo_from_BFM.npz')\ncfg.model.smplx_model_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_NEUTRAL_2020.npz')\ncfg.model.topology_path = os.path.join(cfg.pixie_dir, 'data', 'SMPL_X_template_FLAME_uv.obj') \ncfg.model.topology_smplxtex_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_tex.obj')\ncfg.model.topology_smplx_hand_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_hand.obj')\ncfg.model.face_mask_path = os.path.join(cfg.pixie_dir, 'data', 'uv_face_mask.png')\ncfg.model.face_eye_mask_path = os.path.join(cfg.pixie_dir, 'data', 'uv_face_eye_mask.png')\ncfg.model.extra_joint_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_extra_joints.yaml')\ncfg.model.j14_regressor_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_to_J14.pkl')\ncfg.model.flame2smplx_cached_path = os.path.join(cfg.pixie_dir, 'data', 'flame2smplx_tex_1024.npy')",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.smplx_model_path",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.smplx_model_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_NEUTRAL_2020.npz')\ncfg.model.topology_path = os.path.join(cfg.pixie_dir, 'data', 'SMPL_X_template_FLAME_uv.obj') \ncfg.model.topology_smplxtex_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_tex.obj')\ncfg.model.topology_smplx_hand_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_hand.obj')\ncfg.model.face_mask_path = os.path.join(cfg.pixie_dir, 'data', 'uv_face_mask.png')\ncfg.model.face_eye_mask_path = os.path.join(cfg.pixie_dir, 'data', 'uv_face_eye_mask.png')\ncfg.model.extra_joint_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_extra_joints.yaml')\ncfg.model.j14_regressor_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_to_J14.pkl')\ncfg.model.flame2smplx_cached_path = os.path.join(cfg.pixie_dir, 'data', 'flame2smplx_tex_1024.npy')\ncfg.model.smplx_tex_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_tex.png')",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.topology_path",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.topology_path = os.path.join(cfg.pixie_dir, 'data', 'SMPL_X_template_FLAME_uv.obj') \ncfg.model.topology_smplxtex_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_tex.obj')\ncfg.model.topology_smplx_hand_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_hand.obj')\ncfg.model.face_mask_path = os.path.join(cfg.pixie_dir, 'data', 'uv_face_mask.png')\ncfg.model.face_eye_mask_path = os.path.join(cfg.pixie_dir, 'data', 'uv_face_eye_mask.png')\ncfg.model.extra_joint_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_extra_joints.yaml')\ncfg.model.j14_regressor_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_to_J14.pkl')\ncfg.model.flame2smplx_cached_path = os.path.join(cfg.pixie_dir, 'data', 'flame2smplx_tex_1024.npy')\ncfg.model.smplx_tex_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_tex.png')\ncfg.model.mano_ids_path = os.path.join(cfg.pixie_dir, 'data', 'MANO_SMPLX_vertex_ids.pkl')",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.topology_smplxtex_path",
+ "kind": 5,
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+ "peekOfCode": "cfg.model.topology_smplxtex_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_tex.obj')\ncfg.model.topology_smplx_hand_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_hand.obj')\ncfg.model.face_mask_path = os.path.join(cfg.pixie_dir, 'data', 'uv_face_mask.png')\ncfg.model.face_eye_mask_path = os.path.join(cfg.pixie_dir, 'data', 'uv_face_eye_mask.png')\ncfg.model.extra_joint_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_extra_joints.yaml')\ncfg.model.j14_regressor_path = os.path.join(cfg.pixie_dir, 'data', 'SMPLX_to_J14.pkl')\ncfg.model.flame2smplx_cached_path = os.path.join(cfg.pixie_dir, 'data', 'flame2smplx_tex_1024.npy')\ncfg.model.smplx_tex_path = os.path.join(cfg.pixie_dir, 'data', 'smplx_tex.png')\ncfg.model.mano_ids_path = os.path.join(cfg.pixie_dir, 'data', 'MANO_SMPLX_vertex_ids.pkl')\ncfg.model.flame_ids_path = os.path.join(cfg.pixie_dir, 'data', 'SMPL-X__FLAME_vertex_ids.npy')",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.topology_smplx_hand_path",
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+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.face_mask_path",
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+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.face_eye_mask_path",
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+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.extra_joint_path",
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+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ },
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.flame2smplx_cached_path",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.smplx_tex_path",
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+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.mano_ids_path",
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+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.flame_ids_path",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ "peekOfCode": "cfg.model.flame_ids_path = os.path.join(cfg.pixie_dir, 'data', 'SMPL-X__FLAME_vertex_ids.npy')\ncfg.model.uv_size = 256\n# cfg.model.n_shape = 10\ncfg.model.n_shape = 200\ncfg.model.n_tex = 50\n# cfg.model.n_exp = 10\ncfg.model.n_exp = 50\ncfg.model.n_body_cam = 3\ncfg.model.n_head_cam = 3\ncfg.model.n_hand_cam = 3",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.uv_size",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.uv_size = 256\n# cfg.model.n_shape = 10\ncfg.model.n_shape = 200\ncfg.model.n_tex = 50\n# cfg.model.n_exp = 10\ncfg.model.n_exp = 50\ncfg.model.n_body_cam = 3\ncfg.model.n_head_cam = 3\ncfg.model.n_hand_cam = 3\ncfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_shape",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.n_shape = 200\ncfg.model.n_tex = 50\n# cfg.model.n_exp = 10\ncfg.model.n_exp = 50\ncfg.model.n_body_cam = 3\ncfg.model.n_head_cam = 3\ncfg.model.n_hand_cam = 3\ncfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM\ncfg.model.uvtex_type = 'SMPLX' # FLAME or SMPLX\ncfg.model.use_tex = False #True # whether to use flame texture model",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_tex",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.n_tex = 50\n# cfg.model.n_exp = 10\ncfg.model.n_exp = 50\ncfg.model.n_body_cam = 3\ncfg.model.n_head_cam = 3\ncfg.model.n_hand_cam = 3\ncfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM\ncfg.model.uvtex_type = 'SMPLX' # FLAME or SMPLX\ncfg.model.use_tex = False #True # whether to use flame texture model\ncfg.model.flame_tex_path = '/ps/scratch/yfeng/Data/FLAME/texture/FLAME_texture.npz'",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_exp",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.n_exp = 50\ncfg.model.n_body_cam = 3\ncfg.model.n_head_cam = 3\ncfg.model.n_hand_cam = 3\ncfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM\ncfg.model.uvtex_type = 'SMPLX' # FLAME or SMPLX\ncfg.model.use_tex = False #True # whether to use flame texture model\ncfg.model.flame_tex_path = '/ps/scratch/yfeng/Data/FLAME/texture/FLAME_texture.npz'\n# pose\ncfg.model.n_global_pose = 3*2",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_body_cam",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.n_body_cam = 3\ncfg.model.n_head_cam = 3\ncfg.model.n_hand_cam = 3\ncfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM\ncfg.model.uvtex_type = 'SMPLX' # FLAME or SMPLX\ncfg.model.use_tex = False #True # whether to use flame texture model\ncfg.model.flame_tex_path = '/ps/scratch/yfeng/Data/FLAME/texture/FLAME_texture.npz'\n# pose\ncfg.model.n_global_pose = 3*2\ncfg.model.n_head_pose = 3*2",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_head_cam",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.n_head_cam = 3\ncfg.model.n_hand_cam = 3\ncfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM\ncfg.model.uvtex_type = 'SMPLX' # FLAME or SMPLX\ncfg.model.use_tex = False #True # whether to use flame texture model\ncfg.model.flame_tex_path = '/ps/scratch/yfeng/Data/FLAME/texture/FLAME_texture.npz'\n# pose\ncfg.model.n_global_pose = 3*2\ncfg.model.n_head_pose = 3*2\ncfg.model.n_neck_pose = 3*2",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_hand_cam",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.n_hand_cam = 3\ncfg.model.tex_type = 'BFM' # BFM, FLAME, albedoMM\ncfg.model.uvtex_type = 'SMPLX' # FLAME or SMPLX\ncfg.model.use_tex = False #True # whether to use flame texture model\ncfg.model.flame_tex_path = '/ps/scratch/yfeng/Data/FLAME/texture/FLAME_texture.npz'\n# pose\ncfg.model.n_global_pose = 3*2\ncfg.model.n_head_pose = 3*2\ncfg.model.n_neck_pose = 3*2\ncfg.model.n_jaw_pose = 3 #euler angle",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.tex_type",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.uvtex_type",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.uvtex_type = 'SMPLX' # FLAME or SMPLX\ncfg.model.use_tex = False #True # whether to use flame texture model\ncfg.model.flame_tex_path = '/ps/scratch/yfeng/Data/FLAME/texture/FLAME_texture.npz'\n# pose\ncfg.model.n_global_pose = 3*2\ncfg.model.n_head_pose = 3*2\ncfg.model.n_neck_pose = 3*2\ncfg.model.n_jaw_pose = 3 #euler angle\ncfg.model.n_body_pose = 21*3*2\ncfg.model.n_partbody_pose = (21-4)*3*2",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.use_tex",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.model.use_tex = False #True # whether to use flame texture model\ncfg.model.flame_tex_path = '/ps/scratch/yfeng/Data/FLAME/texture/FLAME_texture.npz'\n# pose\ncfg.model.n_global_pose = 3*2\ncfg.model.n_head_pose = 3*2\ncfg.model.n_neck_pose = 3*2\ncfg.model.n_jaw_pose = 3 #euler angle\ncfg.model.n_body_pose = 21*3*2\ncfg.model.n_partbody_pose = (21-4)*3*2\ncfg.model.n_left_hand_pose = 15*3*2",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.flame_tex_path",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ "peekOfCode": "cfg.model.flame_tex_path = '/ps/scratch/yfeng/Data/FLAME/texture/FLAME_texture.npz'\n# pose\ncfg.model.n_global_pose = 3*2\ncfg.model.n_head_pose = 3*2\ncfg.model.n_neck_pose = 3*2\ncfg.model.n_jaw_pose = 3 #euler angle\ncfg.model.n_body_pose = 21*3*2\ncfg.model.n_partbody_pose = (21-4)*3*2\ncfg.model.n_left_hand_pose = 15*3*2\ncfg.model.n_right_hand_pose = 15*3*2",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_global_pose",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_head_pose",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ "peekOfCode": "cfg.model.n_head_pose = 3*2\ncfg.model.n_neck_pose = 3*2\ncfg.model.n_jaw_pose = 3 #euler angle\ncfg.model.n_body_pose = 21*3*2\ncfg.model.n_partbody_pose = (21-4)*3*2\ncfg.model.n_left_hand_pose = 15*3*2\ncfg.model.n_right_hand_pose = 15*3*2\ncfg.model.n_left_wrist_pose = 1*3*2\ncfg.model.n_right_wrist_pose = 1*3*2\ncfg.model.n_light = 27",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_neck_pose",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_jaw_pose",
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+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_body_pose",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_partbody_pose",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.model.n_right_hand_pose",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
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+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
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+ "label": "cfg.dataset.head.num_workers",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
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+ "detail": "modules.PIXIE.pixielib.utils.config",
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+ "documentation": {}
+ },
+ {
+ "label": "cfg.dataset.body.image_size",
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+ "documentation": {}
+ },
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+ },
+ {
+ "label": "cfg.dataset.hand",
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+ "documentation": {}
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+ "label": "cfg.dataset.hand.batch_size",
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+ "documentation": {}
+ },
+ {
+ "label": "cfg.dataset.hand.num_workers",
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+ "documentation": {}
+ },
+ {
+ "label": "cfg.dataset.hand.image_size",
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+ "documentation": {}
+ },
+ {
+ "label": "cfg.dataset.hand.image_hd_size",
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+ "documentation": {}
+ },
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+ "detail": "modules.PIXIE.pixielib.utils.config",
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+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.extractor.left_hand_share.type = 'mlp'\ncfg.network.extractor.left_hand_share.channels = []\ncfg.network.extractor.right_hand_share = CN()\ncfg.network.extractor.right_hand_share.type = 'mlp'\ncfg.network.extractor.right_hand_share.channels = []\ncfg.network.moderator = CN()\ncfg.network.moderator.head_share = CN()\ncfg.network.moderator.head_share.detach_inputs = False\ncfg.network.moderator.head_share.detach_feature = False\ncfg.network.moderator.head_share.type = 'temp-softmax'",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.extractor.left_hand_share.channels",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.extractor.left_hand_share.channels = []\ncfg.network.extractor.right_hand_share = CN()\ncfg.network.extractor.right_hand_share.type = 'mlp'\ncfg.network.extractor.right_hand_share.channels = []\ncfg.network.moderator = CN()\ncfg.network.moderator.head_share = CN()\ncfg.network.moderator.head_share.detach_inputs = False\ncfg.network.moderator.head_share.detach_feature = False\ncfg.network.moderator.head_share.type = 'temp-softmax'\ncfg.network.moderator.head_share.channels = [1024, 1024]",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.extractor.right_hand_share",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.extractor.right_hand_share = CN()\ncfg.network.extractor.right_hand_share.type = 'mlp'\ncfg.network.extractor.right_hand_share.channels = []\ncfg.network.moderator = CN()\ncfg.network.moderator.head_share = CN()\ncfg.network.moderator.head_share.detach_inputs = False\ncfg.network.moderator.head_share.detach_feature = False\ncfg.network.moderator.head_share.type = 'temp-softmax'\ncfg.network.moderator.head_share.channels = [1024, 1024]\ncfg.network.moderator.head_share.reduction = 4",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.extractor.right_hand_share.type",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.extractor.right_hand_share.type = 'mlp'\ncfg.network.extractor.right_hand_share.channels = []\ncfg.network.moderator = CN()\ncfg.network.moderator.head_share = CN()\ncfg.network.moderator.head_share.detach_inputs = False\ncfg.network.moderator.head_share.detach_feature = False\ncfg.network.moderator.head_share.type = 'temp-softmax'\ncfg.network.moderator.head_share.channels = [1024, 1024]\ncfg.network.moderator.head_share.reduction = 4\ncfg.network.moderator.head_share.scale_type = 'scalars'",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.extractor.right_hand_share.channels",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.extractor.right_hand_share.channels = []\ncfg.network.moderator = CN()\ncfg.network.moderator.head_share = CN()\ncfg.network.moderator.head_share.detach_inputs = False\ncfg.network.moderator.head_share.detach_feature = False\ncfg.network.moderator.head_share.type = 'temp-softmax'\ncfg.network.moderator.head_share.channels = [1024, 1024]\ncfg.network.moderator.head_share.reduction = 4\ncfg.network.moderator.head_share.scale_type = 'scalars'\ncfg.network.moderator.head_share.scale_init = 1.0",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator = CN()\ncfg.network.moderator.head_share = CN()\ncfg.network.moderator.head_share.detach_inputs = False\ncfg.network.moderator.head_share.detach_feature = False\ncfg.network.moderator.head_share.type = 'temp-softmax'\ncfg.network.moderator.head_share.channels = [1024, 1024]\ncfg.network.moderator.head_share.reduction = 4\ncfg.network.moderator.head_share.scale_type = 'scalars'\ncfg.network.moderator.head_share.scale_init = 1.0\ncfg.network.moderator.hand_share = CN()",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.head_share",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.head_share = CN()\ncfg.network.moderator.head_share.detach_inputs = False\ncfg.network.moderator.head_share.detach_feature = False\ncfg.network.moderator.head_share.type = 'temp-softmax'\ncfg.network.moderator.head_share.channels = [1024, 1024]\ncfg.network.moderator.head_share.reduction = 4\ncfg.network.moderator.head_share.scale_type = 'scalars'\ncfg.network.moderator.head_share.scale_init = 1.0\ncfg.network.moderator.hand_share = CN()\ncfg.network.moderator.hand_share.detach_inputs = False",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.head_share.detach_inputs",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.head_share.detach_inputs = False\ncfg.network.moderator.head_share.detach_feature = False\ncfg.network.moderator.head_share.type = 'temp-softmax'\ncfg.network.moderator.head_share.channels = [1024, 1024]\ncfg.network.moderator.head_share.reduction = 4\ncfg.network.moderator.head_share.scale_type = 'scalars'\ncfg.network.moderator.head_share.scale_init = 1.0\ncfg.network.moderator.hand_share = CN()\ncfg.network.moderator.hand_share.detach_inputs = False\ncfg.network.moderator.hand_share.detach_feature = False",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.head_share.detach_feature",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.head_share.detach_feature = False\ncfg.network.moderator.head_share.type = 'temp-softmax'\ncfg.network.moderator.head_share.channels = [1024, 1024]\ncfg.network.moderator.head_share.reduction = 4\ncfg.network.moderator.head_share.scale_type = 'scalars'\ncfg.network.moderator.head_share.scale_init = 1.0\ncfg.network.moderator.hand_share = CN()\ncfg.network.moderator.hand_share.detach_inputs = False\ncfg.network.moderator.hand_share.detach_feature = False\ncfg.network.moderator.hand_share.type = 'temp-softmax'",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.head_share.type",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.head_share.type = 'temp-softmax'\ncfg.network.moderator.head_share.channels = [1024, 1024]\ncfg.network.moderator.head_share.reduction = 4\ncfg.network.moderator.head_share.scale_type = 'scalars'\ncfg.network.moderator.head_share.scale_init = 1.0\ncfg.network.moderator.hand_share = CN()\ncfg.network.moderator.hand_share.detach_inputs = False\ncfg.network.moderator.hand_share.detach_feature = False\ncfg.network.moderator.hand_share.type = 'temp-softmax'\ncfg.network.moderator.hand_share.channels = [1024, 1024]",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.head_share.channels",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.head_share.channels = [1024, 1024]\ncfg.network.moderator.head_share.reduction = 4\ncfg.network.moderator.head_share.scale_type = 'scalars'\ncfg.network.moderator.head_share.scale_init = 1.0\ncfg.network.moderator.hand_share = CN()\ncfg.network.moderator.hand_share.detach_inputs = False\ncfg.network.moderator.hand_share.detach_feature = False\ncfg.network.moderator.hand_share.type = 'temp-softmax'\ncfg.network.moderator.hand_share.channels = [1024, 1024]\ncfg.network.moderator.hand_share.reduction = 4",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.head_share.reduction",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.head_share.reduction = 4\ncfg.network.moderator.head_share.scale_type = 'scalars'\ncfg.network.moderator.head_share.scale_init = 1.0\ncfg.network.moderator.hand_share = CN()\ncfg.network.moderator.hand_share.detach_inputs = False\ncfg.network.moderator.hand_share.detach_feature = False\ncfg.network.moderator.hand_share.type = 'temp-softmax'\ncfg.network.moderator.hand_share.channels = [1024, 1024]\ncfg.network.moderator.hand_share.reduction = 4\ncfg.network.moderator.hand_share.scale_type = 'scalars'",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.head_share.scale_type",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.head_share.scale_type = 'scalars'\ncfg.network.moderator.head_share.scale_init = 1.0\ncfg.network.moderator.hand_share = CN()\ncfg.network.moderator.hand_share.detach_inputs = False\ncfg.network.moderator.hand_share.detach_feature = False\ncfg.network.moderator.hand_share.type = 'temp-softmax'\ncfg.network.moderator.hand_share.channels = [1024, 1024]\ncfg.network.moderator.hand_share.reduction = 4\ncfg.network.moderator.hand_share.scale_type = 'scalars'\ncfg.network.moderator.hand_share.scale_init = 0.0",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.head_share.scale_init",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.head_share.scale_init = 1.0\ncfg.network.moderator.hand_share = CN()\ncfg.network.moderator.hand_share.detach_inputs = False\ncfg.network.moderator.hand_share.detach_feature = False\ncfg.network.moderator.hand_share.type = 'temp-softmax'\ncfg.network.moderator.hand_share.channels = [1024, 1024]\ncfg.network.moderator.hand_share.reduction = 4\ncfg.network.moderator.hand_share.scale_type = 'scalars'\ncfg.network.moderator.hand_share.scale_init = 0.0\ndef get_cfg_defaults():",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.hand_share",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.hand_share = CN()\ncfg.network.moderator.hand_share.detach_inputs = False\ncfg.network.moderator.hand_share.detach_feature = False\ncfg.network.moderator.hand_share.type = 'temp-softmax'\ncfg.network.moderator.hand_share.channels = [1024, 1024]\ncfg.network.moderator.hand_share.reduction = 4\ncfg.network.moderator.hand_share.scale_type = 'scalars'\ncfg.network.moderator.hand_share.scale_init = 0.0\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.hand_share.detach_inputs",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.hand_share.detach_inputs = False\ncfg.network.moderator.hand_share.detach_feature = False\ncfg.network.moderator.hand_share.type = 'temp-softmax'\ncfg.network.moderator.hand_share.channels = [1024, 1024]\ncfg.network.moderator.hand_share.reduction = 4\ncfg.network.moderator.hand_share.scale_type = 'scalars'\ncfg.network.moderator.hand_share.scale_init = 0.0\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.hand_share.detach_feature",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.hand_share.detach_feature = False\ncfg.network.moderator.hand_share.type = 'temp-softmax'\ncfg.network.moderator.hand_share.channels = [1024, 1024]\ncfg.network.moderator.hand_share.reduction = 4\ncfg.network.moderator.hand_share.scale_type = 'scalars'\ncfg.network.moderator.hand_share.scale_init = 0.0\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.hand_share.type",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.hand_share.type = 'temp-softmax'\ncfg.network.moderator.hand_share.channels = [1024, 1024]\ncfg.network.moderator.hand_share.reduction = 4\ncfg.network.moderator.hand_share.scale_type = 'scalars'\ncfg.network.moderator.hand_share.scale_init = 0.0\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.hand_share.channels",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.hand_share.channels = [1024, 1024]\ncfg.network.moderator.hand_share.reduction = 4\ncfg.network.moderator.hand_share.scale_type = 'scalars'\ncfg.network.moderator.hand_share.scale_init = 0.0\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.hand_share.reduction",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.hand_share.reduction = 4\ncfg.network.moderator.hand_share.scale_type = 'scalars'\ncfg.network.moderator.hand_share.scale_init = 0.0\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n # cfg.merge_from_file(cfg_file, allow_unsafe=True)",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.hand_share.scale_type",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.hand_share.scale_type = 'scalars'\ncfg.network.moderator.hand_share.scale_init = 0.0\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n # cfg.merge_from_file(cfg_file, allow_unsafe=True)\n cfg.merge_from_file(cfg_file)",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.network.moderator.hand_share.scale_init",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.config",
+ "description": "modules.PIXIE.pixielib.utils.config",
+ "peekOfCode": "cfg.network.moderator.hand_share.scale_init = 0.0\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n return cfg.clone()\ndef update_cfg(cfg, cfg_file):\n # cfg.merge_from_file(cfg_file, allow_unsafe=True)\n cfg.merge_from_file(cfg_file)\n return cfg.clone()",
+ "detail": "modules.PIXIE.pixielib.utils.config",
+ "documentation": {}
+ },
+ {
+ "label": "StandardRasterizer",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.utils.renderer",
+ "description": "modules.PIXIE.pixielib.utils.renderer",
+ "peekOfCode": "class StandardRasterizer(nn.Module):\n \"\"\" Alg: https://www.scratchapixel.com/lessons/3d-basic-rendering/rasterization-practical-implementation\n Notice:\n x,y,z are in image space, normalized to [-1, 1]\n can render non-squared image\n not differentiable\n \"\"\"\n def __init__(self, height, width=None):\n \"\"\"\n use fixed raster_settings for rendering faces",
+ "detail": "modules.PIXIE.pixielib.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "Pytorch3dRasterizer",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.utils.renderer",
+ "description": "modules.PIXIE.pixielib.utils.renderer",
+ "peekOfCode": "class Pytorch3dRasterizer(nn.Module):\n \"\"\" Borrowed from https://github.com/facebookresearch/pytorch3d\n This class implements methods for rasterizing a batch of heterogenous Meshes.\n Notice:\n x,y,z are in image space, normalized\n can only render squared image now\n \"\"\"\n def __init__(self, image_size=224):\n \"\"\"\n use fixed raster_settings for rendering faces",
+ "detail": "modules.PIXIE.pixielib.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "SRenderY",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.utils.renderer",
+ "description": "modules.PIXIE.pixielib.utils.renderer",
+ "peekOfCode": "class SRenderY(nn.Module):\n def __init__(self, image_size, obj_filename, uv_size=256, rasterizer_type='standard'):\n super(SRenderY, self).__init__()\n self.image_size = image_size\n self.uv_size = uv_size\n self.rasterizer_type=rasterizer_type\n if rasterizer_type == 'pytorch3d':\n self.rasterizer = Pytorch3dRasterizer(image_size)\n self.uv_rasterizer = Pytorch3dRasterizer(uv_size)\n verts, faces, aux = load_obj(obj_filename)",
+ "detail": "modules.PIXIE.pixielib.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "set_rasterizer",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.renderer",
+ "description": "modules.PIXIE.pixielib.utils.renderer",
+ "peekOfCode": "def set_rasterizer(type='pytorch3d'):\n if type == 'pytorch3d':\n global Meshes, load_obj, rasterize_meshes\n from pytorch3d.structures import Meshes\n from pytorch3d.io import load_obj\n from pytorch3d.renderer.mesh import rasterize_meshes\n elif type == 'standard':\n global standard_rasterize, load_obj\n import os\n from .util import load_obj",
+ "detail": "modules.PIXIE.pixielib.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "rad2deg",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def rad2deg(tensor):\n \"\"\"Function that converts angles from radians to degrees.\n See :class:`~torchgeometry.RadToDeg` for details.\n Args:\n tensor (Tensor): Tensor of arbitrary shape.\n Returns:\n Tensor: Tensor with same shape as input.\n Example:\n >>> input = tgm.pi * torch.rand(1, 3, 3)\n >>> output = tgm.rad2deg(input)",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "deg2rad",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def deg2rad(tensor):\n \"\"\"Function that converts angles from degrees to radians.\n See :class:`~torchgeometry.DegToRad` for details.\n Args:\n tensor (Tensor): Tensor of arbitrary shape.\n Returns:\n Tensor: Tensor with same shape as input.\n Examples::\n >>> input = 360. * torch.rand(1, 3, 3)\n >>> output = tgm.deg2rad(input)",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "euler_to_quaternion",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def euler_to_quaternion(r):\n x = r[..., 0]\n y = r[..., 1]\n z = r[..., 2]\n z = z/2.0\n y = y/2.0\n x = x/2.0\n cz = torch.cos(z)\n sz = torch.sin(z)\n cy = torch.cos(y)",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "rotation_matrix_to_quaternion",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6):\n \"\"\"Convert 3x4 rotation matrix to 4d quaternion vector\n This algorithm is based on algorithm described in\n https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201\n Args:\n rotation_matrix (Tensor): the rotation matrix to convert.\n Return:\n Tensor: the rotation in quaternion\n Shape:\n - Input: :math:`(N, 3, 4)`",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "angle_axis_to_quaternion",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def angle_axis_to_quaternion(angle_axis: torch.Tensor) -> torch.Tensor:\n \"\"\"Convert an angle axis to a quaternion.\n Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h\n Args:\n angle_axis (torch.Tensor): tensor with angle axis.\n Return:\n torch.Tensor: tensor with quaternion.\n Shape:\n - Input: :math:`(*, 3)` where `*` means, any number of dimensions\n - Output: :math:`(*, 4)`",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "quaternion_to_rotation_matrix",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def quaternion_to_rotation_matrix(quat):\n \"\"\"Convert quaternion coefficients to rotation matrix.\n Args:\n quat: size = [B, 4] 4 <===>(w, x, y, z)\n Returns:\n Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]\n \"\"\"\n norm_quat = quat\n norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True)\n w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:, 2], norm_quat[:, 3]",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "quaternion_to_angle_axis",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def quaternion_to_angle_axis(quaternion: torch.Tensor):\n \"\"\"Convert quaternion vector to angle axis of rotation. TODO: CORRECT\n Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h\n Args:\n quaternion (torch.Tensor): tensor with quaternions.\n Return:\n torch.Tensor: tensor with angle axis of rotation.\n Shape:\n - Input: :math:`(*, 4)` where `*` means, any number of dimensions\n - Output: :math:`(*, 3)`",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_euler2axis",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def batch_euler2axis(r):\n return quaternion_to_angle_axis(euler_to_quaternion(r))\ndef batch_euler2matrix(r):\n return quaternion_to_rotation_matrix(euler_to_quaternion(r))\ndef batch_matrix2euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n ### only y biw\n # TODO: add x, z\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_euler2matrix",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def batch_euler2matrix(r):\n return quaternion_to_rotation_matrix(euler_to_quaternion(r))\ndef batch_matrix2euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n ### only y biw\n # TODO: add x, z\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +\n rot_mats[:, 1, 0] * rot_mats[:, 1, 0])\n return torch.atan2(-rot_mats[:, 2, 0], sy)",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_matrix2euler",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def batch_matrix2euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n ### only y biw\n # TODO: add x, z\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +\n rot_mats[:, 1, 0] * rot_mats[:, 1, 0])\n return torch.atan2(-rot_mats[:, 2, 0], sy)\ndef batch_matrix2axis(rot_mats):\n return quaternion_to_angle_axis(rotation_matrix_to_quaternion(rot_mats))",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_matrix2axis",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def batch_matrix2axis(rot_mats):\n return quaternion_to_angle_axis(rotation_matrix_to_quaternion(rot_mats))\ndef batch_axis2matrix(theta):\n # angle axis to rotation matrix\n # theta N x 3\n # return quat2mat(quat)\n # batch_rodrigues\n return quaternion_to_rotation_matrix(angle_axis_to_quaternion(theta))\ndef batch_axis2euler(theta):\n return batch_matrix2euler(batch_axis2matrix(theta))",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_axis2matrix",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def batch_axis2matrix(theta):\n # angle axis to rotation matrix\n # theta N x 3\n # return quat2mat(quat)\n # batch_rodrigues\n return quaternion_to_rotation_matrix(angle_axis_to_quaternion(theta))\ndef batch_axis2euler(theta):\n return batch_matrix2euler(batch_axis2matrix(theta))\ndef batch_axis2euler(r):\n return rot_mat_to_euler(batch_rodrigues(r))",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_axis2euler",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def batch_axis2euler(theta):\n return batch_matrix2euler(batch_axis2matrix(theta))\ndef batch_axis2euler(r):\n return rot_mat_to_euler(batch_rodrigues(r))\ndef batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):\n ''' same as batch_matrix2axis\n Calculates the rotation matrices for a batch of rotation vectors\n Parameters\n ----------\n rot_vecs: torch.tensor Nx3",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_axis2euler",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def batch_axis2euler(r):\n return rot_mat_to_euler(batch_rodrigues(r))\ndef batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):\n ''' same as batch_matrix2axis\n Calculates the rotation matrices for a batch of rotation vectors\n Parameters\n ----------\n rot_vecs: torch.tensor Nx3\n array of N axis-angle vectors\n Returns",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_rodrigues",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32):\n ''' same as batch_matrix2axis\n Calculates the rotation matrices for a batch of rotation vectors\n Parameters\n ----------\n rot_vecs: torch.tensor Nx3\n array of N axis-angle vectors\n Returns\n -------\n R: torch.tensor Nx3x3",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "batch_cont2matrix",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "def batch_cont2matrix(module_input):\n ''' Decoder for transforming a latent representation to rotation matrices\n Implements the decoding method described in:\n \"On the Continuity of Rotation Representations in Neural Networks\"\n Code from https://github.com/vchoutas/expose\n '''\n batch_size = module_input.shape[0]\n reshaped_input = module_input.reshape(-1, 3, 2)\n # Normalize the first vector\n b1 = F.normalize(reshaped_input[:, :, 0].clone(), dim=1)",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "pi",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "pi = torch.Tensor([3.14159265358979323846])\ndef rad2deg(tensor):\n \"\"\"Function that converts angles from radians to degrees.\n See :class:`~torchgeometry.RadToDeg` for details.\n Args:\n tensor (Tensor): Tensor of arbitrary shape.\n Returns:\n Tensor: Tensor with same shape as input.\n Example:\n >>> input = tgm.pi * torch.rand(1, 3, 3)",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "_AXIS_TO_IND",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "description": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "peekOfCode": "_AXIS_TO_IND = {'x': 0, 'y': 1, 'z': 2}\ndef _elementary_basis_vector(axis):\n b = torch.zeros(3)\n b[_AXIS_TO_IND[axis]] = 1\n return b\ndef _compute_euler_from_matrix(dcm, seq='xyz', extrinsic=False):\n # The algorithm assumes intrinsic frame transformations. For representation\n # the paper uses transformation matrices, which are transpose of the\n # direction cosine matrices used by our Rotation class.\n # Adapt the algorithm for our case by",
+ "detail": "modules.PIXIE.pixielib.utils.rotation_converter",
+ "documentation": {}
+ },
+ {
+ "label": "Cropper",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "description": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "peekOfCode": "class Cropper(object):\n def __init__(self, crop_size, scale=[1,1], trans_scale = 0.):\n self.crop_size = crop_size\n self.scale = scale\n self.trans_scale = trans_scale\n def crop(self, image, points, points_scale=None):\n # points to bbox\n center, bbox_size = points2bbox(points.clone(), points_scale)\n # argument bbox. TODO: add rotation?\n center, bbox_size = augment_bbox(center, bbox_size, scale=self.scale, trans_scale=self.trans_scale)",
+ "detail": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "points2bbox",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "description": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "peekOfCode": "def points2bbox(points, points_scale=None):\n if points_scale:\n assert points_scale[0]==points_scale[1]\n points = points.clone()\n points[:,:,:2] = (points[:,:,:2]*0.5 + 0.5)*points_scale[0]\n min_coords, _ = torch.min(points, dim=1)\n xmin, ymin = min_coords[:, 0], min_coords[:, 1]\n max_coords, _ = torch.max(points, dim=1)\n xmax, ymax = max_coords[:, 0], max_coords[:, 1]\n center = torch.stack([xmax + xmin, ymax + ymin], dim=-1) * 0.5",
+ "detail": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "augment_bbox",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "description": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "peekOfCode": "def augment_bbox(center, bbox_size, scale=[1.0, 1.0], trans_scale=0.):\n batch_size = center.shape[0]\n trans_scale = (torch.rand([batch_size, 2], device=center.device)*2. -1.) * trans_scale\n center = center + trans_scale*bbox_size # 0.5\n scale = torch.rand([batch_size,1], device=center.device) * (scale[1] - scale[0]) + scale[0]\n size = bbox_size*scale\n return center, size\ndef crop_tensor(image, center, bbox_size, crop_size, interpolation = 'bilinear', align_corners=False):\n ''' for batch image\n Args:",
+ "detail": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "crop_tensor",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "description": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "peekOfCode": "def crop_tensor(image, center, bbox_size, crop_size, interpolation = 'bilinear', align_corners=False):\n ''' for batch image\n Args:\n image (torch.Tensor): the reference tensor of shape BXHxWXC.\n center: [bz, 2]\n bboxsize: [bz, 1]\n crop_size;\n interpolation (str): Interpolation flag. Default: 'bilinear'.\n align_corners (bool): mode for grid_generation. Default: False. See\n https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.interpolate for details",
+ "detail": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "transform_points",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "description": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "peekOfCode": "def transform_points(points, tform, points_scale=None):\n points_2d = points[:,:,:2]\n # -1 to 1\n #'input points must use original range'\n if points_scale:\n assert points_scale[0]==points_scale[1]\n points_2d = (points_2d*0.5 + 0.5)*points_scale[0]\n # import ipdb; ipdb.set_trace()\n # 0 - points_scale\n batch_size, n_points, _ = points.shape",
+ "detail": "modules.PIXIE.pixielib.utils.tensor_cropper",
+ "documentation": {}
+ },
+ {
+ "label": "dict_tensor2npy",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def dict_tensor2npy(tensor_dict):\n npy_dict = {}\n for key, value in tensor_dict.items():\n if type(value)==torch.Tensor:\n npy_dict[key] = value.detach().cpu().numpy()\n else:\n npy_dict[key] = value\n return npy_dict\n# def dict_tensor2npy(tensor_dict):\n# npy_dict = {}",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "perspective_projection",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def perspective_projection(points, rotation, translation,camera_intrics):\n \"\"\"\n This function computes the perspective projection of a set of points.\n Input:\n points (bs, N, 3): 3D points\n rotation (bs, 3, 3): Camera rotation\n translation (bs, 3): Camera translation\n focal_length (bs,) or scalar: Focal length\n camera_center (bs, 2): Camera center\n \"\"\"",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "estimate_translation_np",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def estimate_translation_np(S, joints_2d, joints_conf, camera_intrics):\n \"\"\"Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d.\n Input:\n S: (25, 3) 3D joint locations\n joints: (25, 3) 2D joint locations and confidence\n Returns:\n (3,) camera translation vector\n \"\"\"\n num_joints = S.shape[0]\n # focal length",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "show_tensor_img",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def show_tensor_img(im):\n im = im.cpu().numpy()\n im = im*255\n im = im.transpose(1, 2, 0) # chw-->hwc\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n cv2.imwrite('test.jpg', im)\ndef show_face(datas):\n ax = plt.gca() \n ax.xaxis.set_ticks_position('top')\n ax.invert_yaxis() #y轴反向",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "show_face",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def show_face(datas):\n ax = plt.gca() \n ax.xaxis.set_ticks_position('top')\n ax.invert_yaxis() #y轴反向\n plt.scatter(datas[:,0],datas[:,1])\n# ---------------------------- process/generate vertices, normals, faces\ndef generate_triangles(h, w, mask = None):\n '''\n quad layout:\n 0 1 ... w-1",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "generate_triangles",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def generate_triangles(h, w, mask = None):\n '''\n quad layout:\n 0 1 ... w-1\n w w+1\n .\n w*h\n '''\n triangles = []\n margin=0",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "face_vertices",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def face_vertices(vertices, faces):\n \"\"\"\n borrowed from https://github.com/daniilidis-group/neural_renderer/blob/master/neural_renderer/vertices_to_faces.py\n :param vertices: [batch size, number of vertices, 3]\n :param faces: [batch size, number of faces, 3]\n :return: [batch size, number of faces, 3, 3]\n \"\"\"\n assert (vertices.ndimension() == 3)\n assert (faces.ndimension() == 3)\n assert (vertices.shape[0] == faces.shape[0])",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "vertex_normals",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def vertex_normals(vertices, faces):\n \"\"\"\n borrowed from https://github.com/daniilidis-group/neural_renderer/blob/master/neural_renderer/vertices_to_faces.py\n :param vertices: [batch size, number of vertices, 3]\n :param faces: [batch size, number of faces, 3]\n :return: [batch size, number of vertices, 3]\n \"\"\"\n assert (vertices.ndimension() == 3)\n assert (faces.ndimension() == 3)\n assert (vertices.shape[0] == faces.shape[0])",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "batch_orth_proj",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def batch_orth_proj(X, camera):\n '''\n X is N x num_verts x 3\n '''\n camera = camera.clone().view(-1, 1, 3)\n X_trans = X[:, :, :2] + camera[:, :, 1:]\n X_trans = torch.cat([X_trans, X[:,:,2:]], 2)\n Xn = (camera[:, :, 0:1] * X_trans)\n return Xn\n##### borrowed from https://github.com/vchoutas/expose",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "flip_pose",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def flip_pose(pose_vector, pose_format='rot-mat'):\n if pose_format == 'aa':\n if torch.is_tensor(pose_vector):\n dim_flip = DIM_FLIP_TENSOR\n else:\n dim_flip = DIM_FLIP\n return (pose_vector.reshape(-1, 3) * dim_flip).reshape(-1)\n elif pose_format == 'rot-mat':\n rot_mats = pose_vector.reshape(-1, 9).clone()\n rot_mats[:, [1, 2, 3, 6]] *= -1",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "gaussian",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def gaussian(window_size, sigma):\n def gauss_fcn(x):\n return -(x - window_size // 2)**2 / float(2 * sigma**2)\n gauss = torch.stack(\n [torch.exp(torch.tensor(gauss_fcn(x))) for x in range(window_size)])\n return gauss / gauss.sum()\ndef get_gaussian_kernel(kernel_size: int, sigma: float):\n r\"\"\"Function that returns Gaussian filter coefficients.\n Args:\n kernel_size (int): filter size. It should be odd and positive.",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "get_gaussian_kernel",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def get_gaussian_kernel(kernel_size: int, sigma: float):\n r\"\"\"Function that returns Gaussian filter coefficients.\n Args:\n kernel_size (int): filter size. It should be odd and positive.\n sigma (float): gaussian standard deviation.\n Returns:\n Tensor: 1D tensor with gaussian filter coefficients.\n Shape:\n - Output: :math:`(\\text{kernel_size})`\n Examples::",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "get_gaussian_kernel2d",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def get_gaussian_kernel2d(kernel_size, sigma):\n r\"\"\"Function that returns Gaussian filter matrix coefficients.\n Args:\n kernel_size (Tuple[int, int]): filter sizes in the x and y direction.\n Sizes should be odd and positive.\n sigma (Tuple[int, int]): gaussian standard deviation in the x and y\n direction.\n Returns:\n Tensor: 2D tensor with gaussian filter matrix coefficients.\n Shape:",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "gaussian_blur",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def gaussian_blur(x, kernel_size=(5,5), sigma=(1.3,1.3)):\n b, c, h, w = x.shape\n kernel = get_gaussian_kernel2d(kernel_size, sigma).to(x.device).to(x.dtype)\n kernel = kernel.repeat(c, 1, 1, 1)\n padding = [(k - 1) // 2 for k in kernel_size]\n return F.conv2d(x, kernel, padding=padding, stride=1, groups=c)\ndef _compute_binary_kernel(window_size):\n r\"\"\"Creates a binary kernel to extract the patches. If the window size\n is HxW will create a (H*W)xHxW kernel.\n \"\"\"",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "median_blur",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def median_blur(x, kernel_size=(3,3)):\n b, c, h, w = x.shape\n kernel = _compute_binary_kernel(kernel_size).to(x.device).to(x.dtype)\n kernel = kernel.repeat(c, 1, 1, 1)\n padding = [(k - 1) // 2 for k in kernel_size]\n features = F.conv2d(x, kernel, padding=padding, stride=1, groups=c)\n features = features.view(b,c,-1,h,w)\n median = torch.median(features, dim=2)[0]\n return median\ndef get_laplacian_kernel2d(kernel_size: int):",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "get_laplacian_kernel2d",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def get_laplacian_kernel2d(kernel_size: int):\n r\"\"\"Function that returns Gaussian filter matrix coefficients.\n Args:\n kernel_size (int): filter size should be odd.\n Returns:\n Tensor: 2D tensor with laplacian filter matrix coefficients.\n Shape:\n - Output: :math:`(\\text{kernel_size}_x, \\text{kernel_size}_y)`\n Examples::\n >>> kornia.image.get_laplacian_kernel2d(3)",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "laplacian",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def laplacian(x):\n # https://torchgeometry.readthedocs.io/en/latest/_modules/kornia/filters/laplacian.html\n b, c, h, w = x.shape\n kernel_size = 3\n kernel = get_laplacian_kernel2d(kernel_size).to(x.device).to(x.dtype)\n kernel = kernel.repeat(c, 1, 1, 1)\n padding = (kernel_size - 1) // 2\n return F.conv2d(x, kernel, padding=padding, stride=1, groups=c)\n# -------------------------------------- io\ndef copy_state_dict(cur_state_dict, pre_state_dict, prefix='', load_name=None):",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "copy_state_dict",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def copy_state_dict(cur_state_dict, pre_state_dict, prefix='', load_name=None):\n def _get_params(key):\n key = prefix + key\n if key in pre_state_dict:\n return pre_state_dict[key]\n return None\n for k in cur_state_dict.keys():\n if load_name is not None:\n if load_name not in k:\n continue",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "dict2obj",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def dict2obj(d):\n # if isinstance(d, list):\n # d = [dict2obj(x) for x in d]\n if not isinstance(d, dict):\n return d\n class C(object):\n pass\n o = C()\n for k in d:\n o.__dict__[k] = dict2obj(d[k])",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "remove_module",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def remove_module(state_dict):\n# create new OrderedDict that does not contain `module.`\n new_state_dict = OrderedDict()\n for k, v in state_dict.items():\n name = k[7:] # remove `module.`\n new_state_dict[name] = v\n return new_state_dict\ndef tensor2image(tensor):\n image = tensor.detach().cpu().numpy()\n image = image*255.",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "tensor2image",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def tensor2image(tensor):\n image = tensor.detach().cpu().numpy()\n image = image*255.\n image = np.maximum(np.minimum(image, 255), 0)\n image = image.transpose(1,2,0)[:,:,[2,1,0]]\n return image.astype(np.uint8).copy()\ndef load_config(cfg_file):\n import yaml\n with open(cfg_file, 'r') as f:\n cfg = yaml.load(f, Loader=yaml.FullLoader)",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "load_config",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def load_config(cfg_file):\n import yaml\n with open(cfg_file, 'r') as f:\n cfg = yaml.load(f, Loader=yaml.FullLoader)\n return cfg\ndef move_dict_to_device(dict, device, tensor2float=False):\n for k,v in dict.items():\n if isinstance(v, torch.Tensor):\n if tensor2float:\n dict[k] = v.float().to(device)",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "move_dict_to_device",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def move_dict_to_device(dict, device, tensor2float=False):\n for k,v in dict.items():\n if isinstance(v, torch.Tensor):\n if tensor2float:\n dict[k] = v.float().to(device)\n else:\n dict[k] = v.to(device)\ndef write_obj(obj_name,\n vertices,\n faces,",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "write_obj",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def write_obj(obj_name,\n vertices,\n faces,\n colors=None,\n texture=None,\n uvcoords=None,\n uvfaces=None,\n inverse_face_order=False,\n normal_map=None,\n ):",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "save_pkl",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def save_pkl(savepath, params, ind=0):\n out_data = {}\n for k, v in params.items():\n if torch.is_tensor(v):\n out_data[k] = v[ind].detach().cpu().numpy()\n else:\n out_data[k] = v\n # import ipdb; ipdb.set_trace()\n with open(savepath, 'wb') as f:\n pickle.dump(out_data, f, protocol=2)",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "load_obj",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def load_obj(obj_filename):\n \"\"\" Ref: https://github.com/facebookresearch/pytorch3d/blob/25c065e9dafa90163e7cec873dbb324a637c68b7/pytorch3d/io/obj_io.py\n Load a mesh from a file-like object.\n \"\"\"\n with open(obj_filename, 'r') as f:\n lines = [line.strip() for line in f]\n verts, uvcoords = [], []\n faces, uv_faces = [], []\n # startswith expects each line to be a string. If the file is read in as\n # bytes then first decode to strings.",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "draw_rectangle",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def draw_rectangle(img,\n bbox,\n bbox_color=(255, 255, 255),\n thickness=3,\n is_opaque=False,\n alpha=0.5):\n \"\"\"Draws the rectangle around the object\n borrowed from: https://bbox-visualizer.readthedocs.io/en/latest/_modules/bbox_visualizer/bbox_visualizer.html\n Parameters\n ----------",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "plot_bbox",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def plot_bbox(image, bbox):\n ''' Draw bbox\n Args:\n image: the input image\n bbox: [left, top, right, bottom]\n '''\n image = cv2.rectangle(image.copy(), (bbox[1], bbox[0]), (bbox[3], bbox[2]),\n [0,255,0], thickness=3)\n # image = draw_rectangle(image, bbox, bbox_color=[0,255,0])\n return image",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "plot_kpts",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def plot_kpts(image, kpts, color = 'r'):\n ''' Draw 68 key points\n Args:\n image: the input image\n kpt: (68, 3).\n '''\n kpts = kpts.copy().astype(np.int32)\n if color == 'r':\n c = (255, 0, 0)\n elif color == 'g':",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "plot_verts",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def plot_verts(image, kpts, color = 'r'):\n ''' Draw 68 key points\n Args:\n image: the input image\n kpt: (68, 3).\n '''\n kpts = kpts.copy().astype(np.int32)\n if color == 'r':\n c = (255, 0, 0)\n elif color == 'g':",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "tensor_vis_landmarks",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "def tensor_vis_landmarks(images, landmarks, gt_landmarks=None, color = 'g', isScale=True):\n # visualize landmarks\n vis_landmarks = []\n images = images.cpu().numpy()\n predicted_landmarks = landmarks.detach().cpu().numpy()\n if gt_landmarks is not None:\n gt_landmarks_np = gt_landmarks.detach().cpu().numpy()\n for i in range(images.shape[0]):\n image = images[i]\n image = image.transpose(1,2,0)[:,:,[2,1,0]].copy(); image = (image*255)",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "DIM_FLIP",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "DIM_FLIP = np.array([1, -1, -1], dtype=np.float32)\nDIM_FLIP_TENSOR = torch.tensor([1, -1, -1], dtype=torch.float32)\ndef flip_pose(pose_vector, pose_format='rot-mat'):\n if pose_format == 'aa':\n if torch.is_tensor(pose_vector):\n dim_flip = DIM_FLIP_TENSOR\n else:\n dim_flip = DIM_FLIP\n return (pose_vector.reshape(-1, 3) * dim_flip).reshape(-1)\n elif pose_format == 'rot-mat':",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "DIM_FLIP_TENSOR",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "DIM_FLIP_TENSOR = torch.tensor([1, -1, -1], dtype=torch.float32)\ndef flip_pose(pose_vector, pose_format='rot-mat'):\n if pose_format == 'aa':\n if torch.is_tensor(pose_vector):\n dim_flip = DIM_FLIP_TENSOR\n else:\n dim_flip = DIM_FLIP\n return (pose_vector.reshape(-1, 3) * dim_flip).reshape(-1)\n elif pose_format == 'rot-mat':\n rot_mats = pose_vector.reshape(-1, 9).clone()",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "end_list",
+ "kind": 5,
+ "importPath": "modules.PIXIE.pixielib.utils.util",
+ "description": "modules.PIXIE.pixielib.utils.util",
+ "peekOfCode": "end_list = np.array([17, 22, 27, 42, 48, 31, 36, 68], dtype = np.int32) - 1\ndef plot_kpts(image, kpts, color = 'r'):\n ''' Draw 68 key points\n Args:\n image: the input image\n kpt: (68, 3).\n '''\n kpts = kpts.copy().astype(np.int32)\n if color == 'r':\n c = (255, 0, 0)",
+ "detail": "modules.PIXIE.pixielib.utils.util",
+ "documentation": {}
+ },
+ {
+ "label": "PIXIE",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.pixie",
+ "description": "modules.PIXIE.pixielib.pixie",
+ "peekOfCode": "class PIXIE(object):\n def __init__(self, config=None, device='cuda:0'):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device\n # parameters setting\n self.param_list_dict = {}\n for lst in self.cfg.params.keys():",
+ "detail": "modules.PIXIE.pixielib.pixie",
+ "documentation": {}
+ },
+ {
+ "label": "Visualizer",
+ "kind": 6,
+ "importPath": "modules.PIXIE.pixielib.visualizer",
+ "description": "modules.PIXIE.pixielib.visualizer",
+ "peekOfCode": "class Visualizer(object):\n ''' visualizer\n '''\n def __init__(self, render_size=1024, config=None, device='cuda:0', part='body', background=None, rasterizer_type='standard'):\n if config is None:\n self.cfg = cfg\n else:\n self.cfg = config\n self.device = device\n self.render_size = render_size",
+ "detail": "modules.PIXIE.pixielib.visualizer",
+ "documentation": {}
+ },
+ {
+ "label": "color_map_color",
+ "kind": 2,
+ "importPath": "modules.PIXIE.pixielib.visualizer",
+ "description": "modules.PIXIE.pixielib.visualizer",
+ "peekOfCode": "def color_map_color(value, cmap_name='cool', vmin=0, vmax=1):\n # norm = plt.Normalize(vmin, vmax)\n cmap = cm.get_cmap(cmap_name) # PiYG\n # will return rgba, we take only first 3 so we get rgb\n rgb = cmap(value)[:, :3]\n # color = matplotlib.colors.rgb2hex(rgb)\n # rgb = np.tile(value[:,None], (1,3))\n # rgb = np.exp(rgb)\n return rgb\nclass Visualizer(object):",
+ "detail": "modules.PIXIE.pixielib.visualizer",
+ "documentation": {}
+ },
+ {
+ "label": "CustomFormatter",
+ "kind": 6,
+ "importPath": "modules.PyMAF.apps.demo_smplx",
+ "description": "modules.PyMAF.apps.demo_smplx",
+ "peekOfCode": "class CustomFormatter(argparse.ArgumentDefaultsHelpFormatter,\n argparse.RawDescriptionHelpFormatter):\n pass\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(formatter_class=CustomFormatter)\n print('initializing openpifpaf')\n ppnetwork.Factory.cli(parser)\n ppdecoder.cli(parser)\n Predictor.cli(parser)\n Stream.cli(parser)",
+ "detail": "modules.PyMAF.apps.demo_smplx",
+ "documentation": {}
+ },
+ {
+ "label": "prepare_rendering_results",
+ "kind": 2,
+ "importPath": "modules.PyMAF.apps.demo_smplx",
+ "description": "modules.PyMAF.apps.demo_smplx",
+ "peekOfCode": "def prepare_rendering_results(person_data, nframes):\n frame_results = [{} for _ in range(nframes)]\n for idx, frame_id in enumerate(person_data['frame_ids']):\n person_id = person_data['person_ids'][idx],\n frame_results[frame_id][person_id] = {\n 'verts': person_data['verts'][idx],\n 'smplx_verts': person_data['smplx_verts'][idx] if 'smplx_verts' in person_data else None,\n 'cam': person_data['orig_cam'][idx],\n 'cam_t': person_data['orig_cam_t'][idx] if 'orig_cam_t' in person_data else None,\n # 'cam': person_data['pred_cam'][idx],",
+ "detail": "modules.PyMAF.apps.demo_smplx",
+ "documentation": {}
+ },
+ {
+ "label": "run_demo",
+ "kind": 2,
+ "importPath": "modules.PyMAF.apps.demo_smplx",
+ "description": "modules.PyMAF.apps.demo_smplx",
+ "peekOfCode": "def run_demo(args):\n device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n if args.image_folder is None:\n video_file = args.vid_file\n # ========= [Optional] download the youtube video ========= #\n if video_file.startswith('https://www.youtube.com'):\n print(f'Donwloading YouTube video \\\"{video_file}\\\"')\n video_file = download_url(video_file, '/tmp')\n if video_file is None:\n exit('Youtube url is not valid!')",
+ "detail": "modules.PyMAF.apps.demo_smplx",
+ "documentation": {}
+ },
+ {
+ "label": "torch.backends.cudnn.enabled",
+ "kind": 5,
+ "importPath": "modules.PyMAF.apps.demo_smplx",
+ "description": "modules.PyMAF.apps.demo_smplx",
+ "peekOfCode": "torch.backends.cudnn.enabled = False\nMIN_NUM_FRAMES = 1\ndef prepare_rendering_results(person_data, nframes):\n frame_results = [{} for _ in range(nframes)]\n for idx, frame_id in enumerate(person_data['frame_ids']):\n person_id = person_data['person_ids'][idx],\n frame_results[frame_id][person_id] = {\n 'verts': person_data['verts'][idx],\n 'smplx_verts': person_data['smplx_verts'][idx] if 'smplx_verts' in person_data else None,\n 'cam': person_data['orig_cam'][idx],",
+ "detail": "modules.PyMAF.apps.demo_smplx",
+ "documentation": {}
+ },
+ {
+ "label": "MIN_NUM_FRAMES",
+ "kind": 5,
+ "importPath": "modules.PyMAF.apps.demo_smplx",
+ "description": "modules.PyMAF.apps.demo_smplx",
+ "peekOfCode": "MIN_NUM_FRAMES = 1\ndef prepare_rendering_results(person_data, nframes):\n frame_results = [{} for _ in range(nframes)]\n for idx, frame_id in enumerate(person_data['frame_ids']):\n person_id = person_data['person_ids'][idx],\n frame_results[frame_id][person_id] = {\n 'verts': person_data['verts'][idx],\n 'smplx_verts': person_data['smplx_verts'][idx] if 'smplx_verts' in person_data else None,\n 'cam': person_data['orig_cam'][idx],\n 'cam_t': person_data['orig_cam_t'][idx] if 'orig_cam_t' in person_data else None,",
+ "detail": "modules.PyMAF.apps.demo_smplx",
+ "documentation": {}
+ },
+ {
+ "label": "get_image",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def get_image(filename):\n image = cv2.imread(filename)\n return cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\ndef do_augmentation(scale_factor=0.3, color_factor=0.2):\n scale = random.uniform(1.2, 1.2+scale_factor)\n # scale = np.clip(np.random.randn(), 0.0, 1.0) * scale_factor + 1.2\n rot = 0 # np.clip(np.random.randn(), -2.0, 2.0) * aug_config.rot_factor if random.random() <= aug_config.rot_aug_rate else 0\n do_flip = False # aug_config.do_flip_aug and random.random() <= aug_config.flip_aug_rate\n c_up = 1.0 + color_factor\n c_low = 1.0 - color_factor",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "do_augmentation",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def do_augmentation(scale_factor=0.3, color_factor=0.2):\n scale = random.uniform(1.2, 1.2+scale_factor)\n # scale = np.clip(np.random.randn(), 0.0, 1.0) * scale_factor + 1.2\n rot = 0 # np.clip(np.random.randn(), -2.0, 2.0) * aug_config.rot_factor if random.random() <= aug_config.rot_aug_rate else 0\n do_flip = False # aug_config.do_flip_aug and random.random() <= aug_config.flip_aug_rate\n c_up = 1.0 + color_factor\n c_low = 1.0 - color_factor\n color_scale = [random.uniform(c_low, c_up), random.uniform(c_low, c_up), random.uniform(c_low, c_up)]\n return scale, rot, do_flip, color_scale\ndef trans_point2d(pt_2d, trans):",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "trans_point2d",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def trans_point2d(pt_2d, trans):\n src_pt = np.array([pt_2d[0], pt_2d[1], 1.]).T\n dst_pt = np.dot(trans, src_pt)\n return dst_pt[0:2]\ndef rotate_2d(pt_2d, rot_rad):\n x = pt_2d[0]\n y = pt_2d[1]\n sn, cs = np.sin(rot_rad), np.cos(rot_rad)\n xx = x * cs - y * sn\n yy = x * sn + y * cs",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "rotate_2d",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def rotate_2d(pt_2d, rot_rad):\n x = pt_2d[0]\n y = pt_2d[1]\n sn, cs = np.sin(rot_rad), np.cos(rot_rad)\n xx = x * cs - y * sn\n yy = x * sn + y * cs\n return np.array([xx, yy], dtype=np.float32)\ndef gen_trans_from_patch_cv(c_x, c_y, src_width, src_height, dst_width, dst_height, scale, rot, inv=False):\n # augment size with scale\n src_w = src_width * scale",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "gen_trans_from_patch_cv",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def gen_trans_from_patch_cv(c_x, c_y, src_width, src_height, dst_width, dst_height, scale, rot, inv=False):\n # augment size with scale\n src_w = src_width * scale\n src_h = src_height * scale\n src_center = np.zeros(2)\n src_center[0] = c_x\n src_center[1] = c_y # np.array([c_x, c_y], dtype=np.float32)\n # augment rotation\n rot_rad = np.pi * rot / 180\n src_downdir = rotate_2d(np.array([0, src_h * 0.5], dtype=np.float32), rot_rad)",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "generate_patch_image_cv",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def generate_patch_image_cv(cvimg, c_x, c_y, bb_width, bb_height, patch_width, patch_height, do_flip, scale, rot):\n img = cvimg.copy()\n img_height, img_width, img_channels = img.shape\n if do_flip:\n img = img[:, ::-1, :]\n c_x = img_width - c_x - 1\n trans = gen_trans_from_patch_cv(c_x, c_y, bb_width, bb_height, patch_width, patch_height, scale, rot, inv=False)\n img_patch = cv2.warpAffine(img, trans, (int(patch_width), int(patch_height)),\n flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)\n return img_patch, trans",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "crop_image",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def crop_image(image, kp_2d, center_x, center_y, width, height, patch_width, patch_height, do_augment):\n # get augmentation params\n if do_augment:\n scale, rot, do_flip, color_scale = do_augmentation()\n else:\n scale, rot, do_flip, color_scale = 1.3, 0, False, [1.0, 1.0, 1.0]\n # generate image patch\n image, trans = generate_patch_image_cv(\n image,\n center_x,",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "transfrom_keypoints",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def transfrom_keypoints(kp_2d, center_x, center_y, width, height, patch_width, patch_height, do_augment):\n if do_augment:\n scale, rot, do_flip, color_scale = do_augmentation()\n else:\n scale, rot, do_flip, color_scale = 1.2, 0, False, [1.0, 1.0, 1.0]\n # generate transformation\n trans = gen_trans_from_patch_cv(\n center_x,\n center_y,\n width,",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_image_crops",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def get_image_crops(image_file, bboxes):\n image = cv2.cvtColor(cv2.imread(image_file), cv2.COLOR_BGR2RGB)\n crop_images = []\n for bb in bboxes:\n c_y, c_x = (bb[0]+bb[2]) // 2, (bb[1]+bb[3]) // 2\n h, w = bb[2]-bb[0], bb[3]-bb[1]\n w = h = np.where(w / h > 1, w, h)\n crop_image, _ = generate_patch_image_cv(\n cvimg=image.copy(),\n c_x=c_x,",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_single_image_crop",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def get_single_image_crop(image, bbox, scale=1.3):\n if isinstance(image, str):\n if os.path.isfile(image):\n image = cv2.cvtColor(cv2.imread(image), cv2.COLOR_BGR2RGB)\n else:\n print(image)\n raise BaseException(image, 'is not a valid file!')\n elif isinstance(image, torch.Tensor):\n image = image.numpy()\n elif not isinstance(image, np.ndarray):",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_single_image_crop_demo",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def get_single_image_crop_demo(image, bbox, kp_2d, scale=1.2, crop_size=224):\n if isinstance(image, str):\n if os.path.isfile(image):\n image = cv2.cvtColor(cv2.imread(image), cv2.COLOR_BGR2RGB)\n else:\n print(image)\n raise BaseException(image, 'is not a valid file!')\n elif isinstance(image, torch.Tensor):\n image = image.numpy()\n elif not isinstance(image, np.ndarray):",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "read_image",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def read_image(filename):\n image = cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB)\n image = cv2.resize(image, (224,224))\n return convert_cvimg_to_tensor(image)\ndef convert_cvimg_to_tensor(image):\n transform = get_default_transform()\n image = transform(image)\n return image\ndef torch_inv_normal(image):\n image = image * torch.tensor([0.229, 0.224, 0.225], device=image.device).reshape(1, 3, 1, 1)",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "convert_cvimg_to_tensor",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def convert_cvimg_to_tensor(image):\n transform = get_default_transform()\n image = transform(image)\n return image\ndef torch_inv_normal(image):\n image = image * torch.tensor([0.229, 0.224, 0.225], device=image.device).reshape(1, 3, 1, 1)\n image = image + torch.tensor([0.485, 0.456, 0.406], device=image.device).reshape(1, 3, 1, 1)\n image = image.clamp(0., 1.)\n return image\ndef torch2numpy(image):",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "torch_inv_normal",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def torch_inv_normal(image):\n image = image * torch.tensor([0.229, 0.224, 0.225], device=image.device).reshape(1, 3, 1, 1)\n image = image + torch.tensor([0.485, 0.456, 0.406], device=image.device).reshape(1, 3, 1, 1)\n image = image.clamp(0., 1.)\n return image\ndef torch2numpy(image):\n image = image.detach().cpu()\n inv_normalize = transforms.Normalize(\n mean=[-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.255],\n std=[1 / 0.229, 1 / 0.224, 1 / 0.255]",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "torch2numpy",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def torch2numpy(image):\n image = image.detach().cpu()\n inv_normalize = transforms.Normalize(\n mean=[-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.255],\n std=[1 / 0.229, 1 / 0.224, 1 / 0.255]\n )\n image = inv_normalize(image)\n image = image.clamp(0., 1.)\n image = image.numpy() * 255.\n image = np.transpose(image, (1, 2, 0))",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "torch_vid2numpy",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def torch_vid2numpy(video):\n video = video.detach().cpu().numpy()\n # video = np.transpose(video, (0, 2, 1, 3, 4)) # NCTHW->NTCHW\n # Denormalize\n mean = np.array([-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.255])\n std = np.array([1 / 0.229, 1 / 0.224, 1 / 0.255])\n mean = mean[np.newaxis, np.newaxis, ..., np.newaxis, np.newaxis]\n std = std[np.newaxis, np.newaxis, ..., np.newaxis, np.newaxis]\n video = (video - mean) / std # [:, :, i, :, :].sub_(mean[i]).div_(std[i]).clamp_(0., 1.).mul_(255.)\n video = video.clip(0.,1.) * 255",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_bbox_from_kp2d",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def get_bbox_from_kp2d(kp_2d):\n # get bbox\n if len(kp_2d.shape) > 2:\n ul = np.array([kp_2d[:, :, 0].min(axis=1), kp_2d[:, :, 1].min(axis=1)]) # upper left\n lr = np.array([kp_2d[:, :, 0].max(axis=1), kp_2d[:, :, 1].max(axis=1)]) # lower right\n else:\n ul = np.array([kp_2d[:, 0].min(), kp_2d[:, 1].min()]) # upper left\n lr = np.array([kp_2d[:, 0].max(), kp_2d[:, 1].max()]) # lower right\n # ul[1] -= (lr[1] - ul[1]) * 0.10 # prevent cutting the head\n w = lr[0] - ul[0]",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "normalize_2d_kp",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def normalize_2d_kp(kp_2d, crop_size=224, inv=False):\n # Normalize keypoints between -1, 1\n if not inv:\n ratio = 1.0 / crop_size\n kp_2d = 2.0 * kp_2d * ratio - 1.0\n else:\n ratio = 1.0 / crop_size\n kp_2d = (kp_2d + 1.0)/(2*ratio)\n return kp_2d\ndef get_default_transform():",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_default_transform",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def get_default_transform():\n normalize = transforms.Normalize(\n mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]\n )\n transform = transforms.Compose([\n transforms.ToTensor(),\n normalize,\n ])\n return transform\ndef split_into_chunks(vid_names, seqlen, stride):",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "split_into_chunks",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.img_utils",
+ "description": "modules.PyMAF.datasets.data_utils.img_utils",
+ "peekOfCode": "def split_into_chunks(vid_names, seqlen, stride):\n video_start_end_indices = []\n video_names, group = np.unique(vid_names, return_index=True)\n perm = np.argsort(group)\n video_names, group = video_names[perm], group[perm]\n indices = np.split(np.arange(0, vid_names.shape[0]), group[1:])\n for idx in range(len(video_names)):\n indexes = indices[idx]\n if indexes.shape[0] < seqlen:\n continue",
+ "detail": "modules.PyMAF.datasets.data_utils.img_utils",
+ "documentation": {}
+ },
+ {
+ "label": "keypoint_hflip",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def keypoint_hflip(kp, img_width):\n # Flip a keypoint horizontally around the y-axis\n # kp N,2\n if len(kp.shape) == 2:\n kp[:,0] = (img_width - 1.) - kp[:,0]\n elif len(kp.shape) == 3:\n kp[:, :, 0] = (img_width - 1.) - kp[:, :, 0]\n return kp\ndef convert_kps(joints2d, src, dst):\n src_names = eval(f'get_{src}_joint_names')()",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "convert_kps",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def convert_kps(joints2d, src, dst):\n src_names = eval(f'get_{src}_joint_names')()\n dst_names = eval(f'get_{dst}_joint_names')()\n out_joints2d = np.zeros((joints2d.shape[0], len(dst_names), 3))\n for idx, jn in enumerate(dst_names):\n if jn in src_names:\n out_joints2d[:, idx] = joints2d[:, src_names.index(jn)]\n return out_joints2d\ndef get_perm_idxs(src, dst):\n src_names = eval(f'get_{src}_joint_names')()",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_perm_idxs",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_perm_idxs(src, dst):\n src_names = eval(f'get_{src}_joint_names')()\n dst_names = eval(f'get_{dst}_joint_names')()\n idxs = [src_names.index(h) for h in dst_names if h in src_names]\n return idxs\ndef get_mpii3d_test_joint_names():\n return [\n 'headtop', # 'head_top',\n 'neck',\n 'rshoulder',# 'right_shoulder',",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_mpii3d_test_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_mpii3d_test_joint_names():\n return [\n 'headtop', # 'head_top',\n 'neck',\n 'rshoulder',# 'right_shoulder',\n 'relbow',# 'right_elbow',\n 'rwrist',# 'right_wrist',\n 'lshoulder',# 'left_shoulder',\n 'lelbow', # 'left_elbow',\n 'lwrist', # 'left_wrist',",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_mpii3d_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_mpii3d_joint_names():\n return [\n 'spine3', # 0,\n 'spine4', # 1,\n 'spine2', # 2,\n 'Spine (H36M)', #'spine', # 3,\n 'hip', # 'pelvis', # 4,\n 'neck', # 5,\n 'Head (H36M)', # 'head', # 6,\n \"headtop\", # 'head_top', # 7,",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_insta_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_insta_joint_names():\n return [\n 'OP RHeel',\n 'OP RKnee',\n 'OP RHip',\n 'OP LHip',\n 'OP LKnee',\n 'OP LHeel',\n 'OP RWrist',\n 'OP RElbow',",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_insta_skeleton",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_insta_skeleton():\n return np.array(\n [\n [0 , 1],\n [1 , 2],\n [2 , 3],\n [3 , 4],\n [4 , 5],\n [6 , 7],\n [7 , 8],",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_staf_skeleton",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_staf_skeleton():\n return np.array(\n [\n [0, 1],\n [1, 2],\n [2, 3],\n [3, 4],\n [1, 5],\n [5, 6],\n [6, 7],",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_staf_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_staf_joint_names():\n return [\n 'OP Nose', # 0,\n 'OP Neck', # 1,\n 'OP RShoulder', # 2,\n 'OP RElbow', # 3,\n 'OP RWrist', # 4,\n 'OP LShoulder', # 5,\n 'OP LElbow', # 6,\n 'OP LWrist', # 7,",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_spin_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_spin_joint_names():\n return [\n 'OP Nose', # 0\n 'OP Neck', # 1\n 'OP RShoulder', # 2\n 'OP RElbow', # 3\n 'OP RWrist', # 4\n 'OP LShoulder', # 5\n 'OP LElbow', # 6\n 'OP LWrist', # 7",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_h36m_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_h36m_joint_names():\n return [\n 'hip', # 0\n 'lhip', # 1\n 'lknee', # 2\n 'lankle', # 3\n 'rhip', # 4\n 'rknee', # 5\n 'rankle', # 6\n 'Spine (H36M)', # 7",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_spin_skeleton",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_spin_skeleton():\n return np.array(\n [\n [0 , 1],\n [1 , 2],\n [2 , 3],\n [3 , 4],\n [1 , 5],\n [5 , 6],\n [6 , 7],",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_posetrack_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_posetrack_joint_names():\n return [\n \"nose\",\n \"neck\",\n \"headtop\",\n \"lear\",\n \"rear\",\n \"lshoulder\",\n \"rshoulder\",\n \"lelbow\",",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_posetrack_original_kp_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_posetrack_original_kp_names():\n return [\n 'nose',\n 'head_bottom',\n 'head_top',\n 'left_ear',\n 'right_ear',\n 'left_shoulder',\n 'right_shoulder',\n 'left_elbow',",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_pennaction_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_pennaction_joint_names():\n return [\n \"headtop\", # 0\n \"lshoulder\", # 1\n \"rshoulder\", # 2\n \"lelbow\", # 3\n \"relbow\", # 4\n \"lwrist\", # 5\n \"rwrist\", # 6\n \"lhip\" , # 7",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_common_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_common_joint_names():\n return [\n \"rankle\", # 0 \"lankle\", # 0\n \"rknee\", # 1 \"lknee\", # 1\n \"rhip\", # 2 \"lhip\", # 2\n \"lhip\", # 3 \"rhip\", # 3\n \"lknee\", # 4 \"rknee\", # 4\n \"lankle\", # 5 \"rankle\", # 5\n \"rwrist\", # 6 \"lwrist\", # 6\n \"relbow\", # 7 \"lelbow\", # 7",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_common_skeleton",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_common_skeleton():\n return np.array(\n [\n [ 0, 1 ],\n [ 1, 2 ],\n [ 3, 4 ],\n [ 4, 5 ],\n [ 6, 7 ],\n [ 7, 8 ],\n [ 8, 2 ],",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_coco_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_coco_joint_names():\n return [\n \"nose\", # 0\n \"leye\", # 1\n \"reye\", # 2\n \"lear\", # 3\n \"rear\", # 4\n \"lshoulder\", # 5\n \"rshoulder\", # 6\n \"lelbow\", # 7",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_coco_skeleton",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_coco_skeleton():\n # 0 - nose,\n # 1 - leye,\n # 2 - reye,\n # 3 - lear,\n # 4 - rear,\n # 5 - lshoulder,\n # 6 - rshoulder,\n # 7 - lelbow,\n # 8 - relbow,",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_mpii_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_mpii_joint_names():\n return [\n \"rankle\", # 0\n \"rknee\", # 1\n \"rhip\", # 2\n \"lhip\", # 3\n \"lknee\", # 4\n \"lankle\", # 5\n \"hip\", # 6\n \"thorax\", # 7",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_mpii_skeleton",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_mpii_skeleton():\n # 0 - rankle,\n # 1 - rknee,\n # 2 - rhip,\n # 3 - lhip,\n # 4 - lknee,\n # 5 - lankle,\n # 6 - hip,\n # 7 - thorax,\n # 8 - neck,",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_aich_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_aich_joint_names():\n return [\n \"rshoulder\", # 0\n \"relbow\", # 1\n \"rwrist\", # 2\n \"lshoulder\", # 3\n \"lelbow\", # 4\n \"lwrist\", # 5\n \"rhip\", # 6\n \"rknee\", # 7",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_aich_skeleton",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_aich_skeleton():\n # 0 - rshoulder,\n # 1 - relbow,\n # 2 - rwrist,\n # 3 - lshoulder,\n # 4 - lelbow,\n # 5 - lwrist,\n # 6 - rhip,\n # 7 - rknee,\n # 8 - rankle,",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_3dpw_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_3dpw_joint_names():\n return [\n \"nose\", # 0\n \"thorax\", # 1\n \"rshoulder\", # 2\n \"relbow\", # 3\n \"rwrist\", # 4\n \"lshoulder\", # 5\n \"lelbow\", # 6\n \"lwrist\", # 7",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_3dpw_skeleton",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_3dpw_skeleton():\n return np.array(\n [\n [ 0, 1 ],\n [ 1, 2 ],\n [ 2, 3 ],\n [ 3, 4 ],\n [ 1, 5 ],\n [ 5, 6 ],\n [ 6, 7 ],",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_smplcoco_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_smplcoco_joint_names():\n return [\n \"rankle\", # 0\n \"rknee\", # 1\n \"rhip\", # 2\n \"lhip\", # 3\n \"lknee\", # 4\n \"lankle\", # 5\n \"rwrist\", # 6\n \"relbow\", # 7",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_smplcoco_skeleton",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_smplcoco_skeleton():\n return np.array(\n [\n [ 0, 1 ],\n [ 1, 2 ],\n [ 3, 4 ],\n [ 4, 5 ],\n [ 6, 7 ],\n [ 7, 8 ],\n [ 8, 12],",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_smpl_joint_names",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_smpl_joint_names():\n return [\n 'hips', # 0\n 'leftUpLeg', # 1\n 'rightUpLeg', # 2\n 'spine', # 3\n 'leftLeg', # 4\n 'rightLeg', # 5\n 'spine1', # 6\n 'leftFoot', # 7",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_smpl_skeleton",
+ "kind": 2,
+ "importPath": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "description": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "peekOfCode": "def get_smpl_skeleton():\n return np.array(\n [\n [ 0, 1 ],\n [ 0, 2 ],\n [ 0, 3 ],\n [ 1, 4 ],\n [ 2, 5 ],\n [ 3, 6 ],\n [ 4, 7 ],",
+ "detail": "modules.PyMAF.datasets.data_utils.kp_utils",
+ "documentation": {}
+ },
+ {
+ "label": "Inference",
+ "kind": 6,
+ "importPath": "modules.PyMAF.datasets.inference",
+ "description": "modules.PyMAF.datasets.inference",
+ "peekOfCode": "class Inference(Dataset):\n def __init__(self, image_folder, frames, bboxes=None, joints2d=None, scale=1.0, crop_size=224, pre_load_imgs=None, full_body=False, person_ids=[], wb_kps={}):\n self.pre_load_imgs = pre_load_imgs\n if pre_load_imgs is None:\n self.image_file_names = [\n osp.join(image_folder, x)\n for x in os.listdir(image_folder)\n if x.endswith('.png') or x.endswith('.jpg')\n ]\n self.image_file_names = sorted(self.image_file_names)",
+ "detail": "modules.PyMAF.datasets.inference",
+ "documentation": {}
+ },
+ {
+ "label": "ImageFolder",
+ "kind": 6,
+ "importPath": "modules.PyMAF.datasets.inference",
+ "description": "modules.PyMAF.datasets.inference",
+ "peekOfCode": "class ImageFolder(Dataset):\n def __init__(self, image_folder):\n self.image_file_names = [\n osp.join(image_folder, x)\n for x in os.listdir(image_folder)\n if x.endswith('.png') or x.endswith('.jpg')\n ]\n self.image_file_names = sorted(self.image_file_names)\n def __len__(self):\n return len(self.image_file_names)",
+ "detail": "modules.PyMAF.datasets.inference",
+ "documentation": {}
+ },
+ {
+ "label": "Graphormer_Body_Network",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.e2e_body_network",
+ "description": "modules.PyMAF.models.transformers.bert.e2e_body_network",
+ "peekOfCode": "class Graphormer_Body_Network(torch.nn.Module):\n '''\n End-to-end Graphormer network for human pose and mesh reconstruction from a single image.\n '''\n def __init__(self, args, config, backbone, trans_encoder, mesh_sampler):\n super(Graphormer_Body_Network, self).__init__()\n self.config = config\n self.config.device = args.device\n self.backbone = backbone\n self.trans_encoder = trans_encoder",
+ "detail": "modules.PyMAF.models.transformers.bert.e2e_body_network",
+ "documentation": {}
+ },
+ {
+ "label": "Graphormer_Hand_Network",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.e2e_hand_network",
+ "description": "modules.PyMAF.models.transformers.bert.e2e_hand_network",
+ "peekOfCode": "class Graphormer_Hand_Network(torch.nn.Module):\n '''\n End-to-end Graphormer network for hand pose and mesh reconstruction from a single image.\n '''\n def __init__(self, args, config, backbone, trans_encoder):\n super(Graphormer_Hand_Network, self).__init__()\n self.config = config\n self.backbone = backbone\n self.trans_encoder = trans_encoder\n self.upsampling = torch.nn.Linear(195, 778)",
+ "detail": "modules.PyMAF.models.transformers.bert.e2e_hand_network",
+ "documentation": {}
+ },
+ {
+ "label": "url_to_filename",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.file_utils",
+ "description": "modules.PyMAF.models.transformers.bert.file_utils",
+ "peekOfCode": "def url_to_filename(url, etag=None):\n \"\"\"\n Convert `url` into a hashed filename in a repeatable way.\n If `etag` is specified, append its hash to the url's, delimited\n by a period.\n \"\"\"\n url_bytes = url.encode('utf-8')\n url_hash = sha256(url_bytes)\n filename = url_hash.hexdigest()\n if etag:",
+ "detail": "modules.PyMAF.models.transformers.bert.file_utils",
+ "documentation": {}
+ },
+ {
+ "label": "filename_to_url",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.file_utils",
+ "description": "modules.PyMAF.models.transformers.bert.file_utils",
+ "peekOfCode": "def filename_to_url(filename, cache_dir=None):\n \"\"\"\n Return the url and etag (which may be ``None``) stored for `filename`.\n Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.\n \"\"\"\n if cache_dir is None:\n cache_dir = PYTORCH_PRETRAINED_BERT_CACHE\n if sys.version_info[0] == 3 and isinstance(cache_dir, Path):\n cache_dir = str(cache_dir)\n cache_path = os.path.join(cache_dir, filename)",
+ "detail": "modules.PyMAF.models.transformers.bert.file_utils",
+ "documentation": {}
+ },
+ {
+ "label": "cached_path",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.file_utils",
+ "description": "modules.PyMAF.models.transformers.bert.file_utils",
+ "peekOfCode": "def cached_path(url_or_filename, cache_dir=None):\n \"\"\"\n Given something that might be a URL (or might be a local path),\n determine which. If it's a URL, download the file and cache it, and\n return the path to the cached file. If it's already a local path,\n make sure the file exists and then return the path.\n \"\"\"\n if cache_dir is None:\n cache_dir = PYTORCH_PRETRAINED_BERT_CACHE\n if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):",
+ "detail": "modules.PyMAF.models.transformers.bert.file_utils",
+ "documentation": {}
+ },
+ {
+ "label": "split_s3_path",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.file_utils",
+ "description": "modules.PyMAF.models.transformers.bert.file_utils",
+ "peekOfCode": "def split_s3_path(url):\n \"\"\"Split a full s3 path into the bucket name and path.\"\"\"\n parsed = urlparse(url)\n if not parsed.netloc or not parsed.path:\n raise ValueError(\"bad s3 path {}\".format(url))\n bucket_name = parsed.netloc\n s3_path = parsed.path\n # Remove '/' at beginning of path.\n if s3_path.startswith(\"/\"):\n s3_path = s3_path[1:]",
+ "detail": "modules.PyMAF.models.transformers.bert.file_utils",
+ "documentation": {}
+ },
+ {
+ "label": "s3_request",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.file_utils",
+ "description": "modules.PyMAF.models.transformers.bert.file_utils",
+ "peekOfCode": "def s3_request(func):\n \"\"\"\n Wrapper function for s3 requests in order to create more helpful error\n messages.\n \"\"\"\n @wraps(func)\n def wrapper(url, *args, **kwargs):\n try:\n return func(url, *args, **kwargs)\n except ClientError as exc:",
+ "detail": "modules.PyMAF.models.transformers.bert.file_utils",
+ "documentation": {}
+ },
+ {
+ "label": "s3_etag",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.file_utils",
+ "description": "modules.PyMAF.models.transformers.bert.file_utils",
+ "peekOfCode": "def s3_etag(url):\n \"\"\"Check ETag on S3 object.\"\"\"\n s3_resource = boto3.resource(\"s3\")\n bucket_name, s3_path = split_s3_path(url)\n s3_object = s3_resource.Object(bucket_name, s3_path)\n return s3_object.e_tag\n@s3_request\ndef s3_get(url, temp_file):\n \"\"\"Pull a file directly from S3.\"\"\"\n s3_resource = boto3.resource(\"s3\")",
+ "detail": "modules.PyMAF.models.transformers.bert.file_utils",
+ "documentation": {}
+ },
+ {
+ "label": "s3_get",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.file_utils",
+ "description": "modules.PyMAF.models.transformers.bert.file_utils",
+ "peekOfCode": "def s3_get(url, temp_file):\n \"\"\"Pull a file directly from S3.\"\"\"\n s3_resource = boto3.resource(\"s3\")\n bucket_name, s3_path = split_s3_path(url)\n s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)\ndef http_get(url, temp_file):\n req = requests.get(url, stream=True)\n content_length = req.headers.get('Content-Length')\n total = int(content_length) if content_length is not None else None\n progress = tqdm(unit=\"B\", total=total)",
+ "detail": "modules.PyMAF.models.transformers.bert.file_utils",
+ "documentation": {}
+ },
+ {
+ "label": "http_get",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.file_utils",
+ "description": "modules.PyMAF.models.transformers.bert.file_utils",
+ "peekOfCode": "def http_get(url, temp_file):\n req = requests.get(url, stream=True)\n content_length = req.headers.get('Content-Length')\n total = int(content_length) if content_length is not None else None\n progress = tqdm(unit=\"B\", total=total)\n for chunk in req.iter_content(chunk_size=1024):\n if chunk: # filter out keep-alive new chunks\n progress.update(len(chunk))\n temp_file.write(chunk)\n progress.close()",
+ "detail": "modules.PyMAF.models.transformers.bert.file_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_from_cache",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.file_utils",
+ "description": "modules.PyMAF.models.transformers.bert.file_utils",
+ "peekOfCode": "def get_from_cache(url, cache_dir=None):\n \"\"\"\n Given a URL, look for the corresponding dataset in the local cache.\n If it's not there, download it. Then return the path to the cached file.\n \"\"\"\n if cache_dir is None:\n cache_dir = PYTORCH_PRETRAINED_BERT_CACHE\n if sys.version_info[0] == 3 and isinstance(cache_dir, Path):\n cache_dir = str(cache_dir)\n if sys.version_info[0] == 2 and not isinstance(cache_dir, str):",
+ "detail": "modules.PyMAF.models.transformers.bert.file_utils",
+ "documentation": {}
+ },
+ {
+ "label": "default_cache_path",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.file_utils",
+ "description": "modules.PyMAF.models.transformers.bert.file_utils",
+ "peekOfCode": "default_cache_path = os.path.join(torch_cache_home, 'pytorch_transformers')\ntry:\n from urllib.parse import urlparse\nexcept ImportError:\n from urlparse import urlparse\ntry:\n from pathlib import Path\n PYTORCH_PRETRAINED_BERT_CACHE = Path(\n os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path))\nexcept (AttributeError, ImportError):",
+ "detail": "modules.PyMAF.models.transformers.bert.file_utils",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.file_utils",
+ "description": "modules.PyMAF.models.transformers.bert.file_utils",
+ "peekOfCode": "logger = logging.getLogger(__name__) # pylint: disable=invalid-name\ndef url_to_filename(url, etag=None):\n \"\"\"\n Convert `url` into a hashed filename in a repeatable way.\n If `etag` is specified, append its hash to the url's, delimited\n by a period.\n \"\"\"\n url_bytes = url.encode('utf-8')\n url_hash = sha256(url_bytes)\n filename = url_hash.hexdigest()",
+ "detail": "modules.PyMAF.models.transformers.bert.file_utils",
+ "documentation": {}
+ },
+ {
+ "label": "BertConfig",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertConfig(PretrainedConfig):\n r\"\"\"\n :class:`~pytorch_transformers.BertConfig` is the configuration class to store the configuration of a\n `BertModel`.\n Arguments:\n vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.\n hidden_size: Size of the encoder layers and the pooler layer.\n num_hidden_layers: Number of hidden layers in the Transformer encoder.\n num_attention_heads: Number of attention heads for each attention layer in\n the Transformer encoder.",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertEmbeddings",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertEmbeddings(nn.Module):\n \"\"\"Construct the embeddings from word, position and token_type embeddings.\n \"\"\"\n def __init__(self, config):\n super(BertEmbeddings, self).__init__()\n self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0)\n self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)\n self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)\n # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load\n # any TensorFlow checkpoint file",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertSelfAttention",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertSelfAttention(nn.Module):\n def __init__(self, config):\n super(BertSelfAttention, self).__init__()\n if config.hidden_size % config.num_attention_heads != 0:\n raise ValueError(\n \"The hidden size (%d) is not a multiple of the number of attention \"\n \"heads (%d)\" % (config.hidden_size, config.num_attention_heads))\n self.output_attentions = config.output_attentions\n self.num_attention_heads = config.num_attention_heads\n self.attention_head_size = int(config.hidden_size / config.num_attention_heads)",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertSelfOutput",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertSelfOutput(nn.Module):\n def __init__(self, config):\n super(BertSelfOutput, self).__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n self.dropout = nn.Dropout(config.hidden_dropout_prob)\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertAttention",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertAttention(nn.Module):\n def __init__(self, config):\n super(BertAttention, self).__init__()\n self.self = BertSelfAttention(config)\n self.output = BertSelfOutput(config)\n def prune_heads(self, heads):\n if len(heads) == 0:\n return\n mask = torch.ones(self.self.num_attention_heads, self.self.attention_head_size)\n for head in heads:",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertIntermediate",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertIntermediate(nn.Module):\n def __init__(self, config):\n super(BertIntermediate, self).__init__()\n self.dense = nn.Linear(config.hidden_size, config.intermediate_size)\n if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):\n self.intermediate_act_fn = ACT2FN[config.hidden_act]\n else:\n self.intermediate_act_fn = config.hidden_act\n def forward(self, hidden_states):\n hidden_states = self.dense(hidden_states)",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertOutput",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertOutput(nn.Module):\n def __init__(self, config):\n super(BertOutput, self).__init__()\n self.dense = nn.Linear(config.intermediate_size, config.hidden_size)\n self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n self.dropout = nn.Dropout(config.hidden_dropout_prob)\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertLayer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertLayer(nn.Module):\n def __init__(self, config):\n super(BertLayer, self).__init__()\n self.attention = BertAttention(config)\n self.intermediate = BertIntermediate(config)\n self.output = BertOutput(config)\n def forward(self, hidden_states, attention_mask, head_mask=None):\n attention_outputs = self.attention(hidden_states, attention_mask, head_mask)\n attention_output = attention_outputs[0]\n intermediate_output = self.intermediate(attention_output)",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertEncoder",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertEncoder(nn.Module):\n def __init__(self, config):\n super(BertEncoder, self).__init__()\n self.output_attentions = config.output_attentions\n self.output_hidden_states = config.output_hidden_states\n self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])\n def forward(self, hidden_states, attention_mask, head_mask=None):\n all_hidden_states = ()\n all_attentions = ()\n for i, layer_module in enumerate(self.layer):",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertPooler",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertPooler(nn.Module):\n def __init__(self, config):\n super(BertPooler, self).__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n self.activation = nn.Tanh()\n def forward(self, hidden_states):\n # We \"pool\" the model by simply taking the hidden state corresponding\n # to the first token.\n first_token_tensor = hidden_states[:, 0]\n pooled_output = self.dense(first_token_tensor)",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertPredictionHeadTransform",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertPredictionHeadTransform(nn.Module):\n def __init__(self, config):\n super(BertPredictionHeadTransform, self).__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):\n self.transform_act_fn = ACT2FN[config.hidden_act]\n else:\n self.transform_act_fn = config.hidden_act\n self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n def forward(self, hidden_states):",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertLMPredictionHead",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertLMPredictionHead(nn.Module):\n def __init__(self, config):\n super(BertLMPredictionHead, self).__init__()\n self.transform = BertPredictionHeadTransform(config)\n # The output weights are the same as the input embeddings, but there is\n # an output-only bias for each token.\n self.decoder = nn.Linear(config.hidden_size,\n config.vocab_size,\n bias=False)\n self.bias = nn.Parameter(torch.zeros(config.vocab_size))",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertOnlyMLMHead",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertOnlyMLMHead(nn.Module):\n def __init__(self, config):\n super(BertOnlyMLMHead, self).__init__()\n self.predictions = BertLMPredictionHead(config)\n def forward(self, sequence_output):\n prediction_scores = self.predictions(sequence_output)\n return prediction_scores\nclass BertOnlyNSPHead(nn.Module):\n def __init__(self, config):\n super(BertOnlyNSPHead, self).__init__()",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertOnlyNSPHead",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertOnlyNSPHead(nn.Module):\n def __init__(self, config):\n super(BertOnlyNSPHead, self).__init__()\n self.seq_relationship = nn.Linear(config.hidden_size, 2)\n def forward(self, pooled_output):\n seq_relationship_score = self.seq_relationship(pooled_output)\n return seq_relationship_score\nclass BertPreTrainingHeads(nn.Module):\n def __init__(self, config):\n super(BertPreTrainingHeads, self).__init__()",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertPreTrainingHeads",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertPreTrainingHeads(nn.Module):\n def __init__(self, config):\n super(BertPreTrainingHeads, self).__init__()\n self.predictions = BertLMPredictionHead(config)\n self.seq_relationship = nn.Linear(config.hidden_size, 2)\n def forward(self, sequence_output, pooled_output):\n prediction_scores = self.predictions(sequence_output)\n seq_relationship_score = self.seq_relationship(pooled_output)\n return prediction_scores, seq_relationship_score\nclass BertPreTrainedModel(PreTrainedModel):",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertPreTrainedModel",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertPreTrainedModel(PreTrainedModel):\n \"\"\" An abstract class to handle weights initialization and\n a simple interface for dowloading and loading pretrained models.\n \"\"\"\n config_class = BertConfig\n pretrained_model_archive_map = BERT_PRETRAINED_MODEL_ARCHIVE_MAP\n load_tf_weights = load_tf_weights_in_bert\n base_model_prefix = \"bert\"\n def __init__(self, *inputs, **kwargs):\n super(BertPreTrainedModel, self).__init__(*inputs, **kwargs)",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertModel",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertModel(BertPreTrainedModel):\n r\"\"\"\n Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:\n **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``\n Sequence of hidden-states at the output of the last layer of the model.\n **pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)``\n Last layer hidden-state of the first token of the sequence (classification token)\n further processed by a Linear layer and a Tanh activation function. The Linear\n layer weights are trained from the next sentence prediction (classification)\n objective during Bert pretraining. This output is usually *not* a good summary",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertForPreTraining",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertForPreTraining(BertPreTrainedModel):\n r\"\"\"\n **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:\n Labels for computing the masked language modeling loss.\n Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)\n Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels\n in ``[0, ..., config.vocab_size]``\n **next_sentence_label**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:\n Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see ``input_ids`` docstring)\n Indices should be in ``[0, 1]``.",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertForMaskedLM",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertForMaskedLM(BertPreTrainedModel):\n r\"\"\"\n **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:\n Labels for computing the masked language modeling loss.\n Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)\n Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels\n in ``[0, ..., config.vocab_size]``\n Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:\n **loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:\n Masked language modeling loss.",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertForNextSentencePrediction",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertForNextSentencePrediction(BertPreTrainedModel):\n r\"\"\"\n **next_sentence_label**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:\n Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see ``input_ids`` docstring)\n Indices should be in ``[0, 1]``.\n ``0`` indicates sequence B is a continuation of sequence A,\n ``1`` indicates sequence B is a random sequence.\n Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:\n **loss**: (`optional`, returned when ``next_sentence_label`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:\n Next sequence prediction (classification) loss.",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertForSequenceClassification",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertForSequenceClassification(BertPreTrainedModel):\n r\"\"\"\n **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:\n Labels for computing the sequence classification/regression loss.\n Indices should be in ``[0, ..., config.num_labels]``.\n If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),\n If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).\n Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:\n **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:\n Classification (or regression if config.num_labels==1) loss.",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertForMultipleChoice",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertForMultipleChoice(BertPreTrainedModel):\n r\"\"\"\n Inputs:\n **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:\n Indices of input sequence tokens in the vocabulary.\n The second dimension of the input (`num_choices`) indicates the number of choices to score.\n To match pre-training, BERT input sequence should be formatted with [CLS] and [SEP] tokens as follows:\n (a) For sequence pairs:\n ``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``\n ``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1``",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertForTokenClassification",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertForTokenClassification(BertPreTrainedModel):\n r\"\"\"\n **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:\n Labels for computing the token classification loss.\n Indices should be in ``[0, ..., config.num_labels]``.\n Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:\n **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:\n Classification loss.\n **scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.num_labels)``\n Classification scores (before SoftMax).",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertForQuestionAnswering",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "class BertForQuestionAnswering(BertPreTrainedModel):\n r\"\"\"\n **start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:\n Labels for position (index) of the start of the labelled span for computing the token classification loss.\n Positions are clamped to the length of the sequence (`sequence_length`).\n Position outside of the sequence are not taken into account for computing the loss.\n **end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:\n Labels for position (index) of the end of the labelled span for computing the token classification loss.\n Positions are clamped to the length of the sequence (`sequence_length`).\n Position outside of the sequence are not taken into account for computing the loss.",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "load_tf_weights_in_bert",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "def load_tf_weights_in_bert(model, config, tf_checkpoint_path):\n \"\"\" Load tf checkpoints in a pytorch model.\n \"\"\"\n try:\n import re\n import numpy as np\n import tensorflow as tf\n except ImportError:\n logger.error(\"Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see \"\n \"https://www.tensorflow.org/install/ for installation instructions.\")",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "gelu",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "def gelu(x):\n \"\"\"Implementation of the gelu activation function.\n For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):\n 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))\n Also see https://arxiv.org/abs/1606.08415\n \"\"\"\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\ndef swish(x):\n return x * torch.sigmoid(x)\nACT2FN = {\"gelu\": gelu, \"relu\": torch.nn.functional.relu, \"swish\": swish}",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "swish",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "def swish(x):\n return x * torch.sigmoid(x)\nACT2FN = {\"gelu\": gelu, \"relu\": torch.nn.functional.relu, \"swish\": swish}\nclass BertConfig(PretrainedConfig):\n r\"\"\"\n :class:`~pytorch_transformers.BertConfig` is the configuration class to store the configuration of a\n `BertModel`.\n Arguments:\n vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.\n hidden_size: Size of the encoder layers and the pooler layer.",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "logger = logging.getLogger(__name__)\nBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {\n 'bert-base-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-pytorch_model.bin\",\n 'bert-large-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-pytorch_model.bin\",\n 'bert-base-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-pytorch_model.bin\",\n 'bert-large-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-pytorch_model.bin\",\n 'bert-base-multilingual-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-pytorch_model.bin\",\n 'bert-base-multilingual-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-pytorch_model.bin\",\n 'bert-base-chinese': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-pytorch_model.bin\",\n 'bert-base-german-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-pytorch_model.bin\",",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BERT_PRETRAINED_MODEL_ARCHIVE_MAP",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {\n 'bert-base-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-pytorch_model.bin\",\n 'bert-large-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-pytorch_model.bin\",\n 'bert-base-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-pytorch_model.bin\",\n 'bert-large-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-pytorch_model.bin\",\n 'bert-base-multilingual-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-pytorch_model.bin\",\n 'bert-base-multilingual-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-pytorch_model.bin\",\n 'bert-base-chinese': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-pytorch_model.bin\",\n 'bert-base-german-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-pytorch_model.bin\",\n 'bert-large-uncased-whole-word-masking': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-pytorch_model.bin\",",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {\n 'bert-base-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json\",\n 'bert-large-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-config.json\",\n 'bert-base-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json\",\n 'bert-large-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-config.json\",\n 'bert-base-multilingual-uncased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-config.json\",\n 'bert-base-multilingual-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-config.json\",\n 'bert-base-chinese': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json\",\n 'bert-base-german-cased': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-config.json\",\n 'bert-large-uncased-whole-word-masking': \"https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-config.json\",",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "ACT2FN",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "ACT2FN = {\"gelu\": gelu, \"relu\": torch.nn.functional.relu, \"swish\": swish}\nclass BertConfig(PretrainedConfig):\n r\"\"\"\n :class:`~pytorch_transformers.BertConfig` is the configuration class to store the configuration of a\n `BertModel`.\n Arguments:\n vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.\n hidden_size: Size of the encoder layers and the pooler layer.\n num_hidden_layers: Number of hidden layers in the Transformer encoder.\n num_attention_heads: Number of attention heads for each attention layer in",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BERT_START_DOCSTRING",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "BERT_START_DOCSTRING = r\"\"\" The BERT model was proposed in\n `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_\n by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer\n pre-trained using a combination of masked language modeling objective and next sentence prediction\n on a large corpus comprising the Toronto Book Corpus and Wikipedia.\n This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and\n refer to the PyTorch documentation for all matter related to general usage and behavior.\n .. _`BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`:\n https://arxiv.org/abs/1810.04805\n .. _`torch.nn.Module`:",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BERT_INPUTS_DOCSTRING",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "peekOfCode": "BERT_INPUTS_DOCSTRING = r\"\"\"\n Inputs:\n **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:\n Indices of input sequence tokens in the vocabulary.\n To match pre-training, BERT input sequence should be formatted with [CLS] and [SEP] tokens as follows:\n (a) For sequence pairs:\n ``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``\n ``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1``\n (b) For single sequences:\n ``tokens: [CLS] the dog is hairy . [SEP]``",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_bert",
+ "documentation": {}
+ },
+ {
+ "label": "BertSelfAttention",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "peekOfCode": "class BertSelfAttention(nn.Module):\n def __init__(self, config):\n super(BertSelfAttention, self).__init__()\n if config.hidden_size % config.num_attention_heads != 0:\n raise ValueError(\n \"The hidden size (%d) is not a multiple of the number of attention \"\n \"heads (%d)\" % (config.hidden_size, config.num_attention_heads))\n self.output_attentions = config.output_attentions\n self.num_attention_heads = config.num_attention_heads\n self.attention_head_size = int(config.hidden_size / config.num_attention_heads)",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "documentation": {}
+ },
+ {
+ "label": "BertAttention",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "peekOfCode": "class BertAttention(nn.Module):\n def __init__(self, config):\n super(BertAttention, self).__init__()\n self.self = BertSelfAttention(config)\n self.output = BertSelfOutput(config)\n def prune_heads(self, heads):\n if len(heads) == 0:\n return\n mask = torch.ones(self.self.num_attention_heads, self.self.attention_head_size)\n for head in heads:",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "documentation": {}
+ },
+ {
+ "label": "GraphormerLayer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "peekOfCode": "class GraphormerLayer(nn.Module):\n def __init__(self, config):\n super(GraphormerLayer, self).__init__()\n self.attention = BertAttention(config)\n self.has_graph_conv = config.graph_conv\n self.mesh_type = config.mesh_type\n if self.has_graph_conv == True:\n if self.mesh_type=='hand':\n self.graph_conv = GraphResBlock(config.hidden_size, config.hidden_size, mesh_type=self.mesh_type)\n elif self.mesh_type=='body':",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "documentation": {}
+ },
+ {
+ "label": "GraphormerEncoder",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "peekOfCode": "class GraphormerEncoder(nn.Module):\n def __init__(self, config):\n super(GraphormerEncoder, self).__init__()\n self.output_attentions = config.output_attentions\n self.output_hidden_states = config.output_hidden_states\n self.layer = nn.ModuleList([GraphormerLayer(config) for _ in range(config.num_hidden_layers)])\n def forward(self, hidden_states, attention_mask, head_mask=None,\n encoder_history_states=None):\n all_hidden_states = ()\n all_attentions = ()",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "documentation": {}
+ },
+ {
+ "label": "EncoderBlock",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "peekOfCode": "class EncoderBlock(BertPreTrainedModel):\n def __init__(self, config):\n super(EncoderBlock, self).__init__(config)\n self.config = config\n # self.embeddings = BertEmbeddings(config)\n self.encoder = GraphormerEncoder(config)\n # self.pooler = BertPooler(config)\n self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)\n self.img_dim = config.img_feature_dim \n try:",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "documentation": {}
+ },
+ {
+ "label": "Graphormer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "peekOfCode": "class Graphormer(BertPreTrainedModel):\n '''\n The archtecture of a transformer encoder block we used in Graphormer\n '''\n def __init__(self, config):\n super(Graphormer, self).__init__(config)\n self.config = config\n self.bert = EncoderBlock(config)\n self.cls_head = nn.Linear(config.hidden_size, self.config.output_feature_dim)\n self.residual = nn.Linear(config.img_feature_dim, self.config.output_feature_dim)",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "documentation": {}
+ },
+ {
+ "label": "LayerNormClass",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "peekOfCode": "LayerNormClass = torch.nn.LayerNorm\nBertLayerNorm = torch.nn.LayerNorm\nclass BertSelfAttention(nn.Module):\n def __init__(self, config):\n super(BertSelfAttention, self).__init__()\n if config.hidden_size % config.num_attention_heads != 0:\n raise ValueError(\n \"The hidden size (%d) is not a multiple of the number of attention \"\n \"heads (%d)\" % (config.hidden_size, config.num_attention_heads))\n self.output_attentions = config.output_attentions",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "documentation": {}
+ },
+ {
+ "label": "BertLayerNorm",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "peekOfCode": "BertLayerNorm = torch.nn.LayerNorm\nclass BertSelfAttention(nn.Module):\n def __init__(self, config):\n super(BertSelfAttention, self).__init__()\n if config.hidden_size % config.num_attention_heads != 0:\n raise ValueError(\n \"The hidden size (%d) is not a multiple of the number of attention \"\n \"heads (%d)\" % (config.hidden_size, config.num_attention_heads))\n self.output_attentions = config.output_attentions\n self.num_attention_heads = config.num_attention_heads",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_graphormer",
+ "documentation": {}
+ },
+ {
+ "label": "PretrainedConfig",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "class PretrainedConfig(object):\n \"\"\" Base class for all configuration classes.\n Handle a few common parameters and methods for loading/downloading/saving configurations.\n \"\"\"\n pretrained_config_archive_map = {}\n def __init__(self, **kwargs):\n self.finetuning_task = kwargs.pop('finetuning_task', None)\n self.num_labels = kwargs.pop('num_labels', 2)\n self.output_attentions = kwargs.pop('output_attentions', False)\n self.output_hidden_states = kwargs.pop('output_hidden_states', False)",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "PreTrainedModel",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "class PreTrainedModel(nn.Module):\n \"\"\" Base class for all models. Handle loading/storing model config and\n a simple interface for dowloading and loading pretrained models.\n \"\"\"\n config_class = PretrainedConfig\n pretrained_model_archive_map = {}\n load_tf_weights = lambda model, config, path: None\n base_model_prefix = \"\"\n input_embeddings = None\n def __init__(self, config, *inputs, **kwargs):",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "Conv1D",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "class Conv1D(nn.Module):\n def __init__(self, nf, nx):\n \"\"\" Conv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2)\n Basically works like a Linear layer but the weights are transposed\n \"\"\"\n super(Conv1D, self).__init__()\n self.nf = nf\n w = torch.empty(nx, nf)\n nn.init.normal_(w, std=0.02)\n self.weight = nn.Parameter(w)",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "PoolerStartLogits",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "class PoolerStartLogits(nn.Module):\n \"\"\" Compute SQuAD start_logits from sequence hidden states. \"\"\"\n def __init__(self, config):\n super(PoolerStartLogits, self).__init__()\n self.dense = nn.Linear(config.hidden_size, 1)\n def forward(self, hidden_states, p_mask=None):\n \"\"\" Args:\n **p_mask**: (`optional`) ``torch.FloatTensor`` of shape `(batch_size, seq_len)`\n invalid position mask such as query and special symbols (PAD, SEP, CLS)\n 1.0 means token should be masked.",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "PoolerEndLogits",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "class PoolerEndLogits(nn.Module):\n \"\"\" Compute SQuAD end_logits from sequence hidden states and start token hidden state.\n \"\"\"\n def __init__(self, config):\n super(PoolerEndLogits, self).__init__()\n self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)\n self.activation = nn.Tanh()\n self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n self.dense_1 = nn.Linear(config.hidden_size, 1)\n def forward(self, hidden_states, start_states=None, start_positions=None, p_mask=None):",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "PoolerAnswerClass",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "class PoolerAnswerClass(nn.Module):\n \"\"\" Compute SQuAD 2.0 answer class from classification and start tokens hidden states. \"\"\"\n def __init__(self, config):\n super(PoolerAnswerClass, self).__init__()\n self.dense_0 = nn.Linear(config.hidden_size * 2, config.hidden_size)\n self.activation = nn.Tanh()\n self.dense_1 = nn.Linear(config.hidden_size, 1, bias=False)\n def forward(self, hidden_states, start_states=None, start_positions=None, cls_index=None):\n \"\"\"\n Args:",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "SQuADHead",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "class SQuADHead(nn.Module):\n r\"\"\" A SQuAD head inspired by XLNet.\n Parameters:\n config (:class:`~pytorch_transformers.XLNetConfig`): Model configuration class with all the parameters of the model.\n Inputs:\n **hidden_states**: ``torch.FloatTensor`` of shape ``(batch_size, seq_len, hidden_size)``\n hidden states of sequence tokens\n **start_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``\n position of the first token for the labeled span.\n **end_positions**: ``torch.LongTensor`` of shape ``(batch_size,)``",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "SequenceSummary",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "class SequenceSummary(nn.Module):\n r\"\"\" Compute a single vector summary of a sequence hidden states according to various possibilities:\n Args of the config class:\n summary_type:\n - 'last' => [default] take the last token hidden state (like XLNet)\n - 'first' => take the first token hidden state (like Bert)\n - 'mean' => take the mean of all tokens hidden states\n - 'token_ids' => supply a Tensor of classification token indices (GPT/GPT-2)\n - 'attn' => Not implemented now, use multi-head attention\n summary_use_proj: Add a projection after the vector extraction",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "prune_linear_layer",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "def prune_linear_layer(layer, index, dim=0):\n \"\"\" Prune a linear layer (a model parameters) to keep only entries in index.\n Return the pruned layer as a new layer with requires_grad=True.\n Used to remove heads.\n \"\"\"\n index = index.to(layer.weight.device)\n W = layer.weight.index_select(dim, index).clone().detach()\n if layer.bias is not None:\n if dim == 1:\n b = layer.bias.clone().detach()",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "prune_conv1d_layer",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "def prune_conv1d_layer(layer, index, dim=1):\n \"\"\" Prune a Conv1D layer (a model parameters) to keep only entries in index.\n A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed.\n Return the pruned layer as a new layer with requires_grad=True.\n Used to remove heads.\n \"\"\"\n index = index.to(layer.weight.device)\n W = layer.weight.index_select(dim, index).clone().detach()\n if dim == 0:\n b = layer.bias.clone().detach()",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "prune_layer",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "def prune_layer(layer, index, dim=None):\n \"\"\" Prune a Conv1D or nn.Linear layer (a model parameters) to keep only entries in index.\n Return the pruned layer as a new layer with requires_grad=True.\n Used to remove heads.\n \"\"\"\n if isinstance(layer, nn.Linear):\n return prune_linear_layer(layer, index, dim=0 if dim is None else dim)\n elif isinstance(layer, Conv1D):\n return prune_conv1d_layer(layer, index, dim=1 if dim is None else dim)\n else:",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "logger = logging.getLogger(__name__)\nCONFIG_NAME = \"config.json\"\nWEIGHTS_NAME = \"pytorch_model.bin\"\nTF_WEIGHTS_NAME = 'model.ckpt'\ntry:\n from torch.nn import Identity\nexcept ImportError:\n # Older PyTorch compatibility\n class Identity(nn.Module):\n r\"\"\"A placeholder identity operator that is argument-insensitive.",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "CONFIG_NAME",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "CONFIG_NAME = \"config.json\"\nWEIGHTS_NAME = \"pytorch_model.bin\"\nTF_WEIGHTS_NAME = 'model.ckpt'\ntry:\n from torch.nn import Identity\nexcept ImportError:\n # Older PyTorch compatibility\n class Identity(nn.Module):\n r\"\"\"A placeholder identity operator that is argument-insensitive.\n \"\"\"",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "WEIGHTS_NAME",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "WEIGHTS_NAME = \"pytorch_model.bin\"\nTF_WEIGHTS_NAME = 'model.ckpt'\ntry:\n from torch.nn import Identity\nexcept ImportError:\n # Older PyTorch compatibility\n class Identity(nn.Module):\n r\"\"\"A placeholder identity operator that is argument-insensitive.\n \"\"\"\n def __init__(self, *args, **kwargs):",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "TF_WEIGHTS_NAME",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "description": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "peekOfCode": "TF_WEIGHTS_NAME = 'model.ckpt'\ntry:\n from torch.nn import Identity\nexcept ImportError:\n # Older PyTorch compatibility\n class Identity(nn.Module):\n r\"\"\"A placeholder identity operator that is argument-insensitive.\n \"\"\"\n def __init__(self, *args, **kwargs):\n super(Identity, self).__init__()",
+ "detail": "modules.PyMAF.models.transformers.bert.modeling_utils",
+ "documentation": {}
+ },
+ {
+ "label": "single_conv",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "class single_conv(nn.Module):\n def __init__(self, in_ch, out_ch):\n super(single_conv, self).__init__()\n self.conv = nn.Sequential(nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1),\n nn.BatchNorm2d(out_ch),\n nn.ReLU(inplace=True),)\n def forward(self, x):\n return self.conv(x)\nclass double_conv(nn.Module):\n def __init__(self, in_ch, out_ch):",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "double_conv",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "class double_conv(nn.Module):\n def __init__(self, in_ch, out_ch):\n super(double_conv, self).__init__()\n self.conv = nn.Sequential(nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1),\n nn.BatchNorm2d(out_ch),\n nn.ReLU(inplace=True),\n nn.Conv2d(out_ch, out_ch, 3, stride=1, padding=1),\n nn.BatchNorm2d(out_ch),\n nn.ReLU(inplace=True))\n def forward(self, x):",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "double_conv_down",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "class double_conv_down(nn.Module):\n def __init__(self, in_ch, out_ch):\n super(double_conv_down, self).__init__()\n self.conv = nn.Sequential(nn.Conv2d(in_ch, out_ch, 3, stride=2, padding=1),\n nn.BatchNorm2d(out_ch),\n nn.ReLU(inplace=True),\n nn.Conv2d(out_ch, out_ch, 3, stride=1, padding=1),\n nn.BatchNorm2d(out_ch),\n nn.ReLU(inplace=True))\n def forward(self, x):",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "double_conv_up",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "class double_conv_up(nn.Module):\n def __init__(self, in_ch, out_ch):\n super(double_conv_up, self).__init__()\n self.conv = nn.Sequential(nn.UpsamplingNearest2d(scale_factor=2),\n nn.Conv2d(in_ch, out_ch, 3, stride=1, padding=1),\n nn.BatchNorm2d(out_ch),\n nn.ReLU(inplace=True),\n nn.Conv2d(out_ch, out_ch, 3, stride=1, padding=1),\n nn.BatchNorm2d(out_ch),\n nn.ReLU(inplace=True))",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "PosEnSine",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "class PosEnSine(nn.Module):\n \"\"\"\n Code borrowed from DETR: models/positional_encoding.py\n output size: b*(2.num_pos_feats)*h*w\n \"\"\"\n def __init__(self, num_pos_feats):\n super(PosEnSine, self).__init__()\n self.num_pos_feats = num_pos_feats\n self.normalize = True\n self.scale = 2 * math.pi",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "softmax_attention",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "def softmax_attention(q, k, v):\n # b x n x d x h x w\n h, w = q.shape[-2], q.shape[-1]\n q = q.flatten(-2).transpose(-2, -1) # b x n x hw x d\n k = k.flatten(-2) # b x n x d x hw\n v = v.flatten(-2).transpose(-2, -1)\n print('softmax', q.shape, k.shape, v.shape)\n N = k.shape[-1] # ?????? maybe change to k.shape[-2]????\n attn = torch.matmul(q / N ** 0.5, k)\n attn = F.softmax(attn, dim=-1)",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "dotproduct_attention",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "def dotproduct_attention(q, k, v):\n # b x n x d x h x w\n h, w = q.shape[-2], q.shape[-1]\n q = q.flatten(-2).transpose(-2, -1) # b x n x hw x d\n k = k.flatten(-2) # b x n x d x hw\n v = v.flatten(-2).transpose(-2, -1)\n N = k.shape[-1]\n attn = None\n tmp = torch.matmul(k, v) / N\n output = torch.matmul(q, tmp)",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "long_range_attention",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "def long_range_attention(q, k, v, P_h, P_w): # fixed patch size\n B, N, C, qH, qW = q.size()\n _, _, _, kH, kW = k.size()\n qQ_h, qQ_w = qH // P_h, qW // P_w\n kQ_h, kQ_w = kH // P_h, kW // P_w\n q = q.reshape(B, N, C, qQ_h, P_h, qQ_w, P_w)\n k = k.reshape(B, N, C, kQ_h, P_h, kQ_w, P_w)\n v = v.reshape(B, N, -1, kQ_h, P_h, kQ_w, P_w)\n q = q.permute(0, 1, 4, 6, 2, 3, 5) # [b, n, Ph, Pw, d, Qh, Qw]\n k = k.permute(0, 1, 4, 6, 2, 3, 5)",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "short_range_attention",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "def short_range_attention(q, k, v, Q_h, Q_w): # fixed patch number\n B, N, C, qH, qW = q.size()\n _, _, _, kH, kW = k.size()\n qP_h, qP_w = qH // Q_h, qW // Q_w\n kP_h, kP_w = kH // Q_h, kW // Q_w\n q = q.reshape(B, N, C, Q_h, qP_h, Q_w, qP_w)\n k = k.reshape(B, N, C, Q_h, kP_h, Q_w, kP_w)\n v = v.reshape(B, N, -1, Q_h, kP_h, Q_w, kP_w)\n q = q.permute(0, 1, 3, 5, 2, 4, 6) # [b, n, Qh, Qw, d, Ph, Pw]\n k = k.permute(0, 1, 3, 5, 2, 4, 6)",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "space_to_depth",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "def space_to_depth(x, block_size):\n x_shape = x.shape\n c, h, w = x_shape[-3:]\n if len(x.shape) >= 5:\n x = x.view(-1, c, h, w)\n unfolded_x = torch.nn.functional.unfold(x, block_size, stride=block_size)\n return unfolded_x.view(*x_shape[0:-3], c * block_size ** 2, h // block_size, w // block_size)\ndef depth_to_space(x, block_size):\n x_shape = x.shape\n c, h, w = x_shape[-3:]",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "depth_to_space",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "def depth_to_space(x, block_size):\n x_shape = x.shape\n c, h, w = x_shape[-3:]\n x = x.view(-1, c, h, w)\n y = torch.nn.functional.pixel_shuffle(x, block_size)\n return y.view(*x_shape[0:-3], -1, h*block_size, w*block_size)\ndef patch_attention(q, k, v, P):\n # q: [b, nhead, c, h, w]\n q_patch = space_to_depth(q, P) # [b, nhead, cP^2, h/P, w/P]\n k_patch = space_to_depth(k, P)",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "patch_attention",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.transformers.net_utils",
+ "description": "modules.PyMAF.models.transformers.net_utils",
+ "peekOfCode": "def patch_attention(q, k, v, P):\n # q: [b, nhead, c, h, w]\n q_patch = space_to_depth(q, P) # [b, nhead, cP^2, h/P, w/P]\n k_patch = space_to_depth(k, P)\n v_patch = space_to_depth(v, P)\n # output: [b, nhead, cP^2, h/P, w/P]\n # attn: [b, nhead, h/P*w/P, h/P*w/P]\n output, attn = softmax_attention(q_patch, k_patch, v_patch) \n output = depth_to_space(output, P) # output: [b, nhead, c, h, w]\n return output, attn",
+ "detail": "modules.PyMAF.models.transformers.net_utils",
+ "documentation": {}
+ },
+ {
+ "label": "TransformerDecoderUnit",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.texformer",
+ "description": "modules.PyMAF.models.transformers.texformer",
+ "peekOfCode": "class TransformerDecoderUnit(nn.Module):\n def __init__(self, feat_dim, n_head=8, pos_en_flag=True, attn_type='softmax', P=None):\n super(TransformerDecoderUnit, self).__init__()\n self.feat_dim = feat_dim\n self.attn_type = attn_type\n self.pos_en_flag = pos_en_flag\n self.P = P\n self.pos_en = PosEnSine(self.feat_dim // 2)\n self.attn = OurMultiheadAttention(feat_dim, n_head) # cross-attention\n self.linear1 = nn.Conv2d(self.feat_dim, self.feat_dim, 1)",
+ "detail": "modules.PyMAF.models.transformers.texformer",
+ "documentation": {}
+ },
+ {
+ "label": "Unet",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.texformer",
+ "description": "modules.PyMAF.models.transformers.texformer",
+ "peekOfCode": "class Unet(nn.Module):\n def __init__(self, in_ch, feat_ch, out_ch):\n super().__init__()\n self.conv_in = single_conv(in_ch, feat_ch)\n self.conv1 = double_conv_down(feat_ch, feat_ch)\n self.conv2 = double_conv_down(feat_ch, feat_ch)\n self.conv3 = double_conv(feat_ch, feat_ch)\n self.conv4 = double_conv_up(feat_ch, feat_ch)\n self.conv5 = double_conv_up(feat_ch, feat_ch)\n self.conv6 = double_conv(feat_ch, out_ch)",
+ "detail": "modules.PyMAF.models.transformers.texformer",
+ "documentation": {}
+ },
+ {
+ "label": "Texformer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.texformer",
+ "description": "modules.PyMAF.models.transformers.texformer",
+ "peekOfCode": "class Texformer(nn.Module):\n def __init__(self, opts):\n super().__init__()\n self.feat_dim = opts.feat_dim\n src_ch = opts.src_ch\n tgt_ch = opts.tgt_ch\n out_ch = opts.out_ch\n self.mask_fusion = opts.mask_fusion\n if not self.mask_fusion:\n v_ch = out_ch",
+ "detail": "modules.PyMAF.models.transformers.texformer",
+ "documentation": {}
+ },
+ {
+ "label": "SpatialAttention",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.tokenlearner",
+ "description": "modules.PyMAF.models.transformers.tokenlearner",
+ "peekOfCode": "class SpatialAttention(nn.Module):\n def __init__(self) -> None:\n super().__init__()\n self.conv = nn.Sequential(\n nn.Conv2d(2, 1, kernel_size=(1,1), stride=1),\n nn.BatchNorm2d(1),\n nn.ReLU()\n )\n self.sgap = nn.AvgPool2d(2)\n def forward(self, x):",
+ "detail": "modules.PyMAF.models.transformers.tokenlearner",
+ "documentation": {}
+ },
+ {
+ "label": "TokenLearner",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.tokenlearner",
+ "description": "modules.PyMAF.models.transformers.tokenlearner",
+ "peekOfCode": "class TokenLearner(nn.Module):\n def __init__(self, S) -> None:\n super().__init__()\n self.S = S\n self.tokenizers = nn.ModuleList([SpatialAttention() for _ in range(S)])\n def forward(self, x):\n B, _, _, C = x.shape\n Z = torch.Tensor(B, self.S, C).to(x)\n for i in range(self.S):\n Ai, _ = self.tokenizers[i](x) # [B, C]",
+ "detail": "modules.PyMAF.models.transformers.tokenlearner",
+ "documentation": {}
+ },
+ {
+ "label": "TokenFuser",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.tokenlearner",
+ "description": "modules.PyMAF.models.transformers.tokenlearner",
+ "peekOfCode": "class TokenFuser(nn.Module):\n def __init__(self, H, W, C, S) -> None:\n super().__init__()\n self.projection = nn.Linear(S, S, bias=False)\n self.Bi = nn.Linear(C, S)\n self.spatial_attn = SpatialAttention()\n self.S = S\n def forward(self, y, x):\n B, S, C = y.shape\n B, H, W, C = x.shape",
+ "detail": "modules.PyMAF.models.transformers.tokenlearner",
+ "documentation": {}
+ },
+ {
+ "label": "OurMultiheadAttention",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.transformer_basics",
+ "description": "modules.PyMAF.models.transformers.transformer_basics",
+ "peekOfCode": "class OurMultiheadAttention(nn.Module):\n def __init__(self, q_feat_dim, k_feat_dim, out_feat_dim, n_head, d_k=None, d_v=None):\n super(OurMultiheadAttention, self).__init__()\n if d_k is None:\n d_k = out_feat_dim // n_head\n if d_v is None:\n d_v = out_feat_dim // n_head\n self.n_head = n_head\n self.d_k = d_k\n self.d_v = d_v",
+ "detail": "modules.PyMAF.models.transformers.transformer_basics",
+ "documentation": {}
+ },
+ {
+ "label": "TransformerEncoderUnit",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.transformer_basics",
+ "description": "modules.PyMAF.models.transformers.transformer_basics",
+ "peekOfCode": "class TransformerEncoderUnit(nn.Module):\n def __init__(self, feat_dim, n_head=8, pos_en_flag=True, attn_type='softmax', P=None):\n super(TransformerEncoderUnit, self).__init__()\n self.feat_dim = feat_dim\n self.attn_type = attn_type\n self.pos_en_flag = pos_en_flag\n self.P = P\n self.pos_en = PosEnSine(self.feat_dim // 2)\n self.attn = OurMultiheadAttention(feat_dim, n_head)\n self.linear1 = nn.Conv2d(self.feat_dim, self.feat_dim, 1)",
+ "detail": "modules.PyMAF.models.transformers.transformer_basics",
+ "documentation": {}
+ },
+ {
+ "label": "TransformerEncoderUnitSparse",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.transformer_basics",
+ "description": "modules.PyMAF.models.transformers.transformer_basics",
+ "peekOfCode": "class TransformerEncoderUnitSparse(nn.Module):\n def __init__(self, feat_dim, n_head=8, pos_en_flag=True, ahw=None):\n super(TransformerEncoderUnitSparse, self).__init__()\n self.feat_dim = feat_dim\n self.pos_en_flag = pos_en_flag\n self.ahw = ahw # [Ph, Pw, Qh, Qw]\n self.pos_en = PosEnSine(self.feat_dim // 2)\n self.attn1 = OurMultiheadAttention(feat_dim, n_head) # long range\n self.attn2 = OurMultiheadAttention(feat_dim, n_head) # short range\n self.linear1 = nn.Conv2d(self.feat_dim, self.feat_dim, 1)",
+ "detail": "modules.PyMAF.models.transformers.transformer_basics",
+ "documentation": {}
+ },
+ {
+ "label": "TransformerDecoderUnit",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.transformer_basics",
+ "description": "modules.PyMAF.models.transformers.transformer_basics",
+ "peekOfCode": "class TransformerDecoderUnit(nn.Module):\n def __init__(self, feat_dim, n_head=8, pos_en_flag=True, attn_type='softmax', P=None):\n super(TransformerDecoderUnit, self).__init__()\n self.feat_dim = feat_dim\n self.attn_type = attn_type\n self.pos_en_flag = pos_en_flag\n self.P = P\n self.pos_en = PosEnSine(self.feat_dim // 2)\n self.attn1 = OurMultiheadAttention(feat_dim, n_head) # self-attention\n self.attn2 = OurMultiheadAttention(feat_dim, n_head) # cross-attention",
+ "detail": "modules.PyMAF.models.transformers.transformer_basics",
+ "documentation": {}
+ },
+ {
+ "label": "TransformerDecoderUnitSparse",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.transformers.transformer_basics",
+ "description": "modules.PyMAF.models.transformers.transformer_basics",
+ "peekOfCode": "class TransformerDecoderUnitSparse(nn.Module):\n def __init__(self, feat_dim, n_head=8, pos_en_flag=True, ahw=None):\n super(TransformerDecoderUnitSparse, self).__init__()\n self.feat_dim = feat_dim\n self.ahw = ahw # [Ph_tgt, Pw_tgt, Qh_tgt, Qw_tgt, Ph_src, Pw_src, Qh_tgt, Qw_tgt]\n self.pos_en_flag = pos_en_flag\n self.pos_en = PosEnSine(self.feat_dim // 2)\n self.attn1_1 = OurMultiheadAttention(feat_dim, n_head) # self-attention: long\n self.attn1_2 = OurMultiheadAttention(feat_dim, n_head) # self-attention: short\n self.attn2_1 = OurMultiheadAttention(feat_dim, n_head) # cross-attention: self-attention-long + cross-attention-short",
+ "detail": "modules.PyMAF.models.transformers.transformer_basics",
+ "documentation": {}
+ },
+ {
+ "label": "BertSelfAttention",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.attention",
+ "description": "modules.PyMAF.models.attention",
+ "peekOfCode": "class BertSelfAttention(nn.Module):\n def __init__(self, config):\n super(BertSelfAttention, self).__init__()\n if config.hidden_size % config.num_attention_heads != 0:\n raise ValueError(\n \"The hidden size (%d) is not a multiple of the number of attention \"\n \"heads (%d)\" % (config.hidden_size, config.num_attention_heads))\n self.output_attentions = config.output_attentions\n self.num_attention_heads = config.num_attention_heads\n self.attention_head_size = int(config.hidden_size / config.num_attention_heads)",
+ "detail": "modules.PyMAF.models.attention",
+ "documentation": {}
+ },
+ {
+ "label": "BertAttention",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.attention",
+ "description": "modules.PyMAF.models.attention",
+ "peekOfCode": "class BertAttention(nn.Module):\n def __init__(self, config):\n super(BertAttention, self).__init__()\n self.self = BertSelfAttention(config)\n self.output = BertSelfOutput(config)\n def prune_heads(self, heads):\n if len(heads) == 0:\n return\n mask = torch.ones(self.self.num_attention_heads, self.self.attention_head_size)\n for head in heads:",
+ "detail": "modules.PyMAF.models.attention",
+ "documentation": {}
+ },
+ {
+ "label": "AttLayer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.attention",
+ "description": "modules.PyMAF.models.attention",
+ "peekOfCode": "class AttLayer(nn.Module):\n def __init__(self, config):\n super(AttLayer, self).__init__()\n self.attention = BertAttention(config)\n self.intermediate = BertIntermediate(config)\n self.output = BertOutput(config)\n def MHA(self, hidden_states, attention_mask, head_mask=None,\n history_state=None):\n attention_outputs = self.attention(hidden_states, attention_mask,\n head_mask, history_state)",
+ "detail": "modules.PyMAF.models.attention",
+ "documentation": {}
+ },
+ {
+ "label": "AttEncoder",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.attention",
+ "description": "modules.PyMAF.models.attention",
+ "peekOfCode": "class AttEncoder(nn.Module):\n def __init__(self, config):\n super(AttEncoder, self).__init__()\n self.output_attentions = config.output_attentions\n self.output_hidden_states = config.output_hidden_states\n self.layer = nn.ModuleList([AttLayer(config) for _ in range(config.num_hidden_layers)])\n def forward(self, hidden_states, attention_mask, head_mask=None,\n encoder_history_states=None):\n all_hidden_states = ()\n all_attentions = ()",
+ "detail": "modules.PyMAF.models.attention",
+ "documentation": {}
+ },
+ {
+ "label": "EncoderBlock",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.attention",
+ "description": "modules.PyMAF.models.attention",
+ "peekOfCode": "class EncoderBlock(BertPreTrainedModel):\n def __init__(self, config):\n super(EncoderBlock, self).__init__(config)\n self.config = config\n # self.embeddings = BertEmbeddings(config)\n self.encoder = AttEncoder(config)\n # self.pooler = BertPooler(config)\n self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)\n self.img_dim = config.img_feature_dim \n try:",
+ "detail": "modules.PyMAF.models.attention",
+ "documentation": {}
+ },
+ {
+ "label": "Graphormer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.attention",
+ "description": "modules.PyMAF.models.attention",
+ "peekOfCode": "class Graphormer(BertPreTrainedModel):\n '''\n The archtecture of a transformer encoder block we used in Graphormer\n '''\n def __init__(self, config):\n super(Graphormer, self).__init__(config)\n self.config = config\n self.bert = EncoderBlock(config)\n self.cls_head = nn.Linear(config.hidden_size, self.config.output_feature_dim)\n self.residual = nn.Linear(config.img_feature_dim, self.config.output_feature_dim)",
+ "detail": "modules.PyMAF.models.attention",
+ "documentation": {}
+ },
+ {
+ "label": "get_att_block",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.attention",
+ "description": "modules.PyMAF.models.attention",
+ "peekOfCode": "def get_att_block(img_feature_dim=2048, output_feat_dim=512, hidden_feat_dim=1024, num_attention_heads=4, num_hidden_layers=1):\n config_class = BertConfig\n # config = config_class.from_pretrained('models/transformers/bert/bert-base-uncased/')\n config = config_class.from_pretrained('bert-base-uncased')\n interm_size_scale = 2\n config.output_attentions = False\n # config.hidden_dropout_prob = args.drop_out\n config.img_feature_dim = img_feature_dim\n # config.output_feature_dim = output_feat_dim\n config.hidden_size = hidden_feat_dim",
+ "detail": "modules.PyMAF.models.attention",
+ "documentation": {}
+ },
+ {
+ "label": "LayerNormClass",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.attention",
+ "description": "modules.PyMAF.models.attention",
+ "peekOfCode": "LayerNormClass = torch.nn.LayerNorm\nBertLayerNorm = torch.nn.LayerNorm\nfrom .transformers.bert import BertConfig\nclass BertSelfAttention(nn.Module):\n def __init__(self, config):\n super(BertSelfAttention, self).__init__()\n if config.hidden_size % config.num_attention_heads != 0:\n raise ValueError(\n \"The hidden size (%d) is not a multiple of the number of attention \"\n \"heads (%d)\" % (config.hidden_size, config.num_attention_heads))",
+ "detail": "modules.PyMAF.models.attention",
+ "documentation": {}
+ },
+ {
+ "label": "BertLayerNorm",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.attention",
+ "description": "modules.PyMAF.models.attention",
+ "peekOfCode": "BertLayerNorm = torch.nn.LayerNorm\nfrom .transformers.bert import BertConfig\nclass BertSelfAttention(nn.Module):\n def __init__(self, config):\n super(BertSelfAttention, self).__init__()\n if config.hidden_size % config.num_attention_heads != 0:\n raise ValueError(\n \"The hidden size (%d) is not a multiple of the number of attention \"\n \"heads (%d)\" % (config.hidden_size, config.num_attention_heads))\n self.output_attentions = config.output_attentions",
+ "detail": "modules.PyMAF.models.attention",
+ "documentation": {}
+ },
+ {
+ "label": "Bottleneck",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.hmr",
+ "description": "modules.PyMAF.models.hmr",
+ "peekOfCode": "class Bottleneck(nn.Module):\n \"\"\" Redefinition of Bottleneck residual block\n Adapted from the official PyTorch implementation\n \"\"\"\n expansion = 4\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super().__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,",
+ "detail": "modules.PyMAF.models.hmr",
+ "documentation": {}
+ },
+ {
+ "label": "ResNet_Backbone",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.hmr",
+ "description": "modules.PyMAF.models.hmr",
+ "peekOfCode": "class ResNet_Backbone(nn.Module):\n \"\"\" Feature Extrator with ResNet backbone\n \"\"\"\n def __init__(self, model='res50', pretrained=True):\n if model == 'res50':\n block, layers = Bottleneck, [3, 4, 6, 3]\n else:\n pass # TODO\n self.inplanes = 64\n super().__init__()",
+ "detail": "modules.PyMAF.models.hmr",
+ "documentation": {}
+ },
+ {
+ "label": "HMR",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.hmr",
+ "description": "modules.PyMAF.models.hmr",
+ "peekOfCode": "class HMR(nn.Module):\n \"\"\" SMPL Iterative Regressor with ResNet50 backbone\n \"\"\"\n def __init__(self, block, layers, smpl_mean_params):\n self.inplanes = 64\n super().__init__()\n npose = 24 * 6\n self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,\n bias=False)\n self.bn1 = nn.BatchNorm2d(64)",
+ "detail": "modules.PyMAF.models.hmr",
+ "documentation": {}
+ },
+ {
+ "label": "hmr",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.hmr",
+ "description": "modules.PyMAF.models.hmr",
+ "peekOfCode": "def hmr(smpl_mean_params, pretrained=True, **kwargs):\n \"\"\" Constructs an HMR model with ResNet50 backbone.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n model = HMR(Bottleneck, [3, 4, 6, 3], smpl_mean_params, **kwargs)\n if pretrained:\n resnet_imagenet = resnet.resnet50(pretrained=True)\n model.load_state_dict(resnet_imagenet.state_dict(),strict=False)\n return model",
+ "detail": "modules.PyMAF.models.hmr",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.hmr",
+ "description": "modules.PyMAF.models.hmr",
+ "peekOfCode": "logger = logging.getLogger(__name__)\nBN_MOMENTUM = 0.1\nclass Bottleneck(nn.Module):\n \"\"\" Redefinition of Bottleneck residual block\n Adapted from the official PyTorch implementation\n \"\"\"\n expansion = 4\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super().__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)",
+ "detail": "modules.PyMAF.models.hmr",
+ "documentation": {}
+ },
+ {
+ "label": "BN_MOMENTUM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.hmr",
+ "description": "modules.PyMAF.models.hmr",
+ "peekOfCode": "BN_MOMENTUM = 0.1\nclass Bottleneck(nn.Module):\n \"\"\" Redefinition of Bottleneck residual block\n Adapted from the official PyTorch implementation\n \"\"\"\n expansion = 4\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super().__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)",
+ "detail": "modules.PyMAF.models.hmr",
+ "documentation": {}
+ },
+ {
+ "label": "HighResolutionModule",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.hr_module",
+ "description": "modules.PyMAF.models.hr_module",
+ "peekOfCode": "class HighResolutionModule(nn.Module):\n def __init__(self, num_branches, blocks, num_blocks, num_inchannels,\n num_channels, fuse_method, multi_scale_output=True):\n super().__init__()\n self._check_branches(\n num_branches, blocks, num_blocks, num_inchannels, num_channels)\n self.num_inchannels = num_inchannels\n self.fuse_method = fuse_method\n self.num_branches = num_branches\n self.multi_scale_output = multi_scale_output",
+ "detail": "modules.PyMAF.models.hr_module",
+ "documentation": {}
+ },
+ {
+ "label": "PoseHighResolutionNet",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.hr_module",
+ "description": "modules.PyMAF.models.hr_module",
+ "peekOfCode": "class PoseHighResolutionNet(nn.Module):\n def __init__(self, cfg, pretrained=True, global_mode=False):\n self.inplanes = 64\n extra = cfg.HR_MODEL.EXTRA\n super().__init__()\n # stem net\n self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,\n bias=False)\n self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)\n self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,",
+ "detail": "modules.PyMAF.models.hr_module",
+ "documentation": {}
+ },
+ {
+ "label": "get_hrnet_encoder",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.hr_module",
+ "description": "modules.PyMAF.models.hr_module",
+ "peekOfCode": "def get_hrnet_encoder(cfg, init_weight=True, global_mode=False, **kwargs):\n model = PoseHighResolutionNet(cfg, global_mode=global_mode)\n if init_weight:\n if cfg.HR_MODEL.PRETR_SET in ['imagenet']:\n model.init_weights(cfg.HR_MODEL.PRETRAINED_IM)\n logger.info('loaded HRNet imagenet pretrained model')\n elif cfg.HR_MODEL.PRETR_SET in ['coco']:\n model.init_weights(cfg.HR_MODEL.PRETRAINED_COCO)\n logger.info('loaded HRNet coco pretrained model')\n else:",
+ "detail": "modules.PyMAF.models.hr_module",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.hr_module",
+ "description": "modules.PyMAF.models.hr_module",
+ "peekOfCode": "logger = logging.getLogger(__name__)\nBN_MOMENTUM = 0.1\nclass HighResolutionModule(nn.Module):\n def __init__(self, num_branches, blocks, num_blocks, num_inchannels,\n num_channels, fuse_method, multi_scale_output=True):\n super().__init__()\n self._check_branches(\n num_branches, blocks, num_blocks, num_inchannels, num_channels)\n self.num_inchannels = num_inchannels\n self.fuse_method = fuse_method",
+ "detail": "modules.PyMAF.models.hr_module",
+ "documentation": {}
+ },
+ {
+ "label": "BN_MOMENTUM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.hr_module",
+ "description": "modules.PyMAF.models.hr_module",
+ "peekOfCode": "BN_MOMENTUM = 0.1\nclass HighResolutionModule(nn.Module):\n def __init__(self, num_branches, blocks, num_blocks, num_inchannels,\n num_channels, fuse_method, multi_scale_output=True):\n super().__init__()\n self._check_branches(\n num_branches, blocks, num_blocks, num_inchannels, num_channels)\n self.num_inchannels = num_inchannels\n self.fuse_method = fuse_method\n self.num_branches = num_branches",
+ "detail": "modules.PyMAF.models.hr_module",
+ "documentation": {}
+ },
+ {
+ "label": "blocks_dict",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.hr_module",
+ "description": "modules.PyMAF.models.hr_module",
+ "peekOfCode": "blocks_dict = {\n 'BASIC': BasicBlock,\n 'BOTTLENECK': Bottleneck\n}\nclass PoseHighResolutionNet(nn.Module):\n def __init__(self, cfg, pretrained=True, global_mode=False):\n self.inplanes = 64\n extra = cfg.HR_MODEL.EXTRA\n super().__init__()\n # stem net",
+ "detail": "modules.PyMAF.models.hr_module",
+ "documentation": {}
+ },
+ {
+ "label": "TransformerDecoderUnit",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.maf_extractor",
+ "description": "modules.PyMAF.models.maf_extractor",
+ "peekOfCode": "class TransformerDecoderUnit(nn.Module):\n def __init__(self, feat_dim, attri_dim=0, n_head=8, pos_en_flag=True, attn_type='softmax', P=None):\n super(TransformerDecoderUnit, self).__init__()\n self.feat_dim = feat_dim\n self.attn_type = attn_type\n self.pos_en_flag = pos_en_flag\n self.P = P\n assert attri_dim == 0\n if self.pos_en_flag:\n pe_dim = 10",
+ "detail": "modules.PyMAF.models.maf_extractor",
+ "documentation": {}
+ },
+ {
+ "label": "Mesh_Sampler",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.maf_extractor",
+ "description": "modules.PyMAF.models.maf_extractor",
+ "peekOfCode": "class Mesh_Sampler(nn.Module):\n ''' Mesh Up/Down-sampling\n '''\n def __init__(self, type='smpl', level=2, device=torch.device('cuda'), option=None):\n super().__init__()\n # downsample SMPL mesh and assign part labels\n if type == 'smpl':\n # from https://github.com/nkolot/GraphCMR/blob/master/data/mesh_downsampling.npz\n smpl_mesh_graph = np.load('data/smpl_downsampling.npz', allow_pickle=True, encoding='latin1')\n A = smpl_mesh_graph['A']",
+ "detail": "modules.PyMAF.models.maf_extractor",
+ "documentation": {}
+ },
+ {
+ "label": "MAF_Extractor",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.maf_extractor",
+ "description": "modules.PyMAF.models.maf_extractor",
+ "peekOfCode": "class MAF_Extractor(nn.Module):\n ''' Mesh-aligned Feature Extrator\n As discussed in the paper, we extract mesh-aligned features based on 2D projection of the mesh vertices.\n The features extrated from spatial feature maps will go through a MLP for dimension reduction.\n '''\n def __init__(self, filter_channels, device=torch.device('cuda'), iwp_cam_mode=True, option=None):\n super().__init__()\n self.device = device\n self.filters = []\n self.num_views = 1",
+ "detail": "modules.PyMAF.models.maf_extractor",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.maf_extractor",
+ "description": "modules.PyMAF.models.maf_extractor",
+ "peekOfCode": "logger = logging.getLogger(__name__)\nfrom .transformers.net_utils import PosEnSine\nfrom .transformers.transformer_basics import OurMultiheadAttention\nfrom utils.iuvmap import iuv_img2map, iuv_map2img, seg_img2map\nfrom .smpl import get_smpl_tpose\nfrom utils.imutils import j2d_processing\nclass TransformerDecoderUnit(nn.Module):\n def __init__(self, feat_dim, attri_dim=0, n_head=8, pos_en_flag=True, attn_type='softmax', P=None):\n super(TransformerDecoderUnit, self).__init__()\n self.feat_dim = feat_dim",
+ "detail": "modules.PyMAF.models.maf_extractor",
+ "documentation": {}
+ },
+ {
+ "label": "BasicBlock",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.pose_resnet",
+ "description": "modules.PyMAF.models.pose_resnet",
+ "peekOfCode": "class BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(BasicBlock, self).__init__()\n self.conv1 = conv3x3(inplanes, planes, stride)\n self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)\n self.relu = nn.ReLU(inplace=True)\n self.conv2 = conv3x3(planes, planes)\n self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)\n self.downsample = downsample",
+ "detail": "modules.PyMAF.models.pose_resnet",
+ "documentation": {}
+ },
+ {
+ "label": "Bottleneck",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.pose_resnet",
+ "description": "modules.PyMAF.models.pose_resnet",
+ "peekOfCode": "class Bottleneck(nn.Module):\n expansion = 4\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(Bottleneck, self).__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)\n self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,",
+ "detail": "modules.PyMAF.models.pose_resnet",
+ "documentation": {}
+ },
+ {
+ "label": "PoseResNet",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.pose_resnet",
+ "description": "modules.PyMAF.models.pose_resnet",
+ "peekOfCode": "class PoseResNet(nn.Module):\n def __init__(self, block, layers, cfg, global_mode, **kwargs):\n self.inplanes = 64\n extra = cfg.POSE_RES_MODEL.EXTRA\n self.extra = extra\n self.deconv_with_bias = extra.DECONV_WITH_BIAS\n super(PoseResNet, self).__init__()\n self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,\n bias=False)\n self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)",
+ "detail": "modules.PyMAF.models.pose_resnet",
+ "documentation": {}
+ },
+ {
+ "label": "conv3x3",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.pose_resnet",
+ "description": "modules.PyMAF.models.pose_resnet",
+ "peekOfCode": "def conv3x3(in_planes, out_planes, stride=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(\n in_planes, out_planes, kernel_size=3, stride=stride,\n padding=1, bias=False\n )\nclass BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(BasicBlock, self).__init__()",
+ "detail": "modules.PyMAF.models.pose_resnet",
+ "documentation": {}
+ },
+ {
+ "label": "get_resnet_encoder",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.pose_resnet",
+ "description": "modules.PyMAF.models.pose_resnet",
+ "peekOfCode": "def get_resnet_encoder(cfg, init_weight=True, global_mode=False, **kwargs):\n num_layers = cfg.POSE_RES_MODEL.EXTRA.NUM_LAYERS\n block_class, layers = resnet_spec[num_layers]\n model = PoseResNet(block_class, layers, cfg, global_mode, **kwargs)\n if init_weight:\n if num_layers == 50:\n if cfg.POSE_RES_MODEL.PRETR_SET in ['imagenet']:\n model.init_weights(cfg.POSE_RES_MODEL.PRETRAINED_IM)\n logger.info('loaded ResNet imagenet pretrained model')\n elif cfg.POSE_RES_MODEL.PRETR_SET in ['coco']:",
+ "detail": "modules.PyMAF.models.pose_resnet",
+ "documentation": {}
+ },
+ {
+ "label": "BN_MOMENTUM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.pose_resnet",
+ "description": "modules.PyMAF.models.pose_resnet",
+ "peekOfCode": "BN_MOMENTUM = 0.1\nlogger = logging.getLogger(__name__)\ndef conv3x3(in_planes, out_planes, stride=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(\n in_planes, out_planes, kernel_size=3, stride=stride,\n padding=1, bias=False\n )\nclass BasicBlock(nn.Module):\n expansion = 1",
+ "detail": "modules.PyMAF.models.pose_resnet",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.pose_resnet",
+ "description": "modules.PyMAF.models.pose_resnet",
+ "peekOfCode": "logger = logging.getLogger(__name__)\ndef conv3x3(in_planes, out_planes, stride=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(\n in_planes, out_planes, kernel_size=3, stride=stride,\n padding=1, bias=False\n )\nclass BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None):",
+ "detail": "modules.PyMAF.models.pose_resnet",
+ "documentation": {}
+ },
+ {
+ "label": "resnet_spec",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.pose_resnet",
+ "description": "modules.PyMAF.models.pose_resnet",
+ "peekOfCode": "resnet_spec = {\n 18: (BasicBlock, [2, 2, 2, 2]),\n 34: (BasicBlock, [3, 4, 6, 3]),\n 50: (Bottleneck, [3, 4, 6, 3]),\n 101: (Bottleneck, [3, 4, 23, 3]),\n 152: (Bottleneck, [3, 8, 36, 3])\n}\ndef get_resnet_encoder(cfg, init_weight=True, global_mode=False, **kwargs):\n num_layers = cfg.POSE_RES_MODEL.EXTRA.NUM_LAYERS\n block_class, layers = resnet_spec[num_layers]",
+ "detail": "modules.PyMAF.models.pose_resnet",
+ "documentation": {}
+ },
+ {
+ "label": "Regressor",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.pymaf_net",
+ "description": "modules.PyMAF.models.pymaf_net",
+ "peekOfCode": "class Regressor(nn.Module):\n def __init__(self, feat_dim, smpl_mean_params, use_cam_feats=False, feat_dim_hand=0, feat_dim_face=0, bhf_names=['body'], smpl_models={}):\n super().__init__()\n npose = 24 * 6\n shape_dim = 10\n cam_dim = 3\n hand_dim = 15 * 6\n face_dim = 3 * 6 + 10\n self.body_feat_dim = feat_dim\n self.smpl_mode = (cfg.MODEL.MESH_MODEL == 'smpl')",
+ "detail": "modules.PyMAF.models.pymaf_net",
+ "documentation": {}
+ },
+ {
+ "label": "PyMAF",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.pymaf_net",
+ "description": "modules.PyMAF.models.pymaf_net",
+ "peekOfCode": "class PyMAF(nn.Module):\n \"\"\" PyMAF based Regression Network for Human Mesh Recovery / Full-body Mesh Recovery\n PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop, in ICCV, 2021\n PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images, arXiv:2207.06400, 2022\n \"\"\"\n def __init__(self, smpl_mean_params=SMPL_MEAN_PARAMS, pretrained=True, device=torch.device('cuda')):\n super().__init__()\n self.device = device\n self.smpl_mode = (cfg.MODEL.MESH_MODEL == 'smpl')\n self.smplx_mode = (cfg.MODEL.MESH_MODEL == 'smplx')",
+ "detail": "modules.PyMAF.models.pymaf_net",
+ "documentation": {}
+ },
+ {
+ "label": "get_attention_modules",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.pymaf_net",
+ "description": "modules.PyMAF.models.pymaf_net",
+ "peekOfCode": "def get_attention_modules(module_keys, img_feature_dim_list, hidden_feat_dim, n_iter, num_attention_heads=1):\n align_attention = nn.ModuleDict()\n for k in module_keys:\n align_attention[k] = nn.ModuleList()\n for i in range(n_iter):\n align_attention[k].append(get_att_block(img_feature_dim=img_feature_dim_list[k][i], hidden_feat_dim=hidden_feat_dim, num_attention_heads=num_attention_heads))\n return align_attention\ndef get_fusion_modules(module_keys, ma_feat_dim, grid_feat_dim, n_iter, out_feat_len):\n feat_fusion = nn.ModuleDict()\n for k in module_keys:",
+ "detail": "modules.PyMAF.models.pymaf_net",
+ "documentation": {}
+ },
+ {
+ "label": "get_fusion_modules",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.pymaf_net",
+ "description": "modules.PyMAF.models.pymaf_net",
+ "peekOfCode": "def get_fusion_modules(module_keys, ma_feat_dim, grid_feat_dim, n_iter, out_feat_len):\n feat_fusion = nn.ModuleDict()\n for k in module_keys:\n feat_fusion[k] = nn.ModuleList()\n for i in range(n_iter):\n feat_fusion[k].append(nn.Linear(grid_feat_dim + ma_feat_dim[k], out_feat_len[k]))\n return feat_fusion\nclass PyMAF(nn.Module):\n \"\"\" PyMAF based Regression Network for Human Mesh Recovery / Full-body Mesh Recovery\n PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop, in ICCV, 2021",
+ "detail": "modules.PyMAF.models.pymaf_net",
+ "documentation": {}
+ },
+ {
+ "label": "pymaf_net",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.pymaf_net",
+ "description": "modules.PyMAF.models.pymaf_net",
+ "peekOfCode": "def pymaf_net(smpl_mean_params, pretrained=True, device=torch.device('cuda')):\n \"\"\" Constructs an PyMAF model with ResNet50 backbone.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n model = PyMAF(smpl_mean_params, pretrained, device)\n return model",
+ "detail": "modules.PyMAF.models.pymaf_net",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.pymaf_net",
+ "description": "modules.PyMAF.models.pymaf_net",
+ "peekOfCode": "logger = logging.getLogger(__name__)\nBN_MOMENTUM = 0.1\nclass Regressor(nn.Module):\n def __init__(self, feat_dim, smpl_mean_params, use_cam_feats=False, feat_dim_hand=0, feat_dim_face=0, bhf_names=['body'], smpl_models={}):\n super().__init__()\n npose = 24 * 6\n shape_dim = 10\n cam_dim = 3\n hand_dim = 15 * 6\n face_dim = 3 * 6 + 10",
+ "detail": "modules.PyMAF.models.pymaf_net",
+ "documentation": {}
+ },
+ {
+ "label": "BN_MOMENTUM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.pymaf_net",
+ "description": "modules.PyMAF.models.pymaf_net",
+ "peekOfCode": "BN_MOMENTUM = 0.1\nclass Regressor(nn.Module):\n def __init__(self, feat_dim, smpl_mean_params, use_cam_feats=False, feat_dim_hand=0, feat_dim_face=0, bhf_names=['body'], smpl_models={}):\n super().__init__()\n npose = 24 * 6\n shape_dim = 10\n cam_dim = 3\n hand_dim = 15 * 6\n face_dim = 3 * 6 + 10\n self.body_feat_dim = feat_dim",
+ "detail": "modules.PyMAF.models.pymaf_net",
+ "documentation": {}
+ },
+ {
+ "label": "BasicBlock",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.res_module",
+ "description": "modules.PyMAF.models.res_module",
+ "peekOfCode": "class BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1):\n super().__init__()\n self.conv1 = conv3x3(inplanes, planes, stride, groups=groups)\n self.bn1 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)\n self.relu = nn.ReLU(inplace=True)\n self.conv2 = conv3x3(planes, planes, groups=groups)\n self.bn2 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)\n self.downsample = downsample",
+ "detail": "modules.PyMAF.models.res_module",
+ "documentation": {}
+ },
+ {
+ "label": "Bottleneck",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.res_module",
+ "description": "modules.PyMAF.models.res_module",
+ "peekOfCode": "class Bottleneck(nn.Module):\n expansion = 4\n def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1):\n super().__init__()\n self.conv1 = nn.Conv2d(inplanes * groups, planes * groups, kernel_size=1, bias=False, groups=groups)\n self.bn1 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)\n self.conv2 = nn.Conv2d(planes * groups, planes * groups, kernel_size=3, stride=stride,\n padding=1, bias=False, groups=groups)\n self.bn2 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)\n self.conv3 = nn.Conv2d(planes * groups, planes * self.expansion * groups, kernel_size=1,",
+ "detail": "modules.PyMAF.models.res_module",
+ "documentation": {}
+ },
+ {
+ "label": "IUV_predict_layer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.res_module",
+ "description": "modules.PyMAF.models.res_module",
+ "peekOfCode": "class IUV_predict_layer(nn.Module):\n def __init__(self, feat_dim=256, final_cov_k=3, out_channels=25, with_uv=True, mode='iuv'):\n super().__init__()\n assert mode in ['iuv', 'seg', 'pncc']\n self.mode = mode\n if mode == 'seg':\n self.predict_ann_index = nn.Conv2d(\n in_channels=feat_dim,\n out_channels=15,\n kernel_size=final_cov_k,",
+ "detail": "modules.PyMAF.models.res_module",
+ "documentation": {}
+ },
+ {
+ "label": "Seg_predict_layer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.res_module",
+ "description": "modules.PyMAF.models.res_module",
+ "peekOfCode": "class Seg_predict_layer(nn.Module):\n def __init__(self, feat_dim=256, final_cov_k=3, out_channels=25):\n super().__init__()\n self.predict_seg_index = nn.Conv2d(\n in_channels=feat_dim,\n out_channels=out_channels,\n kernel_size=final_cov_k,\n stride=1,\n padding=1 if final_cov_k == 3 else 0\n )",
+ "detail": "modules.PyMAF.models.res_module",
+ "documentation": {}
+ },
+ {
+ "label": "Kps_predict_layer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.res_module",
+ "description": "modules.PyMAF.models.res_module",
+ "peekOfCode": "class Kps_predict_layer(nn.Module):\n def __init__(self, feat_dim=256, final_cov_k=3, out_channels=3, add_module=None):\n super().__init__()\n if add_module is not None:\n conv = nn.Conv2d(\n in_channels=feat_dim,\n out_channels=out_channels,\n kernel_size=final_cov_k,\n stride=1,\n padding=1 if final_cov_k == 3 else 0",
+ "detail": "modules.PyMAF.models.res_module",
+ "documentation": {}
+ },
+ {
+ "label": "SmplResNet",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.res_module",
+ "description": "modules.PyMAF.models.res_module",
+ "peekOfCode": "class SmplResNet(nn.Module):\n def __init__(self, resnet_nums, in_channels=3, num_classes=229, last_stride=2, n_extra_feat=0, truncate=0, **kwargs):\n super().__init__()\n self.inplanes = 64\n self.truncate = truncate\n # extra = cfg.MODEL.EXTRA\n # self.deconv_with_bias = extra.DECONV_WITH_BIAS\n block, layers = resnet_spec[resnet_nums]\n self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3,\n bias=False)",
+ "detail": "modules.PyMAF.models.res_module",
+ "documentation": {}
+ },
+ {
+ "label": "LimbResLayers",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.res_module",
+ "description": "modules.PyMAF.models.res_module",
+ "peekOfCode": "class LimbResLayers(nn.Module):\n def __init__(self, resnet_nums, inplanes, outplanes=None, groups=1, **kwargs):\n super().__init__()\n self.inplanes = inplanes\n block, layers = resnet_spec[resnet_nums]\n self.outplanes = 256 if outplanes == None else outplanes\n self.layer3 = self._make_layer(block, self.outplanes, layers[2], stride=2, groups=groups)\n # self.outplanes = 512 if outplanes == None else outplanes\n # self.layer4 = self._make_layer(block, self.outplanes, layers[3], stride=2, groups=groups)\n self.avg_pooling = nn.AdaptiveAvgPool2d(1)",
+ "detail": "modules.PyMAF.models.res_module",
+ "documentation": {}
+ },
+ {
+ "label": "conv3x3",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.res_module",
+ "description": "modules.PyMAF.models.res_module",
+ "peekOfCode": "def conv3x3(in_planes, out_planes, stride=1, bias=False, groups=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes * groups, out_planes * groups, kernel_size=3, stride=stride,\n padding=1, bias=bias, groups=groups)\nclass BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1):\n super().__init__()\n self.conv1 = conv3x3(inplanes, planes, stride, groups=groups)\n self.bn1 = nn.BatchNorm2d(planes * groups, momentum=BN_MOMENTUM)",
+ "detail": "modules.PyMAF.models.res_module",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.res_module",
+ "description": "modules.PyMAF.models.res_module",
+ "peekOfCode": "logger = logging.getLogger(__name__)\nBN_MOMENTUM = 0.1\ndef conv3x3(in_planes, out_planes, stride=1, bias=False, groups=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes * groups, out_planes * groups, kernel_size=3, stride=stride,\n padding=1, bias=bias, groups=groups)\nclass BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1):\n super().__init__()",
+ "detail": "modules.PyMAF.models.res_module",
+ "documentation": {}
+ },
+ {
+ "label": "BN_MOMENTUM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.res_module",
+ "description": "modules.PyMAF.models.res_module",
+ "peekOfCode": "BN_MOMENTUM = 0.1\ndef conv3x3(in_planes, out_planes, stride=1, bias=False, groups=1):\n \"\"\"3x3 convolution with padding\"\"\"\n return nn.Conv2d(in_planes * groups, out_planes * groups, kernel_size=3, stride=stride,\n padding=1, bias=bias, groups=groups)\nclass BasicBlock(nn.Module):\n expansion = 1\n def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1):\n super().__init__()\n self.conv1 = conv3x3(inplanes, planes, stride, groups=groups)",
+ "detail": "modules.PyMAF.models.res_module",
+ "documentation": {}
+ },
+ {
+ "label": "resnet_spec",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.res_module",
+ "description": "modules.PyMAF.models.res_module",
+ "peekOfCode": "resnet_spec = {18: (BasicBlock, [2, 2, 2, 2]),\n 34: (BasicBlock, [3, 4, 6, 3]),\n 50: (Bottleneck, [3, 4, 6, 3]),\n 101: (Bottleneck, [3, 4, 23, 3]),\n 152: (Bottleneck, [3, 8, 36, 3])}\nclass IUV_predict_layer(nn.Module):\n def __init__(self, feat_dim=256, final_cov_k=3, out_channels=25, with_uv=True, mode='iuv'):\n super().__init__()\n assert mode in ['iuv', 'seg', 'pncc']\n self.mode = mode",
+ "detail": "modules.PyMAF.models.res_module",
+ "documentation": {}
+ },
+ {
+ "label": "ModelOutput",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "class ModelOutput(SMPLXOutput):\n smpl_joints: Optional[torch.Tensor] = None\n joints_J19: Optional[torch.Tensor] = None\n smplx_vertices: Optional[torch.Tensor] = None\n flame_vertices: Optional[torch.Tensor] = None\n lhand_vertices: Optional[torch.Tensor] = None\n rhand_vertices: Optional[torch.Tensor] = None\n lhand_joints: Optional[torch.Tensor] = None\n rhand_joints: Optional[torch.Tensor] = None\n face_joints: Optional[torch.Tensor] = None",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "SMPL",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "class SMPL(_SMPL):\n \"\"\" Extension of the official SMPL implementation to support more joints \"\"\"\n def __init__(self, create_betas=False, create_global_orient=False, create_body_pose=False, create_transl=False, *args, **kwargs):\n super().__init__(create_betas=create_betas, \n create_global_orient=create_global_orient, \n create_body_pose=create_body_pose, \n create_transl=create_transl, *args, **kwargs)\n joints = [constants.JOINT_MAP[i] for i in constants.JOINT_NAMES]\n J_regressor_extra = np.load(path_config.JOINT_REGRESSOR_TRAIN_EXTRA)\n self.register_buffer('J_regressor_extra', torch.tensor(J_regressor_extra, dtype=torch.float32))",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "SMPLX",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "class SMPLX(SMPLXLayer):\n \"\"\" Extension of the official SMPLX implementation to support more functions \"\"\"\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n def get_global_rotation(\n self,\n global_orient: Optional[torch.Tensor] = None,\n body_pose: Optional[torch.Tensor] = None,\n left_hand_pose: Optional[torch.Tensor] = None,\n right_hand_pose: Optional[torch.Tensor] = None,",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "SMPLX_ALL",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "class SMPLX_ALL(nn.Module):\n \"\"\" Extension of the official SMPLX implementation to support more joints \"\"\"\n def __init__(self, batch_size=1, use_face_contour=True, all_gender=False, **kwargs):\n super().__init__()\n numBetas = 10\n self.use_face_contour = use_face_contour\n if all_gender:\n self.genders = ['male', 'female', 'neutral']\n else:\n self.genders = ['neutral']",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "MANO",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "class MANO(MANOLayer):\n \"\"\" Extension of the official MANO implementation to support more joints \"\"\"\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n def forward(self, *args, **kwargs):\n if 'pose2rot' not in kwargs:\n kwargs['pose2rot'] = True\n pose_keys = ['global_orient', 'right_hand_pose']\n batch_size = kwargs['global_orient'].shape[0]\n if kwargs['pose2rot']:",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "FLAME",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "class FLAME(FLAMELayer):\n \"\"\" Extension of the official FLAME implementation to support more joints \"\"\"\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n def forward(self, *args, **kwargs):\n if 'pose2rot' not in kwargs:\n kwargs['pose2rot'] = True\n pose_keys = ['global_orient', 'jaw_pose', 'leye_pose', 'reye_pose']\n batch_size = kwargs['global_orient'].shape[0]\n if kwargs['pose2rot']:",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "SMPL_Family",
+ "kind": 6,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "class SMPL_Family():\n def __init__(self, model_type='smpl', *args, **kwargs):\n if model_type == 'smpl':\n self.model = SMPL(\n model_path=SMPL_MODEL_DIR,\n *args, **kwargs\n )\n elif model_type == 'smplx':\n self.model = SMPLX_ALL(*args, **kwargs)\n elif model_type == 'mano':",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_smpl_faces",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_smpl_faces():\n smpl = SMPL(model_path=SMPL_MODEL_DIR, batch_size=1)\n return smpl.faces\ndef get_smplx_faces():\n smplx = SMPLX(SMPL_MODEL_DIR, batch_size=1)\n return smplx.faces\ndef get_mano_faces(hand_type='right'):\n assert hand_type in ['right', 'left']\n is_rhand = True if hand_type == 'right' else False\n mano = MANO(SMPL_MODEL_DIR, batch_size=1, is_rhand=is_rhand)",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_smplx_faces",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_smplx_faces():\n smplx = SMPLX(SMPL_MODEL_DIR, batch_size=1)\n return smplx.faces\ndef get_mano_faces(hand_type='right'):\n assert hand_type in ['right', 'left']\n is_rhand = True if hand_type == 'right' else False\n mano = MANO(SMPL_MODEL_DIR, batch_size=1, is_rhand=is_rhand)\n return mano.faces\ndef get_flame_faces():\n flame = FLAME(SMPL_MODEL_DIR, batch_size=1)",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_mano_faces",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_mano_faces(hand_type='right'):\n assert hand_type in ['right', 'left']\n is_rhand = True if hand_type == 'right' else False\n mano = MANO(SMPL_MODEL_DIR, batch_size=1, is_rhand=is_rhand)\n return mano.faces\ndef get_flame_faces():\n flame = FLAME(SMPL_MODEL_DIR, batch_size=1)\n return flame.faces\ndef get_model_faces(type='smpl'):\n if type == 'smpl':",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_flame_faces",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_flame_faces():\n flame = FLAME(SMPL_MODEL_DIR, batch_size=1)\n return flame.faces\ndef get_model_faces(type='smpl'):\n if type == 'smpl':\n return get_smpl_faces()\n elif type == 'smplx':\n return get_smplx_faces()\n elif type == 'mano':\n return get_mano_faces()",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_model_faces",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_model_faces(type='smpl'):\n if type == 'smpl':\n return get_smpl_faces()\n elif type == 'smplx':\n return get_smplx_faces()\n elif type == 'mano':\n return get_mano_faces()\n elif type == 'flame':\n return get_flame_faces()\ndef get_model_tpose(type='smpl'):",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_model_tpose",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_model_tpose(type='smpl'):\n if type == 'smpl':\n return get_smpl_tpose()\n elif type == 'smplx':\n return get_smplx_tpose()\n elif type == 'mano':\n return get_mano_tpose()\n elif type == 'flame':\n return get_flame_tpose()\ndef get_smpl_tpose():",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_smpl_tpose",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_smpl_tpose():\n smpl = SMPL(create_betas=True, create_global_orient=True, create_body_pose=True, model_path=SMPL_MODEL_DIR, batch_size=1)\n vertices = smpl().vertices[0]\n return vertices.detach()\ndef get_smpl_tpose_joint():\n smpl = SMPL(create_betas=True, create_global_orient=True, create_body_pose=True, model_path=SMPL_MODEL_DIR, batch_size=1)\n tpose_joint = smpl().smpl_joints[0]\n return tpose_joint.detach()\ndef get_smplx_tpose():\n smplx = SMPLXLayer(SMPL_MODEL_DIR, batch_size=1)",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_smpl_tpose_joint",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_smpl_tpose_joint():\n smpl = SMPL(create_betas=True, create_global_orient=True, create_body_pose=True, model_path=SMPL_MODEL_DIR, batch_size=1)\n tpose_joint = smpl().smpl_joints[0]\n return tpose_joint.detach()\ndef get_smplx_tpose():\n smplx = SMPLXLayer(SMPL_MODEL_DIR, batch_size=1)\n vertices = smplx().vertices[0]\n return vertices\ndef get_smplx_tpose_joint():\n smplx = SMPLXLayer(SMPL_MODEL_DIR, batch_size=1)",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_smplx_tpose",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_smplx_tpose():\n smplx = SMPLXLayer(SMPL_MODEL_DIR, batch_size=1)\n vertices = smplx().vertices[0]\n return vertices\ndef get_smplx_tpose_joint():\n smplx = SMPLXLayer(SMPL_MODEL_DIR, batch_size=1)\n tpose_joint = smplx().joints[0]\n return tpose_joint\ndef get_mano_tpose():\n mano = MANO(SMPL_MODEL_DIR, batch_size=1, is_rhand=True)",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_smplx_tpose_joint",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_smplx_tpose_joint():\n smplx = SMPLXLayer(SMPL_MODEL_DIR, batch_size=1)\n tpose_joint = smplx().joints[0]\n return tpose_joint\ndef get_mano_tpose():\n mano = MANO(SMPL_MODEL_DIR, batch_size=1, is_rhand=True)\n vertices = mano(global_orient=torch.zeros(1, 3), \n right_hand_pose=torch.zeros(1, 15*3)).rhand_vertices[0]\n return vertices\ndef get_flame_tpose():",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_mano_tpose",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_mano_tpose():\n mano = MANO(SMPL_MODEL_DIR, batch_size=1, is_rhand=True)\n vertices = mano(global_orient=torch.zeros(1, 3), \n right_hand_pose=torch.zeros(1, 15*3)).rhand_vertices[0]\n return vertices\ndef get_flame_tpose():\n flame = FLAME(SMPL_MODEL_DIR, batch_size=1)\n vertices = flame(global_orient=torch.zeros(1, 3)).flame_vertices[0]\n return vertices\ndef get_part_joints(smpl_joints):",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_flame_tpose",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_flame_tpose():\n flame = FLAME(SMPL_MODEL_DIR, batch_size=1)\n vertices = flame(global_orient=torch.zeros(1, 3)).flame_vertices[0]\n return vertices\ndef get_part_joints(smpl_joints):\n batch_size = smpl_joints.shape[0]\n # part_joints = torch.zeros().to(smpl_joints.device)\n one_seg_pairs = [(0, 1), (0, 2), (0, 3), (3, 6), (9, 12), (9, 13), (9, 14), (12, 15), (13, 16), (14, 17)]\n two_seg_pairs = [(1, 4), (2, 5), (4, 7), (5, 8), (16, 18), (17, 19), (18, 20), (19, 21)]\n one_seg_pairs.extend(two_seg_pairs)",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_part_joints",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_part_joints(smpl_joints):\n batch_size = smpl_joints.shape[0]\n # part_joints = torch.zeros().to(smpl_joints.device)\n one_seg_pairs = [(0, 1), (0, 2), (0, 3), (3, 6), (9, 12), (9, 13), (9, 14), (12, 15), (13, 16), (14, 17)]\n two_seg_pairs = [(1, 4), (2, 5), (4, 7), (5, 8), (16, 18), (17, 19), (18, 20), (19, 21)]\n one_seg_pairs.extend(two_seg_pairs)\n single_joints = [(10), (11), (15), (22), (23)]\n part_joints = []\n for j_p in one_seg_pairs:\n new_joint = torch.mean(smpl_joints[:, j_p], dim=1, keepdim=True)",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_partial_smpl",
+ "kind": 2,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "def get_partial_smpl(body_model='smpl', device=torch.device('cuda')):\n body_model_faces = get_model_faces(body_model)\n body_model_num_verts = len(get_model_tpose(body_model))\n part_vert_faces = {}\n for part in ['lhand', 'rhand', 'face', 'arm', 'forearm', 'larm', 'rarm', 'lwrist', 'rwrist']:\n part_vid_fname = 'data/partial_mesh/{}_{}_vids.npz'.format(body_model, part)\n if os.path.exists(part_vid_fname):\n part_vids = np.load(part_vid_fname)\n part_vert_faces[part] = {'vids': part_vids['vids'], 'faces': part_vids['faces']}\n else:",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "SMPL_MEAN_PARAMS",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "SMPL_MEAN_PARAMS = path_config.SMPL_MEAN_PARAMS\nSMPL_MODEL_DIR = path_config.SMPL_MODEL_DIR\n@dataclass\nclass ModelOutput(SMPLXOutput):\n smpl_joints: Optional[torch.Tensor] = None\n joints_J19: Optional[torch.Tensor] = None\n smplx_vertices: Optional[torch.Tensor] = None\n flame_vertices: Optional[torch.Tensor] = None\n lhand_vertices: Optional[torch.Tensor] = None\n rhand_vertices: Optional[torch.Tensor] = None",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "SMPL_MODEL_DIR",
+ "kind": 5,
+ "importPath": "modules.PyMAF.models.smpl",
+ "description": "modules.PyMAF.models.smpl",
+ "peekOfCode": "SMPL_MODEL_DIR = path_config.SMPL_MODEL_DIR\n@dataclass\nclass ModelOutput(SMPLXOutput):\n smpl_joints: Optional[torch.Tensor] = None\n joints_J19: Optional[torch.Tensor] = None\n smplx_vertices: Optional[torch.Tensor] = None\n flame_vertices: Optional[torch.Tensor] = None\n lhand_vertices: Optional[torch.Tensor] = None\n rhand_vertices: Optional[torch.Tensor] = None\n lhand_joints: Optional[torch.Tensor] = None",
+ "detail": "modules.PyMAF.models.smpl",
+ "documentation": {}
+ },
+ {
+ "label": "get_cfg_defaults",
+ "kind": 2,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "def get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n # return cfg.clone()\n return cfg\ndef update_cfg(cfg_file):\n # cfg = get_cfg_defaults()\n cfg.merge_from_file(cfg_file)\n # return cfg.clone()",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "update_cfg",
+ "kind": 2,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "def update_cfg(cfg_file):\n # cfg = get_cfg_defaults()\n cfg.merge_from_file(cfg_file)\n # return cfg.clone()\n return cfg\ndef parse_args(args):\n cfg_file = args.cfg_file\n if args.cfg_file is not None:\n cfg = update_cfg(args.cfg_file)\n else:",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "parse_args",
+ "kind": 2,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "def parse_args(args):\n cfg_file = args.cfg_file\n if args.cfg_file is not None:\n cfg = update_cfg(args.cfg_file)\n else:\n cfg = get_cfg_defaults()\n if args.misc is not None:\n cfg.merge_from_list(args.misc)\n # a=['TRAIN.BHF_MODE', 'full_body', 'MODEL.EVAL_MODE', 'True', 'MODEL.PyMAF.HAND_VIS_TH', '0.1']\n # cfg.merge_from_list(a)",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "parse_args_extend",
+ "kind": 2,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "def parse_args_extend(args):\n if args.resume:\n if not os.path.exists(args.log_dir):\n raise ValueError('Experiment are set to resume mode, but log directory does not exist.')\n if args.cfg_file is not None:\n cfg = update_cfg(args.cfg_file)\n else:\n cfg = get_cfg_defaults()\n # load log's cfg\n cfg_file = os.path.join(args.log_dir, 'cfg.yaml')",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg = CN(new_allowed=True)\ncfg.OUTPUT_DIR = 'results'\ncfg.DEVICE = 'cuda'\ncfg.DEBUG = False\ncfg.LOGDIR = ''\ncfg.VAL_VIS_BATCH_FREQ = 200\ncfg.TRAIN_VIS_ITER_FERQ = 1000\ncfg.SEED_VALUE = -1\ncfg.TRAIN = CN(new_allowed=True)\ncfg.LOSS = CN(new_allowed=True)",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.OUTPUT_DIR",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.OUTPUT_DIR = 'results'\ncfg.DEVICE = 'cuda'\ncfg.DEBUG = False\ncfg.LOGDIR = ''\ncfg.VAL_VIS_BATCH_FREQ = 200\ncfg.TRAIN_VIS_ITER_FERQ = 1000\ncfg.SEED_VALUE = -1\ncfg.TRAIN = CN(new_allowed=True)\ncfg.LOSS = CN(new_allowed=True)\ncfg.LOSS.KP_2D_W = 300.0",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.DEVICE",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.DEVICE = 'cuda'\ncfg.DEBUG = False\ncfg.LOGDIR = ''\ncfg.VAL_VIS_BATCH_FREQ = 200\ncfg.TRAIN_VIS_ITER_FERQ = 1000\ncfg.SEED_VALUE = -1\ncfg.TRAIN = CN(new_allowed=True)\ncfg.LOSS = CN(new_allowed=True)\ncfg.LOSS.KP_2D_W = 300.0\ncfg.LOSS.KP_3D_W = 300.0",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.DEBUG",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.DEBUG = False\ncfg.LOGDIR = ''\ncfg.VAL_VIS_BATCH_FREQ = 200\ncfg.TRAIN_VIS_ITER_FERQ = 1000\ncfg.SEED_VALUE = -1\ncfg.TRAIN = CN(new_allowed=True)\ncfg.LOSS = CN(new_allowed=True)\ncfg.LOSS.KP_2D_W = 300.0\ncfg.LOSS.KP_3D_W = 300.0\ncfg.LOSS.SHAPE_W = 0.06",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.LOGDIR",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.LOGDIR = ''\ncfg.VAL_VIS_BATCH_FREQ = 200\ncfg.TRAIN_VIS_ITER_FERQ = 1000\ncfg.SEED_VALUE = -1\ncfg.TRAIN = CN(new_allowed=True)\ncfg.LOSS = CN(new_allowed=True)\ncfg.LOSS.KP_2D_W = 300.0\ncfg.LOSS.KP_3D_W = 300.0\ncfg.LOSS.SHAPE_W = 0.06\ncfg.LOSS.POSE_W = 60.0",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.VAL_VIS_BATCH_FREQ",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.VAL_VIS_BATCH_FREQ = 200\ncfg.TRAIN_VIS_ITER_FERQ = 1000\ncfg.SEED_VALUE = -1\ncfg.TRAIN = CN(new_allowed=True)\ncfg.LOSS = CN(new_allowed=True)\ncfg.LOSS.KP_2D_W = 300.0\ncfg.LOSS.KP_3D_W = 300.0\ncfg.LOSS.SHAPE_W = 0.06\ncfg.LOSS.POSE_W = 60.0\ncfg.LOSS.VERT_W = 0.0",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.TRAIN_VIS_ITER_FERQ",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.TRAIN_VIS_ITER_FERQ = 1000\ncfg.SEED_VALUE = -1\ncfg.TRAIN = CN(new_allowed=True)\ncfg.LOSS = CN(new_allowed=True)\ncfg.LOSS.KP_2D_W = 300.0\ncfg.LOSS.KP_3D_W = 300.0\ncfg.LOSS.SHAPE_W = 0.06\ncfg.LOSS.POSE_W = 60.0\ncfg.LOSS.VERT_W = 0.0\n# Loss weights for dense correspondences",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.SEED_VALUE",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.SEED_VALUE = -1\ncfg.TRAIN = CN(new_allowed=True)\ncfg.LOSS = CN(new_allowed=True)\ncfg.LOSS.KP_2D_W = 300.0\ncfg.LOSS.KP_3D_W = 300.0\ncfg.LOSS.SHAPE_W = 0.06\ncfg.LOSS.POSE_W = 60.0\ncfg.LOSS.VERT_W = 0.0\n# Loss weights for dense correspondences\ncfg.LOSS.INDEX_WEIGHTS = 2.0",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.TRAIN",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.TRAIN = CN(new_allowed=True)\ncfg.LOSS = CN(new_allowed=True)\ncfg.LOSS.KP_2D_W = 300.0\ncfg.LOSS.KP_3D_W = 300.0\ncfg.LOSS.SHAPE_W = 0.06\ncfg.LOSS.POSE_W = 60.0\ncfg.LOSS.VERT_W = 0.0\n# Loss weights for dense correspondences\ncfg.LOSS.INDEX_WEIGHTS = 2.0\n# Loss weights for surface parts. (24 Parts)",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.LOSS",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.LOSS = CN(new_allowed=True)\ncfg.LOSS.KP_2D_W = 300.0\ncfg.LOSS.KP_3D_W = 300.0\ncfg.LOSS.SHAPE_W = 0.06\ncfg.LOSS.POSE_W = 60.0\ncfg.LOSS.VERT_W = 0.0\n# Loss weights for dense correspondences\ncfg.LOSS.INDEX_WEIGHTS = 2.0\n# Loss weights for surface parts. (24 Parts)\ncfg.LOSS.PART_WEIGHTS = 0.3",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.LOSS.KP_2D_W",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.LOSS.KP_2D_W = 300.0\ncfg.LOSS.KP_3D_W = 300.0\ncfg.LOSS.SHAPE_W = 0.06\ncfg.LOSS.POSE_W = 60.0\ncfg.LOSS.VERT_W = 0.0\n# Loss weights for dense correspondences\ncfg.LOSS.INDEX_WEIGHTS = 2.0\n# Loss weights for surface parts. (24 Parts)\ncfg.LOSS.PART_WEIGHTS = 0.3\n# Loss weights for UV regression.",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.LOSS.KP_3D_W",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.LOSS.KP_3D_W = 300.0\ncfg.LOSS.SHAPE_W = 0.06\ncfg.LOSS.POSE_W = 60.0\ncfg.LOSS.VERT_W = 0.0\n# Loss weights for dense correspondences\ncfg.LOSS.INDEX_WEIGHTS = 2.0\n# Loss weights for surface parts. (24 Parts)\ncfg.LOSS.PART_WEIGHTS = 0.3\n# Loss weights for UV regression.\ncfg.LOSS.POINT_REGRESSION_WEIGHTS = 0.5",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.LOSS.SHAPE_W",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.LOSS.SHAPE_W = 0.06\ncfg.LOSS.POSE_W = 60.0\ncfg.LOSS.VERT_W = 0.0\n# Loss weights for dense correspondences\ncfg.LOSS.INDEX_WEIGHTS = 2.0\n# Loss weights for surface parts. (24 Parts)\ncfg.LOSS.PART_WEIGHTS = 0.3\n# Loss weights for UV regression.\ncfg.LOSS.POINT_REGRESSION_WEIGHTS = 0.5\ncfg.MODEL = CN(new_allowed=True)",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.LOSS.POSE_W",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.LOSS.POSE_W = 60.0\ncfg.LOSS.VERT_W = 0.0\n# Loss weights for dense correspondences\ncfg.LOSS.INDEX_WEIGHTS = 2.0\n# Loss weights for surface parts. (24 Parts)\ncfg.LOSS.PART_WEIGHTS = 0.3\n# Loss weights for UV regression.\ncfg.LOSS.POINT_REGRESSION_WEIGHTS = 0.5\ncfg.MODEL = CN(new_allowed=True)\ncfg.MODEL.PyMAF = CN(new_allowed=True)",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.LOSS.VERT_W",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.LOSS.VERT_W = 0.0\n# Loss weights for dense correspondences\ncfg.LOSS.INDEX_WEIGHTS = 2.0\n# Loss weights for surface parts. (24 Parts)\ncfg.LOSS.PART_WEIGHTS = 0.3\n# Loss weights for UV regression.\ncfg.LOSS.POINT_REGRESSION_WEIGHTS = 0.5\ncfg.MODEL = CN(new_allowed=True)\ncfg.MODEL.PyMAF = CN(new_allowed=True)\n## switch",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.LOSS.INDEX_WEIGHTS",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.LOSS.INDEX_WEIGHTS = 2.0\n# Loss weights for surface parts. (24 Parts)\ncfg.LOSS.PART_WEIGHTS = 0.3\n# Loss weights for UV regression.\ncfg.LOSS.POINT_REGRESSION_WEIGHTS = 0.5\ncfg.MODEL = CN(new_allowed=True)\ncfg.MODEL.PyMAF = CN(new_allowed=True)\n## switch\ncfg.TRAIN.BATCH_SIZE = 64\ncfg.TRAIN.VAL_LOOP = True",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.LOSS.PART_WEIGHTS",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.LOSS.PART_WEIGHTS = 0.3\n# Loss weights for UV regression.\ncfg.LOSS.POINT_REGRESSION_WEIGHTS = 0.5\ncfg.MODEL = CN(new_allowed=True)\ncfg.MODEL.PyMAF = CN(new_allowed=True)\n## switch\ncfg.TRAIN.BATCH_SIZE = 64\ncfg.TRAIN.VAL_LOOP = True\ncfg.TEST = CN(new_allowed=True)\ndef get_cfg_defaults():",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.LOSS.POINT_REGRESSION_WEIGHTS",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.LOSS.POINT_REGRESSION_WEIGHTS = 0.5\ncfg.MODEL = CN(new_allowed=True)\ncfg.MODEL.PyMAF = CN(new_allowed=True)\n## switch\ncfg.TRAIN.BATCH_SIZE = 64\ncfg.TRAIN.VAL_LOOP = True\ncfg.TEST = CN(new_allowed=True)\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.MODEL",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.MODEL = CN(new_allowed=True)\ncfg.MODEL.PyMAF = CN(new_allowed=True)\n## switch\ncfg.TRAIN.BATCH_SIZE = 64\ncfg.TRAIN.VAL_LOOP = True\ncfg.TEST = CN(new_allowed=True)\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.MODEL.PyMAF",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.MODEL.PyMAF = CN(new_allowed=True)\n## switch\ncfg.TRAIN.BATCH_SIZE = 64\ncfg.TRAIN.VAL_LOOP = True\ncfg.TEST = CN(new_allowed=True)\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n # return cfg.clone()",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.TRAIN.BATCH_SIZE",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.TRAIN.BATCH_SIZE = 64\ncfg.TRAIN.VAL_LOOP = True\ncfg.TEST = CN(new_allowed=True)\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n # return cfg.clone()\n return cfg\ndef update_cfg(cfg_file):",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.TRAIN.VAL_LOOP",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.TRAIN.VAL_LOOP = True\ncfg.TEST = CN(new_allowed=True)\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n # return cfg.clone()\n return cfg\ndef update_cfg(cfg_file):\n # cfg = get_cfg_defaults()",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "cfg.TEST",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.cfgs",
+ "description": "modules.PyMAF.pymaf_core.cfgs",
+ "peekOfCode": "cfg.TEST = CN(new_allowed=True)\ndef get_cfg_defaults():\n \"\"\"Get a yacs CfgNode object with default values for my_project.\"\"\"\n # Return a clone so that the defaults will not be altered\n # This is for the \"local variable\" use pattern\n # return cfg.clone()\n return cfg\ndef update_cfg(cfg_file):\n # cfg = get_cfg_defaults()\n cfg.merge_from_file(cfg_file)",
+ "detail": "modules.PyMAF.pymaf_core.cfgs",
+ "documentation": {}
+ },
+ {
+ "label": "FOCAL_LENGTH",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "FOCAL_LENGTH = 5000.\nIMG_RES = 224\n# IMG_RES = 256\n# Mean and standard deviation for normalizing input image\nIMG_NORM_MEAN = [0.485, 0.456, 0.406]\nIMG_NORM_STD = [0.229, 0.224, 0.225]\n\"\"\"\nWe create a superset of joints containing the OpenPose joints together with the ones that each dataset provides.\nWe keep a superset of 24 joints such that we include all joints from every dataset.\nIf a dataset doesn't provide annotations for a specific joint, we simply ignore it.",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "IMG_RES",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "IMG_RES = 224\n# IMG_RES = 256\n# Mean and standard deviation for normalizing input image\nIMG_NORM_MEAN = [0.485, 0.456, 0.406]\nIMG_NORM_STD = [0.229, 0.224, 0.225]\n\"\"\"\nWe create a superset of joints containing the OpenPose joints together with the ones that each dataset provides.\nWe keep a superset of 24 joints such that we include all joints from every dataset.\nIf a dataset doesn't provide annotations for a specific joint, we simply ignore it.\nThe joints used here are the following:",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "IMG_NORM_MEAN",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "IMG_NORM_MEAN = [0.485, 0.456, 0.406]\nIMG_NORM_STD = [0.229, 0.224, 0.225]\n\"\"\"\nWe create a superset of joints containing the OpenPose joints together with the ones that each dataset provides.\nWe keep a superset of 24 joints such that we include all joints from every dataset.\nIf a dataset doesn't provide annotations for a specific joint, we simply ignore it.\nThe joints used here are the following:\n\"\"\"\nOP_JOINT_NAMES = [\n# 25 OpenPose joints (in the order provided by OpenPose)",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "IMG_NORM_STD",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "IMG_NORM_STD = [0.229, 0.224, 0.225]\n\"\"\"\nWe create a superset of joints containing the OpenPose joints together with the ones that each dataset provides.\nWe keep a superset of 24 joints such that we include all joints from every dataset.\nIf a dataset doesn't provide annotations for a specific joint, we simply ignore it.\nThe joints used here are the following:\n\"\"\"\nOP_JOINT_NAMES = [\n# 25 OpenPose joints (in the order provided by OpenPose)\n'OP Nose',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "OP_JOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "OP_JOINT_NAMES = [\n# 25 OpenPose joints (in the order provided by OpenPose)\n'OP Nose',\n'OP Neck',\n'OP RShoulder',\n'OP RElbow',\n'OP RWrist',\n'OP LShoulder',\n'OP LElbow',\n'OP LWrist',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "SPIN_JOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "SPIN_JOINT_NAMES = [\n# 24 Ground Truth joints (superset of joints from different datasets)\n'Right Ankle',\n'Right Knee',\n'Right Hip', # 2\n'Left Hip',\n'Left Knee', # 4\n'Left Ankle',\n'Right Wrist', # 6\n'Right Elbow',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "JOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "JOINT_NAMES = OP_JOINT_NAMES + SPIN_JOINT_NAMES\nCOCO_KEYPOINTS = ['nose',\n 'left_eye',\n 'right_eye',\n 'left_ear',\n 'right_ear',\n 'left_shoulder',\n 'right_shoulder',\n 'left_elbow',\n 'right_elbow',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "COCO_KEYPOINTS",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "COCO_KEYPOINTS = ['nose',\n 'left_eye',\n 'right_eye',\n 'left_ear',\n 'right_ear',\n 'left_shoulder',\n 'right_shoulder',\n 'left_elbow',\n 'right_elbow',\n 'left_wrist',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "JOINT_IDS",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "JOINT_IDS = {JOINT_NAMES[i]: i for i in range(len(JOINT_NAMES))}\n# Map joints to SMPL joints\nJOINT_MAP = {\n'OP Nose': 24, 'OP Neck': 12, 'OP RShoulder': 17,\n'OP RElbow': 19, 'OP RWrist': 21, 'OP LShoulder': 16,\n'OP LElbow': 18, 'OP LWrist': 20, 'OP MidHip': 0,\n'OP RHip': 2, 'OP RKnee': 5, 'OP RAnkle': 8,\n'OP LHip': 1, 'OP LKnee': 4, 'OP LAnkle': 7,\n'OP REye': 25, 'OP LEye': 26, 'OP REar': 27,\n'OP LEar': 28, 'OP LBigToe': 29, 'OP LSmallToe': 30,",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "JOINT_MAP",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "JOINT_MAP = {\n'OP Nose': 24, 'OP Neck': 12, 'OP RShoulder': 17,\n'OP RElbow': 19, 'OP RWrist': 21, 'OP LShoulder': 16,\n'OP LElbow': 18, 'OP LWrist': 20, 'OP MidHip': 0,\n'OP RHip': 2, 'OP RKnee': 5, 'OP RAnkle': 8,\n'OP LHip': 1, 'OP LKnee': 4, 'OP LAnkle': 7,\n'OP REye': 25, 'OP LEye': 26, 'OP REar': 27,\n'OP LEar': 28, 'OP LBigToe': 29, 'OP LSmallToe': 30,\n'OP LHeel': 31, 'OP RBigToe': 32, 'OP RSmallToe': 33, 'OP RHeel': 34,\n'Right Ankle': 8, 'Right Knee': 5, 'Right Hip': 45,",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "H36M_TO_J17",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "H36M_TO_J17 = [6, 5, 4, 1, 2, 3, 16, 15, 14, 11, 12, 13, 8, 10, 0, 7, 9]\nH36M_TO_J14 = H36M_TO_J17[:14]\n# Indices to get the 14 LSP joints from the ground truth joints\nJ24_TO_J17 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 18, 14, 16, 17]\nJ24_TO_J14 = J24_TO_J17[:14]\nJ24_TO_J19 = J24_TO_J17[:14] + [19, 20, 21, 22, 23]\n# COCO with also 17 joints\nJ24_TO_JCOCO = [19, 20, 21, 22, 23, 9, 8, 10, 7, 11, 6, 3, 2, 4, 1, 5, 0]\n# Permutation of SMPL pose parameters when flipping the shape\nSMPL_JOINTS_FLIP_PERM = [0, 2, 1, 3, 5, 4, 6, 8, 7, 9, 11, 10, 12, 14, 13, 15, 17, 16, 19, 18, 21, 20, 23, 22]",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "H36M_TO_J14",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "H36M_TO_J14 = H36M_TO_J17[:14]\n# Indices to get the 14 LSP joints from the ground truth joints\nJ24_TO_J17 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 18, 14, 16, 17]\nJ24_TO_J14 = J24_TO_J17[:14]\nJ24_TO_J19 = J24_TO_J17[:14] + [19, 20, 21, 22, 23]\n# COCO with also 17 joints\nJ24_TO_JCOCO = [19, 20, 21, 22, 23, 9, 8, 10, 7, 11, 6, 3, 2, 4, 1, 5, 0]\n# Permutation of SMPL pose parameters when flipping the shape\nSMPL_JOINTS_FLIP_PERM = [0, 2, 1, 3, 5, 4, 6, 8, 7, 9, 11, 10, 12, 14, 13, 15, 17, 16, 19, 18, 21, 20, 23, 22]\nSMPL_POSE_FLIP_PERM = []",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "J24_TO_J17",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "J24_TO_J17 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 18, 14, 16, 17]\nJ24_TO_J14 = J24_TO_J17[:14]\nJ24_TO_J19 = J24_TO_J17[:14] + [19, 20, 21, 22, 23]\n# COCO with also 17 joints\nJ24_TO_JCOCO = [19, 20, 21, 22, 23, 9, 8, 10, 7, 11, 6, 3, 2, 4, 1, 5, 0]\n# Permutation of SMPL pose parameters when flipping the shape\nSMPL_JOINTS_FLIP_PERM = [0, 2, 1, 3, 5, 4, 6, 8, 7, 9, 11, 10, 12, 14, 13, 15, 17, 16, 19, 18, 21, 20, 23, 22]\nSMPL_POSE_FLIP_PERM = []\nfor i in SMPL_JOINTS_FLIP_PERM:\n SMPL_POSE_FLIP_PERM.append(3*i)",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "J24_TO_J14",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "J24_TO_J14 = J24_TO_J17[:14]\nJ24_TO_J19 = J24_TO_J17[:14] + [19, 20, 21, 22, 23]\n# COCO with also 17 joints\nJ24_TO_JCOCO = [19, 20, 21, 22, 23, 9, 8, 10, 7, 11, 6, 3, 2, 4, 1, 5, 0]\n# Permutation of SMPL pose parameters when flipping the shape\nSMPL_JOINTS_FLIP_PERM = [0, 2, 1, 3, 5, 4, 6, 8, 7, 9, 11, 10, 12, 14, 13, 15, 17, 16, 19, 18, 21, 20, 23, 22]\nSMPL_POSE_FLIP_PERM = []\nfor i in SMPL_JOINTS_FLIP_PERM:\n SMPL_POSE_FLIP_PERM.append(3*i)\n SMPL_POSE_FLIP_PERM.append(3*i+1)",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "J24_TO_J19",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "J24_TO_J19 = J24_TO_J17[:14] + [19, 20, 21, 22, 23]\n# COCO with also 17 joints\nJ24_TO_JCOCO = [19, 20, 21, 22, 23, 9, 8, 10, 7, 11, 6, 3, 2, 4, 1, 5, 0]\n# Permutation of SMPL pose parameters when flipping the shape\nSMPL_JOINTS_FLIP_PERM = [0, 2, 1, 3, 5, 4, 6, 8, 7, 9, 11, 10, 12, 14, 13, 15, 17, 16, 19, 18, 21, 20, 23, 22]\nSMPL_POSE_FLIP_PERM = []\nfor i in SMPL_JOINTS_FLIP_PERM:\n SMPL_POSE_FLIP_PERM.append(3*i)\n SMPL_POSE_FLIP_PERM.append(3*i+1)\n SMPL_POSE_FLIP_PERM.append(3*i+2)",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "J24_TO_JCOCO",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "J24_TO_JCOCO = [19, 20, 21, 22, 23, 9, 8, 10, 7, 11, 6, 3, 2, 4, 1, 5, 0]\n# Permutation of SMPL pose parameters when flipping the shape\nSMPL_JOINTS_FLIP_PERM = [0, 2, 1, 3, 5, 4, 6, 8, 7, 9, 11, 10, 12, 14, 13, 15, 17, 16, 19, 18, 21, 20, 23, 22]\nSMPL_POSE_FLIP_PERM = []\nfor i in SMPL_JOINTS_FLIP_PERM:\n SMPL_POSE_FLIP_PERM.append(3*i)\n SMPL_POSE_FLIP_PERM.append(3*i+1)\n SMPL_POSE_FLIP_PERM.append(3*i+2)\n# Permutation indices for the 24 ground truth joints\nJ24_FLIP_PERM = [5, 4, 3, 2, 1, 0, 11, 10, 9, 8, 7, 6, 12, 13, 14, 15, 16, 17, 18, 19, 21, 20, 23, 22]",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "SMPL_JOINTS_FLIP_PERM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "SMPL_JOINTS_FLIP_PERM = [0, 2, 1, 3, 5, 4, 6, 8, 7, 9, 11, 10, 12, 14, 13, 15, 17, 16, 19, 18, 21, 20, 23, 22]\nSMPL_POSE_FLIP_PERM = []\nfor i in SMPL_JOINTS_FLIP_PERM:\n SMPL_POSE_FLIP_PERM.append(3*i)\n SMPL_POSE_FLIP_PERM.append(3*i+1)\n SMPL_POSE_FLIP_PERM.append(3*i+2)\n# Permutation indices for the 24 ground truth joints\nJ24_FLIP_PERM = [5, 4, 3, 2, 1, 0, 11, 10, 9, 8, 7, 6, 12, 13, 14, 15, 16, 17, 18, 19, 21, 20, 23, 22]\n# Permutation indices for the full set of 49 joints\nJ49_FLIP_PERM = [0, 1, 5, 6, 7, 2, 3, 4, 8, 12, 13, 14, 9, 10, 11, 16, 15, 18, 17, 22, 23, 24, 19, 20, 21]\\",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "SMPL_POSE_FLIP_PERM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "SMPL_POSE_FLIP_PERM = []\nfor i in SMPL_JOINTS_FLIP_PERM:\n SMPL_POSE_FLIP_PERM.append(3*i)\n SMPL_POSE_FLIP_PERM.append(3*i+1)\n SMPL_POSE_FLIP_PERM.append(3*i+2)\n# Permutation indices for the 24 ground truth joints\nJ24_FLIP_PERM = [5, 4, 3, 2, 1, 0, 11, 10, 9, 8, 7, 6, 12, 13, 14, 15, 16, 17, 18, 19, 21, 20, 23, 22]\n# Permutation indices for the full set of 49 joints\nJ49_FLIP_PERM = [0, 1, 5, 6, 7, 2, 3, 4, 8, 12, 13, 14, 9, 10, 11, 16, 15, 18, 17, 22, 23, 24, 19, 20, 21]\\\n + [25+i for i in J24_FLIP_PERM]",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "J24_FLIP_PERM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "J24_FLIP_PERM = [5, 4, 3, 2, 1, 0, 11, 10, 9, 8, 7, 6, 12, 13, 14, 15, 16, 17, 18, 19, 21, 20, 23, 22]\n# Permutation indices for the full set of 49 joints\nJ49_FLIP_PERM = [0, 1, 5, 6, 7, 2, 3, 4, 8, 12, 13, 14, 9, 10, 11, 16, 15, 18, 17, 22, 23, 24, 19, 20, 21]\\\n + [25+i for i in J24_FLIP_PERM]\nSMPL_J49_FLIP_PERM = [0, 1, 5, 6, 7, 2, 3, 4, 8, 12, 13, 14, 9, 10, 11, 16, 15, 18, 17, 22, 23, 24, 19, 20, 21]\\\n + [25+i for i in SMPL_JOINTS_FLIP_PERM]\nSMPLX2SMPL_J45 = [i for i in range(22)] + [30, 45] + [i for i in range(55, 55+21)]\nSMPL_PART_ID = {'rightHand': 1, 'rightUpLeg': 2, 'leftArm': 3, 'leftLeg': 4, 'leftToeBase': 5, \n 'leftFoot': 6, 'spine1': 7, 'spine2': 8, 'leftShoulder': 9, 'rightShoulder': 10, 'rightFoot': 11, \n 'head': 12, 'rightArm': 13, 'leftHandIndex1': 14, 'rightLeg': 15, 'rightHandIndex1': 16, 'leftForeArm': 17, 'rightForeArm': 18, ",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "J49_FLIP_PERM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "J49_FLIP_PERM = [0, 1, 5, 6, 7, 2, 3, 4, 8, 12, 13, 14, 9, 10, 11, 16, 15, 18, 17, 22, 23, 24, 19, 20, 21]\\\n + [25+i for i in J24_FLIP_PERM]\nSMPL_J49_FLIP_PERM = [0, 1, 5, 6, 7, 2, 3, 4, 8, 12, 13, 14, 9, 10, 11, 16, 15, 18, 17, 22, 23, 24, 19, 20, 21]\\\n + [25+i for i in SMPL_JOINTS_FLIP_PERM]\nSMPLX2SMPL_J45 = [i for i in range(22)] + [30, 45] + [i for i in range(55, 55+21)]\nSMPL_PART_ID = {'rightHand': 1, 'rightUpLeg': 2, 'leftArm': 3, 'leftLeg': 4, 'leftToeBase': 5, \n 'leftFoot': 6, 'spine1': 7, 'spine2': 8, 'leftShoulder': 9, 'rightShoulder': 10, 'rightFoot': 11, \n 'head': 12, 'rightArm': 13, 'leftHandIndex1': 14, 'rightLeg': 15, 'rightHandIndex1': 16, 'leftForeArm': 17, 'rightForeArm': 18, \n 'neck': 19, 'rightToeBase': 20, 'spine': 21, 'leftUpLeg': 22, 'leftHand': 23, 'hips': 24}\n# MANO_NAMES = [",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "SMPL_J49_FLIP_PERM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "SMPL_J49_FLIP_PERM = [0, 1, 5, 6, 7, 2, 3, 4, 8, 12, 13, 14, 9, 10, 11, 16, 15, 18, 17, 22, 23, 24, 19, 20, 21]\\\n + [25+i for i in SMPL_JOINTS_FLIP_PERM]\nSMPLX2SMPL_J45 = [i for i in range(22)] + [30, 45] + [i for i in range(55, 55+21)]\nSMPL_PART_ID = {'rightHand': 1, 'rightUpLeg': 2, 'leftArm': 3, 'leftLeg': 4, 'leftToeBase': 5, \n 'leftFoot': 6, 'spine1': 7, 'spine2': 8, 'leftShoulder': 9, 'rightShoulder': 10, 'rightFoot': 11, \n 'head': 12, 'rightArm': 13, 'leftHandIndex1': 14, 'rightLeg': 15, 'rightHandIndex1': 16, 'leftForeArm': 17, 'rightForeArm': 18, \n 'neck': 19, 'rightToeBase': 20, 'spine': 21, 'leftUpLeg': 22, 'leftHand': 23, 'hips': 24}\n# MANO_NAMES = [\n# 'wrist',\n# 'index1',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "SMPLX2SMPL_J45",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "SMPLX2SMPL_J45 = [i for i in range(22)] + [30, 45] + [i for i in range(55, 55+21)]\nSMPL_PART_ID = {'rightHand': 1, 'rightUpLeg': 2, 'leftArm': 3, 'leftLeg': 4, 'leftToeBase': 5, \n 'leftFoot': 6, 'spine1': 7, 'spine2': 8, 'leftShoulder': 9, 'rightShoulder': 10, 'rightFoot': 11, \n 'head': 12, 'rightArm': 13, 'leftHandIndex1': 14, 'rightLeg': 15, 'rightHandIndex1': 16, 'leftForeArm': 17, 'rightForeArm': 18, \n 'neck': 19, 'rightToeBase': 20, 'spine': 21, 'leftUpLeg': 22, 'leftHand': 23, 'hips': 24}\n# MANO_NAMES = [\n# 'wrist',\n# 'index1',\n# 'index2',\n# 'index3',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "SMPL_PART_ID",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "SMPL_PART_ID = {'rightHand': 1, 'rightUpLeg': 2, 'leftArm': 3, 'leftLeg': 4, 'leftToeBase': 5, \n 'leftFoot': 6, 'spine1': 7, 'spine2': 8, 'leftShoulder': 9, 'rightShoulder': 10, 'rightFoot': 11, \n 'head': 12, 'rightArm': 13, 'leftHandIndex1': 14, 'rightLeg': 15, 'rightHandIndex1': 16, 'leftForeArm': 17, 'rightForeArm': 18, \n 'neck': 19, 'rightToeBase': 20, 'spine': 21, 'leftUpLeg': 22, 'leftHand': 23, 'hips': 24}\n# MANO_NAMES = [\n# 'wrist',\n# 'index1',\n# 'index2',\n# 'index3',\n# 'middle1',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "HAND_NAMES",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "HAND_NAMES = [\n 'wrist',\n 'thumb1',\n 'thumb2',\n 'thumb3',\n 'thumb',\n 'index1',\n 'index2',\n 'index3',\n 'index',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "SMPLX_JOINT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "SMPLX_JOINT_NAMES = smplx_joint_name.JOINT_NAMES\nSMPLX_JOINT_IDS = {SMPLX_JOINT_NAMES[i]: i for i in range(len(SMPLX_JOINT_NAMES))}\nFOOT_NAMES = ['big_toe', 'small_toe', 'heel']\nFACIAL_LANDMARKS = [\n 'right_eye_brow1',\n 'right_eye_brow2',\n 'right_eye_brow3',\n 'right_eye_brow4',\n 'right_eye_brow5',\n 'left_eye_brow5',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "SMPLX_JOINT_IDS",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "SMPLX_JOINT_IDS = {SMPLX_JOINT_NAMES[i]: i for i in range(len(SMPLX_JOINT_NAMES))}\nFOOT_NAMES = ['big_toe', 'small_toe', 'heel']\nFACIAL_LANDMARKS = [\n 'right_eye_brow1',\n 'right_eye_brow2',\n 'right_eye_brow3',\n 'right_eye_brow4',\n 'right_eye_brow5',\n 'left_eye_brow5',\n 'left_eye_brow4',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "FOOT_NAMES",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "FOOT_NAMES = ['big_toe', 'small_toe', 'heel']\nFACIAL_LANDMARKS = [\n 'right_eye_brow1',\n 'right_eye_brow2',\n 'right_eye_brow3',\n 'right_eye_brow4',\n 'right_eye_brow5',\n 'left_eye_brow5',\n 'left_eye_brow4',\n 'left_eye_brow3',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "FACIAL_LANDMARKS",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "FACIAL_LANDMARKS = [\n 'right_eye_brow1',\n 'right_eye_brow2',\n 'right_eye_brow3',\n 'right_eye_brow4',\n 'right_eye_brow5',\n 'left_eye_brow5',\n 'left_eye_brow4',\n 'left_eye_brow3',\n 'left_eye_brow2',",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "LRHAND_FLIP_PERM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "LRHAND_FLIP_PERM = [i for i in range(len(HAND_NAMES), len(HAND_NAMES)*2)] + [i for i in range(len(HAND_NAMES))]\nSINGLE_HAND_FLIP_PERM = [i for i in range(len(HAND_NAMES))]\nFEEF_FLIP_PERM = [i for i in range(len(FOOT_NAMES), len(FOOT_NAMES)*2)] + [i for i in range(len(FOOT_NAMES))]\n# matchedParts = (\n# [17, 26], [18, 25], [19, 24], [20, 23], [21, 22],\n# [21],[20],[19],[18],[17],\n# [27], [28], [29], [30], \n# [31, 35], [32, 34], [33],\n# [32],[31],\n# [36, 45], [37, 44], [38, 43], [39, 42], [40, 47], [41, 46],",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "SINGLE_HAND_FLIP_PERM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "SINGLE_HAND_FLIP_PERM = [i for i in range(len(HAND_NAMES))]\nFEEF_FLIP_PERM = [i for i in range(len(FOOT_NAMES), len(FOOT_NAMES)*2)] + [i for i in range(len(FOOT_NAMES))]\n# matchedParts = (\n# [17, 26], [18, 25], [19, 24], [20, 23], [21, 22],\n# [21],[20],[19],[18],[17],\n# [27], [28], [29], [30], \n# [31, 35], [32, 34], [33],\n# [32],[31],\n# [36, 45], [37, 44], [38, 43], [39, 42], [40, 47], [41, 46],\n# [39],[38], [37],[36],[41],[40],",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "FEEF_FLIP_PERM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "FEEF_FLIP_PERM = [i for i in range(len(FOOT_NAMES), len(FOOT_NAMES)*2)] + [i for i in range(len(FOOT_NAMES))]\n# matchedParts = (\n# [17, 26], [18, 25], [19, 24], [20, 23], [21, 22],\n# [21],[20],[19],[18],[17],\n# [27], [28], [29], [30], \n# [31, 35], [32, 34], [33],\n# [32],[31],\n# [36, 45], [37, 44], [38, 43], [39, 42], [40, 47], [41, 46],\n# [39],[38], [37],[36],[41],[40],\n# [48, 54], [49, 53], [50, 52], [51],",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "FACE_FLIP_PERM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "FACE_FLIP_PERM = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 10, 11, 12, 13, 18, 17, 16, 15, 14, 28, 27, 26, 25, 30, 29, 22, 21, 20, 19, 24, 23, 37, 36, 35, 34, 33, 32, 31, 42, 41, 40, 39, 38, 47, 46, 45, 44, 43, 50, 49, 48]\nFACE_FLIP_PERM = FACE_FLIP_PERM + [67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51]",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "FACE_FLIP_PERM",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.constants",
+ "description": "modules.PyMAF.pymaf_core.constants",
+ "peekOfCode": "FACE_FLIP_PERM = FACE_FLIP_PERM + [67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51]",
+ "detail": "modules.PyMAF.pymaf_core.constants",
+ "documentation": {}
+ },
+ {
+ "label": "JOINT_REGRESSOR_TRAIN_EXTRA",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.path_config",
+ "description": "modules.PyMAF.pymaf_core.path_config",
+ "peekOfCode": "JOINT_REGRESSOR_TRAIN_EXTRA = 'data/J_regressor_extra.npy'\nJOINT_REGRESSOR_H36M = 'data/J_regressor_h36m.npy'\nSMPL_MEAN_PARAMS = 'data/smpl_mean_params.npz'\nSMPL_MODEL_DIR = 'data/smpl'",
+ "detail": "modules.PyMAF.pymaf_core.path_config",
+ "documentation": {}
+ },
+ {
+ "label": "JOINT_REGRESSOR_H36M",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.path_config",
+ "description": "modules.PyMAF.pymaf_core.path_config",
+ "peekOfCode": "JOINT_REGRESSOR_H36M = 'data/J_regressor_h36m.npy'\nSMPL_MEAN_PARAMS = 'data/smpl_mean_params.npz'\nSMPL_MODEL_DIR = 'data/smpl'",
+ "detail": "modules.PyMAF.pymaf_core.path_config",
+ "documentation": {}
+ },
+ {
+ "label": "SMPL_MEAN_PARAMS",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.path_config",
+ "description": "modules.PyMAF.pymaf_core.path_config",
+ "peekOfCode": "SMPL_MEAN_PARAMS = 'data/smpl_mean_params.npz'\nSMPL_MODEL_DIR = 'data/smpl'",
+ "detail": "modules.PyMAF.pymaf_core.path_config",
+ "documentation": {}
+ },
+ {
+ "label": "SMPL_MODEL_DIR",
+ "kind": 5,
+ "importPath": "modules.PyMAF.pymaf_core.path_config",
+ "description": "modules.PyMAF.pymaf_core.path_config",
+ "peekOfCode": "SMPL_MODEL_DIR = 'data/smpl'",
+ "detail": "modules.PyMAF.pymaf_core.path_config",
+ "documentation": {}
+ },
+ {
+ "label": "Voxels",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.binvox_rw",
+ "description": "modules.PyMAF.utils.binvox_rw",
+ "peekOfCode": "class Voxels(object):\n \"\"\" Holds a binvox model.\n data is either a three-dimensional numpy boolean array (dense representation)\n or a two-dimensional numpy float array (coordinate representation).\n dims, translate and scale are the model metadata.\n dims are the voxel dimensions, e.g. [32, 32, 32] for a 32x32x32 model.\n scale and translate relate the voxels to the original model coordinates.\n To translate voxel coordinates i, j, k to original coordinates x, y, z:\n x_n = (i+.5)/dims[0]\n y_n = (j+.5)/dims[1]",
+ "detail": "modules.PyMAF.utils.binvox_rw",
+ "documentation": {}
+ },
+ {
+ "label": "read_header",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.binvox_rw",
+ "description": "modules.PyMAF.utils.binvox_rw",
+ "peekOfCode": "def read_header(fp):\n \"\"\" Read binvox header. Mostly meant for internal use.\n \"\"\"\n line = fp.readline().strip()\n if not line.startswith(b'#binvox'):\n raise IOError('Not a binvox file')\n dims = [int(i) for i in fp.readline().strip().split(b' ')[1:]]\n translate = [float(i) for i in fp.readline().strip().split(b' ')[1:]]\n scale = [float(i) for i in fp.readline().strip().split(b' ')[1:]][0]\n line = fp.readline()",
+ "detail": "modules.PyMAF.utils.binvox_rw",
+ "documentation": {}
+ },
+ {
+ "label": "read_as_3d_array",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.binvox_rw",
+ "description": "modules.PyMAF.utils.binvox_rw",
+ "peekOfCode": "def read_as_3d_array(fp, fix_coords=True):\n \"\"\" Read binary binvox format as array.\n Returns the model with accompanying metadata.\n Voxels are stored in a three-dimensional numpy array, which is simple and\n direct, but may use a lot of memory for large models. (Storage requirements\n are 8*(d^3) bytes, where d is the dimensions of the binvox model. Numpy\n boolean arrays use a byte per element).\n Doesn't do any checks on input except for the '#binvox' line.\n \"\"\"\n dims, translate, scale = read_header(fp)",
+ "detail": "modules.PyMAF.utils.binvox_rw",
+ "documentation": {}
+ },
+ {
+ "label": "read_as_coord_array",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.binvox_rw",
+ "description": "modules.PyMAF.utils.binvox_rw",
+ "peekOfCode": "def read_as_coord_array(fp, fix_coords=True):\n \"\"\" Read binary binvox format as coordinates.\n Returns binvox model with voxels in a \"coordinate\" representation, i.e. an\n 3 x N array where N is the number of nonzero voxels. Each column\n corresponds to a nonzero voxel and the 3 rows are the (x, z, y) coordinates\n of the voxel. (The odd ordering is due to the way binvox format lays out\n data). Note that coordinates refer to the binvox voxels, without any\n scaling or translation.\n Use this to save memory if your model is very sparse (mostly empty).\n Doesn't do any checks on input except for the '#binvox' line.",
+ "detail": "modules.PyMAF.utils.binvox_rw",
+ "documentation": {}
+ },
+ {
+ "label": "dense_to_sparse",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.binvox_rw",
+ "description": "modules.PyMAF.utils.binvox_rw",
+ "peekOfCode": "def dense_to_sparse(voxel_data, dtype=np.int):\n \"\"\" From dense representation to sparse (coordinate) representation.\n No coordinate reordering.\n \"\"\"\n if voxel_data.ndim!=3:\n raise ValueError('voxel_data is wrong shape; should be 3D array.')\n return np.asarray(np.nonzero(voxel_data), dtype)\ndef sparse_to_dense(voxel_data, dims, dtype=np.bool):\n if voxel_data.ndim!=2 or voxel_data.shape[0]!=3:\n raise ValueError('voxel_data is wrong shape; should be 3xN array.')",
+ "detail": "modules.PyMAF.utils.binvox_rw",
+ "documentation": {}
+ },
+ {
+ "label": "sparse_to_dense",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.binvox_rw",
+ "description": "modules.PyMAF.utils.binvox_rw",
+ "peekOfCode": "def sparse_to_dense(voxel_data, dims, dtype=np.bool):\n if voxel_data.ndim!=2 or voxel_data.shape[0]!=3:\n raise ValueError('voxel_data is wrong shape; should be 3xN array.')\n if np.isscalar(dims):\n dims = [dims]*3\n dims = np.atleast_2d(dims).T\n # truncate to integers\n xyz = voxel_data.astype(np.int)\n # discard voxels that fall outside dims\n valid_ix = ~np.any((xyz < 0) | (xyz >= dims), 0)",
+ "detail": "modules.PyMAF.utils.binvox_rw",
+ "documentation": {}
+ },
+ {
+ "label": "write",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.binvox_rw",
+ "description": "modules.PyMAF.utils.binvox_rw",
+ "peekOfCode": "def write(voxel_model, fp):\n \"\"\" Write binary binvox format.\n Note that when saving a model in sparse (coordinate) format, it is first\n converted to dense format.\n Doesn't check if the model is 'sane'.\n \"\"\"\n if voxel_model.data.ndim==2:\n # TODO avoid conversion to dense\n dense_voxel_data = sparse_to_dense(voxel_model.data, voxel_model.dims)\n else:",
+ "detail": "modules.PyMAF.utils.binvox_rw",
+ "documentation": {}
+ },
+ {
+ "label": "get_image_blob",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.blob",
+ "description": "modules.PyMAF.utils.blob",
+ "peekOfCode": "def get_image_blob(im, target_scale, target_max_size):\n \"\"\"Convert an image into a network input.\n Arguments:\n im (ndarray): a color image in BGR order\n Returns:\n blob (ndarray): a data blob holding an image pyramid\n im_scale (float): image scale (target size) / (original size)\n im_info (ndarray)\n \"\"\"\n processed_im, im_scale = prep_im_for_blob(",
+ "detail": "modules.PyMAF.utils.blob",
+ "documentation": {}
+ },
+ {
+ "label": "im_list_to_blob",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.blob",
+ "description": "modules.PyMAF.utils.blob",
+ "peekOfCode": "def im_list_to_blob(ims):\n \"\"\"Convert a list of images into a network input. Assumes images were\n prepared using prep_im_for_blob or equivalent: i.e.\n - BGR channel order\n - pixel means subtracted\n - resized to the desired input size\n - float32 numpy ndarray format\n Output is a 4D HCHW tensor of the images concatenated along axis 0 with\n shape.\n \"\"\"",
+ "detail": "modules.PyMAF.utils.blob",
+ "documentation": {}
+ },
+ {
+ "label": "get_max_shape",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.blob",
+ "description": "modules.PyMAF.utils.blob",
+ "peekOfCode": "def get_max_shape(im_shapes):\n \"\"\"Calculate max spatial size (h, w) for batching given a list of image shapes\n \"\"\"\n max_shape = np.array(im_shapes).max(axis=0)\n assert max_shape.size == 2\n # Pad the image so they can be divisible by a stride\n if cfg.FPN.FPN_ON:\n stride = float(cfg.FPN.COARSEST_STRIDE)\n max_shape[0] = int(np.ceil(max_shape[0] / stride) * stride)\n max_shape[1] = int(np.ceil(max_shape[1] / stride) * stride)",
+ "detail": "modules.PyMAF.utils.blob",
+ "documentation": {}
+ },
+ {
+ "label": "prep_im_for_blob",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.blob",
+ "description": "modules.PyMAF.utils.blob",
+ "peekOfCode": "def prep_im_for_blob(im, pixel_means, target_sizes, max_size):\n \"\"\"Prepare an image for use as a network input blob. Specially:\n - Subtract per-channel pixel mean\n - Convert to float32\n - Rescale to each of the specified target size (capped at max_size)\n Returns a list of transformed images, one for each target size. Also returns\n the scale factors that were used to compute each returned image.\n \"\"\"\n im = im.astype(np.float32, copy=False)\n im -= pixel_means",
+ "detail": "modules.PyMAF.utils.blob",
+ "documentation": {}
+ },
+ {
+ "label": "get_im_blob_sizes",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.blob",
+ "description": "modules.PyMAF.utils.blob",
+ "peekOfCode": "def get_im_blob_sizes(im_shape, target_sizes, max_size):\n \"\"\"Calculate im blob size for multiple target_sizes given original im shape\n \"\"\"\n im_size_min = np.min(im_shape)\n im_size_max = np.max(im_shape)\n im_sizes = []\n for target_size in target_sizes:\n im_scale = get_target_scale(im_size_min, im_size_max, target_size, max_size)\n im_sizes.append(np.round(im_shape * im_scale))\n return np.array(im_sizes)",
+ "detail": "modules.PyMAF.utils.blob",
+ "documentation": {}
+ },
+ {
+ "label": "get_target_scale",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.blob",
+ "description": "modules.PyMAF.utils.blob",
+ "peekOfCode": "def get_target_scale(im_size_min, im_size_max, target_size, max_size):\n \"\"\"Calculate target resize scale\n \"\"\"\n im_scale = float(target_size) / float(im_size_min)\n # Prevent the biggest axis from being more than max_size\n if np.round(im_scale * im_size_max) > max_size:\n im_scale = float(max_size) / float(im_size_max)\n return im_scale\ndef zeros(shape, int32=False):\n \"\"\"Return a blob of all zeros of the given shape with the correct float or",
+ "detail": "modules.PyMAF.utils.blob",
+ "documentation": {}
+ },
+ {
+ "label": "zeros",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.blob",
+ "description": "modules.PyMAF.utils.blob",
+ "peekOfCode": "def zeros(shape, int32=False):\n \"\"\"Return a blob of all zeros of the given shape with the correct float or\n int data type.\n \"\"\"\n return np.zeros(shape, dtype=np.int32 if int32 else np.float32)\ndef ones(shape, int32=False):\n \"\"\"Return a blob of all ones of the given shape with the correct float or\n int data type.\n \"\"\"\n return np.ones(shape, dtype=np.int32 if int32 else np.float32)",
+ "detail": "modules.PyMAF.utils.blob",
+ "documentation": {}
+ },
+ {
+ "label": "ones",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.blob",
+ "description": "modules.PyMAF.utils.blob",
+ "peekOfCode": "def ones(shape, int32=False):\n \"\"\"Return a blob of all ones of the given shape with the correct float or\n int data type.\n \"\"\"\n return np.ones(shape, dtype=np.int32 if int32 else np.float32)\ndef serialize(obj):\n \"\"\"Serialize a Python object using pickle and encode it as an array of\n float32 values so that it can be feed into the workspace. See deserialize().\n \"\"\"\n return np.fromstring(pickle.dumps(obj), dtype=np.uint8).astype(np.float32)",
+ "detail": "modules.PyMAF.utils.blob",
+ "documentation": {}
+ },
+ {
+ "label": "serialize",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.blob",
+ "description": "modules.PyMAF.utils.blob",
+ "peekOfCode": "def serialize(obj):\n \"\"\"Serialize a Python object using pickle and encode it as an array of\n float32 values so that it can be feed into the workspace. See deserialize().\n \"\"\"\n return np.fromstring(pickle.dumps(obj), dtype=np.uint8).astype(np.float32)\ndef deserialize(arr):\n \"\"\"Unserialize a Python object from an array of float32 values fetched from\n a workspace. See serialize().\n \"\"\"\n return pickle.loads(arr.astype(np.uint8).tobytes())",
+ "detail": "modules.PyMAF.utils.blob",
+ "documentation": {}
+ },
+ {
+ "label": "deserialize",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.blob",
+ "description": "modules.PyMAF.utils.blob",
+ "peekOfCode": "def deserialize(arr):\n \"\"\"Unserialize a Python object from an array of float32 values fetched from\n a workspace. See serialize().\n \"\"\"\n return pickle.loads(arr.astype(np.uint8).tobytes())",
+ "detail": "modules.PyMAF.utils.blob",
+ "documentation": {}
+ },
+ {
+ "label": "f_pix2vfov",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.cam_params",
+ "description": "modules.PyMAF.utils.cam_params",
+ "peekOfCode": "def f_pix2vfov(f_pix, img_h):\n if torch.is_tensor(f_pix):\n fov = 2. * torch.arctan(img_h / (2. * f_pix))\n else:\n fov = 2. * np.arctan(img_h / (2. * f_pix))\n return fov\ndef vfov2f_pix(fov, img_h):\n if torch.is_tensor(fov):\n f_pix = img_h / 2. / torch.tan(fov / 2.)\n else:",
+ "detail": "modules.PyMAF.utils.cam_params",
+ "documentation": {}
+ },
+ {
+ "label": "vfov2f_pix",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.cam_params",
+ "description": "modules.PyMAF.utils.cam_params",
+ "peekOfCode": "def vfov2f_pix(fov, img_h):\n if torch.is_tensor(fov):\n f_pix = img_h / 2. / torch.tan(fov / 2.)\n else:\n f_pix = img_h / 2. / np.tan(fov / 2.)\n return f_pix\ndef read_cam_params(cam_params, orig_shape=None):\n # These are predicted camera parameters\n # cam_param_folder = CAM_PARAM_FOLDERS[dataset_name][cam_param_type]\n cam_pitch = cam_params['pitch'].item()",
+ "detail": "modules.PyMAF.utils.cam_params",
+ "documentation": {}
+ },
+ {
+ "label": "read_cam_params",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.cam_params",
+ "description": "modules.PyMAF.utils.cam_params",
+ "peekOfCode": "def read_cam_params(cam_params, orig_shape=None):\n # These are predicted camera parameters\n # cam_param_folder = CAM_PARAM_FOLDERS[dataset_name][cam_param_type]\n cam_pitch = cam_params['pitch'].item()\n cam_roll = cam_params['roll'].item() if 'roll' in cam_params else None\n cam_vfov = cam_params['vfov'].item() if 'vfov' in cam_params else None\n cam_focal_length = cam_params['f_pix']\n orig_shape = cam_params['orig_resolution']\n # cam_rotmat = batch_euler2matrix(torch.tensor([[cam_pitch, 0., cam_roll]]).float())[0]\n cam_rotmat = batch_euler2matrix(torch.tensor([[cam_pitch, 0., 0.]]).float())[0]",
+ "detail": "modules.PyMAF.utils.cam_params",
+ "documentation": {}
+ },
+ {
+ "label": "homo_vector",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.cam_params",
+ "description": "modules.PyMAF.utils.cam_params",
+ "peekOfCode": "def homo_vector(vector):\n \"\"\"\n vector: B x N x C\n h_vector: B x N x (C + 1)\n \"\"\"\n batch_size, n_pts = vector.shape[:2]\n h_vector = torch.cat([vector, torch.ones((batch_size, n_pts, 1)).to(vector)], dim=-1)\n return h_vector",
+ "detail": "modules.PyMAF.utils.cam_params",
+ "documentation": {}
+ },
+ {
+ "label": "AttrDict",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.collections",
+ "description": "modules.PyMAF.utils.collections",
+ "peekOfCode": "class AttrDict(dict):\n IMMUTABLE = '__immutable__'\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.__dict__[AttrDict.IMMUTABLE] = False\n def __getattr__(self, name):\n if name in self.__dict__:\n return self.__dict__[name]\n elif name in self:\n return self[name]",
+ "detail": "modules.PyMAF.utils.collections",
+ "documentation": {}
+ },
+ {
+ "label": "colormap",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.colormap",
+ "description": "modules.PyMAF.utils.colormap",
+ "peekOfCode": "def colormap(rgb=False):\n color_list = np.array(\n [\n 0.000, 0.447, 0.741,\n 0.850, 0.325, 0.098,\n 0.929, 0.694, 0.125,\n 0.494, 0.184, 0.556,\n 0.466, 0.674, 0.188,\n 0.301, 0.745, 0.933,\n 0.635, 0.078, 0.184,",
+ "detail": "modules.PyMAF.utils.colormap",
+ "documentation": {}
+ },
+ {
+ "label": "compute_iou",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def compute_iou(occ1, occ2):\n ''' Computes the Intersection over Union (IoU) value for two sets of\n occupancy values.\n Args:\n occ1 (tensor): first set of occupancy values\n occ2 (tensor): second set of occupancy values\n '''\n occ1 = np.asarray(occ1)\n occ2 = np.asarray(occ2)\n # Put all data in second dimension",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "rgb2gray",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def rgb2gray(rgb):\n ''' rgb of size B x h x w x 3\n '''\n r, g, b = rgb[:, :, :, 0], rgb[:, :, :, 1], rgb[:, :, :, 2]\n gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n return gray\ndef sample_patch_points(batch_size, n_points, patch_size=1,\n image_resolution=(128, 128), continuous=True):\n ''' Returns sampled points in the range [-1, 1].\n Args:",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "sample_patch_points",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def sample_patch_points(batch_size, n_points, patch_size=1,\n image_resolution=(128, 128), continuous=True):\n ''' Returns sampled points in the range [-1, 1].\n Args:\n batch_size (int): required batch size\n n_points (int): number of points to sample\n patch_size (int): size of patch; if > 1, patches of size patch_size\n are sampled instead of individual points\n image_resolution (tuple): image resolution (required for calculating\n the pixel distances)",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "get_proposal_points_in_unit_cube",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def get_proposal_points_in_unit_cube(ray0, ray_direction, padding=0.1,\n eps=1e-6, n_steps=40):\n ''' Returns n_steps equally spaced points inside the unit cube on the rays\n cast from ray0 with direction ray_direction.\n This function is used to get the ray marching points {p^ray_j} for a given\n camera position ray0 and\n a given ray direction ray_direction which goes from the camera_position to\n the pixel location.\n NOTE: The returned values d_proposal are the lengths of the ray:\n p^ray_j = ray0 + d_proposal_j * ray_direction",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "check_ray_intersection_with_unit_cube",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def check_ray_intersection_with_unit_cube(ray0, ray_direction, padding=0.1,\n eps=1e-6, scale=2.0):\n ''' Checks if rays ray0 + d * ray_direction intersect with unit cube with\n padding padding.\n It returns the two intersection points as well as the sorted ray lengths d.\n Args:\n ray0 (tensor): Start positions of the rays\n ray_direction (tensor): Directions of rays\n padding (float): Padding which is applied to the unit cube\n eps (float): The epsilon value for numerical stability",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "intersect_camera_rays_with_unit_cube",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def intersect_camera_rays_with_unit_cube(\n pixels, camera_mat, world_mat, scale_mat, padding=0.1, eps=1e-6,\n use_ray_length_as_depth=True):\n ''' Returns the intersection points of ray cast from camera origin to\n pixel points p on the image plane.\n The function returns the intersection points as well the depth values and\n a mask specifying which ray intersects the unit cube.\n Args:\n pixels (tensor): Pixel points on image plane (range [-1, 1])\n camera_mat (tensor): camera matrix",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "arange_pixels",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def arange_pixels(resolution=(128, 128), batch_size=1, image_range=(-1., 1.),\n subsample_to=None):\n ''' Arranges pixels for given resolution in range image_range.\n The function returns the unscaled pixel locations as integers and the\n scaled float values.\n Args:\n resolution (tuple): image resolution\n batch_size (int): batch size\n image_range (tuple): range of output points (default [-1, 1])\n subsample_to (int): if integer and > 0, the points are randomly",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "to_pytorch",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def to_pytorch(tensor, return_type=False):\n ''' Converts input tensor to pytorch.\n Args:\n tensor (tensor): Numpy or Pytorch tensor\n return_type (bool): whether to return input type\n '''\n is_numpy = False\n if type(tensor) == np.ndarray:\n tensor = torch.from_numpy(tensor)\n is_numpy = True",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "get_mask",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def get_mask(tensor):\n ''' Returns mask of non-illegal values for tensor.\n Args:\n tensor (tensor): Numpy or Pytorch tensor\n '''\n tensor, is_numpy = to_pytorch(tensor, True)\n mask = ((abs(tensor) != np.inf) & (torch.isnan(tensor) == False))\n mask = mask.to(torch.bool)\n if is_numpy:\n mask = mask.numpy()",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "transform_mesh",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def transform_mesh(mesh, transform):\n ''' Transforms a mesh with given transformation.\n Args:\n mesh (trimesh mesh): mesh\n transform (tensor): transformation matrix of size 4 x 4\n '''\n mesh = deepcopy(mesh)\n v = np.asarray(mesh.vertices).astype(np.float32)\n v_transformed = transform_pointcloud(v, transform)\n mesh.vertices = v_transformed",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "transform_pointcloud",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def transform_pointcloud(pointcloud, transform):\n ''' Transforms a point cloud with given transformation.\n Args:\n pointcloud (tensor): tensor of size N x 3\n transform (tensor): transformation of size 4 x 4\n '''\n assert(transform.shape == (4, 4) and pointcloud.shape[-1] == 3)\n pcl, is_numpy = to_pytorch(pointcloud, True)\n transform = to_pytorch(transform)\n # Transform point cloud to homogen coordinate system",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "transform_points_batch",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def transform_points_batch(p, transform):\n ''' Transform points tensor with given transform.\n Args:\n p (tensor): tensor of size B x N x 3\n transform (tensor): transformation of size B x 4 x 4\n '''\n device = p.device\n assert(transform.shape[1:] == (4, 4) and p.shape[-1]\n == 3 and p.shape[0] == transform.shape[0])\n # Transform points to homogen coordinates",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "get_tensor_values",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def get_tensor_values(tensor, p, grid_sample=True, mode='nearest',\n with_mask=False, squeeze_channel_dim=False):\n '''\n Returns values from tensor at given location p.\n Args:\n tensor (tensor): tensor of size B x C x H x W\n p (tensor): position values scaled between [-1, 1] and\n of size B x N x 2\n grid_sample (boolean): whether to use grid sampling\n mode (string): what mode to perform grid sampling in",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "transform_to_world",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def transform_to_world(pixels, depth, camera_mat, world_mat, scale_mat,\n invert=True):\n ''' Transforms pixel positions p with given depth value d to world coordinates.\n Args:\n pixels (tensor): pixel tensor of size B x N x 2\n depth (tensor): depth tensor of size B x N x 1\n camera_mat (tensor): camera matrix\n world_mat (tensor): world matrix\n scale_mat (tensor): scale matrix\n invert (bool): whether to invert matrices (default: true)",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "transform_to_camera_space",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def transform_to_camera_space(p_world, camera_mat, world_mat, scale_mat):\n ''' Transforms world points to camera space.\n Args:\n p_world (tensor): world points tensor of size B x N x 3\n camera_mat (tensor): camera matrix\n world_mat (tensor): world matrix\n scale_mat (tensor): scale matrix\n '''\n batch_size, n_p, _ = p_world.shape\n device = p_world.device",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "origin_to_world",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def origin_to_world(n_points, camera_mat, world_mat, scale_mat, invert=True):\n ''' Transforms origin (camera location) to world coordinates.\n Args:\n n_points (int): how often the transformed origin is repeated in the\n form (batch_size, n_points, 3)\n camera_mat (tensor): camera matrix\n world_mat (tensor): world matrix\n scale_mat (tensor): scale matrix\n invert (bool): whether to invert the matrices (default: true)\n '''",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "image_points_to_world",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def image_points_to_world(image_points, camera_mat, world_mat, scale_mat,\n invert=True):\n ''' Transforms points on image plane to world coordinates.\n In contrast to transform_to_world, no depth value is needed as points on\n the image plane have a fixed depth of 1.\n Args:\n image_points (tensor): image points tensor of size B x N x 2\n camera_mat (tensor): camera matrix\n world_mat (tensor): world matrix\n scale_mat (tensor): scale matrix",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "check_weights",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def check_weights(params):\n ''' Checks weights for illegal values.\n Args:\n params (tensor): parameter tensor\n '''\n for k, v in params.items():\n if torch.isnan(v).any():\n logger_py.warn('NaN Values detected in model weight %s.' % k)\ndef check_tensor(tensor, tensorname='', input_tensor=None):\n ''' Checks tensor for illegal values.",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "check_tensor",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def check_tensor(tensor, tensorname='', input_tensor=None):\n ''' Checks tensor for illegal values.\n Args:\n tensor (tensor): tensor\n tensorname (string): name of tensor\n input_tensor (tensor): previous input\n '''\n if torch.isnan(tensor).any():\n logger_py.warn('Tensor %s contains nan values.' % tensorname)\n if input_tensor is not None:",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "get_prob_from_logits",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def get_prob_from_logits(logits):\n ''' Returns probabilities for logits\n Args:\n logits (tensor): logits\n '''\n odds = np.exp(logits)\n probs = odds / (1 + odds)\n return probs\ndef get_logits_from_prob(probs, eps=1e-4):\n ''' Returns logits for probabilities.",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "get_logits_from_prob",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def get_logits_from_prob(probs, eps=1e-4):\n ''' Returns logits for probabilities.\n Args:\n probs (tensor): probability tensor\n eps (float): epsilon value for numerical stability\n '''\n probs = np.clip(probs, a_min=eps, a_max=1-eps)\n logits = np.log(probs / (1 - probs))\n return logits\ndef chamfer_distance(points1, points2, use_kdtree=True, give_id=False):",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "chamfer_distance",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def chamfer_distance(points1, points2, use_kdtree=True, give_id=False):\n ''' Returns the chamfer distance for the sets of points.\n Args:\n points1 (numpy array): first point set\n points2 (numpy array): second point set\n use_kdtree (bool): whether to use a kdtree\n give_id (bool): whether to return the IDs of nearest points\n '''\n if use_kdtree:\n return chamfer_distance_kdtree(points1, points2, give_id=give_id)",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "chamfer_distance_naive",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def chamfer_distance_naive(points1, points2):\n ''' Naive implementation of the Chamfer distance.\n Args:\n points1 (numpy array): first point set\n points2 (numpy array): second point set\n '''\n assert(points1.size() == points2.size())\n batch_size, T, _ = points1.size()\n points1 = points1.view(batch_size, T, 1, 3)\n points2 = points2.view(batch_size, 1, T, 3)",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "chamfer_distance_kdtree",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def chamfer_distance_kdtree(points1, points2, give_id=False):\n ''' KD-tree based implementation of the Chamfer distance.\n Args:\n points1 (numpy array): first point set\n points2 (numpy array): second point set\n give_id (bool): whether to return the IDs of the nearest points\n '''\n # Points have size batch_size x T x 3\n batch_size = points1.size(0)\n # First convert points to numpy",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "get_nearest_neighbors_indices_batch",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def get_nearest_neighbors_indices_batch(points_src, points_tgt, k=1):\n ''' Returns the nearest neighbors for point sets batchwise.\n Args:\n points_src (numpy array): source points\n points_tgt (numpy array): target points\n k (int): number of nearest neighbors to return\n '''\n indices = []\n distances = []\n for (p1, p2) in zip(points_src, points_tgt):",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "normalize_imagenet",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def normalize_imagenet(x):\n ''' Normalize input images according to ImageNet standards.\n Args:\n x (tensor): input images\n '''\n x = x.clone()\n x[:, 0] = (x[:, 0] - 0.485) / 0.229\n x[:, 1] = (x[:, 1] - 0.456) / 0.224\n x[:, 2] = (x[:, 2] - 0.406) / 0.225\n return x",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "make_3d_grid",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def make_3d_grid(bb_min, bb_max, shape):\n ''' Makes a 3D grid.\n Args:\n bb_min (tuple): bounding box minimum\n bb_max (tuple): bounding box maximum\n shape (tuple): output shape\n '''\n size = shape[0] * shape[1] * shape[2]\n pxs = torch.linspace(bb_min[0], bb_max[0], shape[0])\n pys = torch.linspace(bb_min[1], bb_max[1], shape[1])",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "get_occupancy_loss_points",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def get_occupancy_loss_points(pixels, camera_mat, world_mat, scale_mat,\n depth_image=None, use_cube_intersection=True,\n occupancy_random_normal=False,\n depth_range=[0, 2.4]):\n ''' Returns 3D points for occupancy loss.\n Args:\n pixels (tensor): sampled pixels in range [-1, 1]\n camera_mat (tensor): camera matrix\n world_mat (tensor): world matrix\n scale_mat (tensor): scale matrix",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "get_freespace_loss_points",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def get_freespace_loss_points(pixels, camera_mat, world_mat, scale_mat,\n use_cube_intersection=True, depth_range=[0, 2.4]):\n ''' Returns 3D points for freespace loss.\n Args:\n pixels (tensor): sampled pixels in range [-1, 1]\n camera_mat (tensor): camera matrix\n world_mat (tensor): world matrix\n scale_mat (tensor): scale matrix\n use_cube_intersection (bool): whether to check unit cube intersection\n depth_range (float): depth range; important when no cube",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "normalize_tensor",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "def normalize_tensor(tensor, min_norm=1e-5, feat_dim=-1):\n ''' Normalizes the tensor.\n Args:\n tensor (tensor): tensor\n min_norm (float): minimum norm for numerical stability\n feat_dim (int): feature dimension in tensor (default: -1)\n '''\n norm_tensor = torch.clamp(torch.norm(tensor, dim=feat_dim, keepdim=True),\n min=min_norm)\n normed_tensor = tensor / norm_tensor",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "logger_py",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.common",
+ "description": "modules.PyMAF.utils.common",
+ "peekOfCode": "logger_py = logging.getLogger(__name__)\ndef compute_iou(occ1, occ2):\n ''' Computes the Intersection over Union (IoU) value for two sets of\n occupancy values.\n Args:\n occ1 (tensor): first set of occupancy values\n occ2 (tensor): second set of occupancy values\n '''\n occ1 = np.asarray(occ1)\n occ2 = np.asarray(occ2)",
+ "detail": "modules.PyMAF.utils.common",
+ "documentation": {}
+ },
+ {
+ "label": "RandomSampler",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.data_loader",
+ "description": "modules.PyMAF.utils.data_loader",
+ "peekOfCode": "class RandomSampler(Sampler):\n def __init__(self, data_source, checkpoint):\n self.data_source = data_source\n if checkpoint is not None and checkpoint['dataset_perm'] is not None:\n self.dataset_perm = checkpoint['dataset_perm']\n self.perm = self.dataset_perm[checkpoint['batch_size']*checkpoint['batch_idx']:]\n else:\n self.dataset_perm = torch.randperm(len(self.data_source)).tolist()\n self.perm = torch.randperm(len(self.data_source)).tolist() \n def __iter__(self):",
+ "detail": "modules.PyMAF.utils.data_loader",
+ "documentation": {}
+ },
+ {
+ "label": "SequentialSampler",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.data_loader",
+ "description": "modules.PyMAF.utils.data_loader",
+ "peekOfCode": "class SequentialSampler(Sampler):\n def __init__(self, data_source, checkpoint):\n self.data_source = data_source\n if checkpoint is not None and checkpoint['dataset_perm'] is not None:\n self.dataset_perm = checkpoint['dataset_perm']\n self.perm = self.dataset_perm[checkpoint['batch_size']*checkpoint['batch_idx']:]\n else:\n self.dataset_perm = list(range(len(self.data_source)))\n self.perm = self.dataset_perm\n def __iter__(self):",
+ "detail": "modules.PyMAF.utils.data_loader",
+ "documentation": {}
+ },
+ {
+ "label": "CheckpointDataLoader",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.data_loader",
+ "description": "modules.PyMAF.utils.data_loader",
+ "peekOfCode": "class CheckpointDataLoader(DataLoader):\n \"\"\"\n Extends torch.utils.data.DataLoader to handle resuming training from an arbitrary point within an epoch.\n \"\"\"\n def __init__(self, dataset, checkpoint=None, batch_size=1,\n shuffle=False, num_workers=0, pin_memory=False, drop_last=True,\n timeout=0, worker_init_fn=None):\n if shuffle:\n sampler = RandomSampler(dataset, checkpoint)\n else:",
+ "detail": "modules.PyMAF.utils.data_loader",
+ "documentation": {}
+ },
+ {
+ "label": "preprocess_video",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.demo_utils",
+ "description": "modules.PyMAF.utils.demo_utils",
+ "peekOfCode": "def preprocess_video(video, joints2d, bboxes, frames, scale=1.0, crop_size=224):\n \"\"\"\n Read video, do normalize and crop it according to the bounding box.\n If there are bounding box annotations, use them to crop the image.\n If no bounding box is specified but openpose detections are available, use them to get the bounding box.\n :param video (ndarray): input video\n :param joints2d (ndarray, NxJx3): openpose detections\n :param bboxes (ndarray, Nx5): bbox detections\n :param scale (float): bbox crop scaling factor\n :param crop_size (int): crop width and height",
+ "detail": "modules.PyMAF.utils.demo_utils",
+ "documentation": {}
+ },
+ {
+ "label": "download_youtube_clip",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.demo_utils",
+ "description": "modules.PyMAF.utils.demo_utils",
+ "peekOfCode": "def download_youtube_clip(url, download_folder):\n return YouTube(url).streams.first().download(output_path=download_folder)\ndef smplify_runner(\n pred_rotmat,\n pred_betas,\n pred_cam,\n j2d,\n device,\n batch_size,\n lr=1.0,",
+ "detail": "modules.PyMAF.utils.demo_utils",
+ "documentation": {}
+ },
+ {
+ "label": "smplify_runner",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.demo_utils",
+ "description": "modules.PyMAF.utils.demo_utils",
+ "peekOfCode": "def smplify_runner(\n pred_rotmat,\n pred_betas,\n pred_cam,\n j2d,\n device,\n batch_size,\n lr=1.0,\n opt_steps=1,\n use_lbfgs=True,",
+ "detail": "modules.PyMAF.utils.demo_utils",
+ "documentation": {}
+ },
+ {
+ "label": "trim_videos",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.demo_utils",
+ "description": "modules.PyMAF.utils.demo_utils",
+ "peekOfCode": "def trim_videos(filename, start_time, end_time, output_filename):\n command = ['ffmpeg',\n '-i', '\"%s\"' % filename,\n '-ss', str(start_time),\n '-t', str(end_time - start_time),\n '-c:v', 'libx264', '-c:a', 'copy',\n '-threads', '1',\n '-loglevel', 'panic',\n '\"%s\"' % output_filename]\n # command = ' '.join(command)",
+ "detail": "modules.PyMAF.utils.demo_utils",
+ "documentation": {}
+ },
+ {
+ "label": "video_to_images",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.demo_utils",
+ "description": "modules.PyMAF.utils.demo_utils",
+ "peekOfCode": "def video_to_images(vid_file, img_folder=None, return_info=False):\n if img_folder is None:\n img_folder = osp.join(osp.expanduser('~'), 'tmp', osp.basename(vid_file).replace('.', '_'))\n # img_folder = osp.join('/tmp', osp.basename(vid_file).replace('.', '_'))\n print(img_folder)\n os.makedirs(img_folder, exist_ok=True)\n command = ['ffmpeg',\n '-i', vid_file,\n '-f', 'image2',\n '-v', 'error',",
+ "detail": "modules.PyMAF.utils.demo_utils",
+ "documentation": {}
+ },
+ {
+ "label": "download_url",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.demo_utils",
+ "description": "modules.PyMAF.utils.demo_utils",
+ "peekOfCode": "def download_url(url, outdir):\n print(f'Downloading files from {url}')\n cmd = ['wget', '-c', url, '-P', outdir]\n subprocess.call(cmd)\ndef download_ckpt(outdir='data/vibe_data', use_3dpw=False):\n os.makedirs(outdir, exist_ok=True)\n if use_3dpw:\n ckpt_file = 'data/vibe_data/vibe_model_w_3dpw.pth.tar'\n url = 'https://www.dropbox.com/s/41ozgqorcp095ja/vibe_model_w_3dpw.pth.tar'\n if not os.path.isfile(ckpt_file):",
+ "detail": "modules.PyMAF.utils.demo_utils",
+ "documentation": {}
+ },
+ {
+ "label": "download_ckpt",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.demo_utils",
+ "description": "modules.PyMAF.utils.demo_utils",
+ "peekOfCode": "def download_ckpt(outdir='data/vibe_data', use_3dpw=False):\n os.makedirs(outdir, exist_ok=True)\n if use_3dpw:\n ckpt_file = 'data/vibe_data/vibe_model_w_3dpw.pth.tar'\n url = 'https://www.dropbox.com/s/41ozgqorcp095ja/vibe_model_w_3dpw.pth.tar'\n if not os.path.isfile(ckpt_file):\n download_url(url=url, outdir=outdir)\n else:\n ckpt_file = 'data/vibe_data/vibe_model_wo_3dpw.pth.tar'\n url = 'https://www.dropbox.com/s/amj2p8bmf6g56k6/vibe_model_wo_3dpw.pth.tar'",
+ "detail": "modules.PyMAF.utils.demo_utils",
+ "documentation": {}
+ },
+ {
+ "label": "images_to_video",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.demo_utils",
+ "description": "modules.PyMAF.utils.demo_utils",
+ "peekOfCode": "def images_to_video(img_folder, output_vid_file):\n os.makedirs(img_folder, exist_ok=True)\n command = [\n 'ffmpeg', '-y', '-threads', '16', '-i', f'{img_folder}/%06d.png', '-profile:v', 'baseline',\n '-level', '3.0', '-c:v', 'libx264', '-pix_fmt', 'yuv420p', '-an', '-v', 'error', output_vid_file,\n ]\n print(f'Running \\\"{\" \".join(command)}\\\"')\n try:\n subprocess.call(command)\n except:",
+ "detail": "modules.PyMAF.utils.demo_utils",
+ "documentation": {}
+ },
+ {
+ "label": "convert_crop_cam_to_orig_img",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.demo_utils",
+ "description": "modules.PyMAF.utils.demo_utils",
+ "peekOfCode": "def convert_crop_cam_to_orig_img(cam, bbox, img_width, img_height):\n '''\n Convert predicted camera from cropped image coordinates\n to original image coordinates\n :param cam (ndarray, shape=(3,)): weak perspective camera in cropped img coordinates\n :param bbox (ndarray, shape=(4,)): bbox coordinates (c_x, c_y, h)\n :param img_width (int): original image width\n :param img_height (int): original image height\n :return:\n '''",
+ "detail": "modules.PyMAF.utils.demo_utils",
+ "documentation": {}
+ },
+ {
+ "label": "prepare_rendering_results",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.demo_utils",
+ "description": "modules.PyMAF.utils.demo_utils",
+ "peekOfCode": "def prepare_rendering_results(results_dict, nframes):\n frame_results = [{} for _ in range(nframes)]\n for person_id, person_data in results_dict.items():\n for idx, frame_id in enumerate(person_data['frame_ids']):\n frame_results[frame_id][person_id] = {\n 'verts': person_data['verts'][idx],\n 'smplx_verts': person_data['smplx_verts'][idx] if 'smplx_verts' in person_data else None,\n 'cam': person_data['orig_cam'][idx],\n 'cam_t': person_data['orig_cam_t'][idx] if 'orig_cam_t' in person_data else None,\n # 'cam': person_data['pred_cam'][idx],",
+ "detail": "modules.PyMAF.utils.demo_utils",
+ "documentation": {}
+ },
+ {
+ "label": "DensePoseMethods",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.densepose_methods",
+ "description": "modules.PyMAF.utils.densepose_methods",
+ "peekOfCode": "class DensePoseMethods:\n def __init__(self):\n #\n ALP_UV = loadmat(os.path.join('./data/UV_data', 'UV_Processed.mat'))\n self.FaceIndices = np.array(ALP_UV['All_FaceIndices']).squeeze()\n self.FacesDensePose = ALP_UV['All_Faces'] - 1\n self.U_norm = ALP_UV['All_U_norm'].squeeze()\n self.V_norm = ALP_UV['All_V_norm'].squeeze()\n self.All_vertices = ALP_UV['All_vertices'][0]\n ## Info to compute symmetries.",
+ "detail": "modules.PyMAF.utils.densepose_methods",
+ "documentation": {}
+ },
+ {
+ "label": "batch_rodrigues",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def batch_rodrigues(theta):\n \"\"\"Convert axis-angle representation to rotation matrix.\n Args:\n theta: size = [B, 3]\n Returns:\n Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]\n \"\"\"\n l1norm = torch.norm(theta + 1e-8, p = 2, dim = 1)\n angle = torch.unsqueeze(l1norm, -1)\n normalized = torch.div(theta, angle)",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "quat_to_rotmat",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def quat_to_rotmat(quat):\n \"\"\"Convert quaternion coefficients to rotation matrix.\n Args:\n quat: size = [B, 4] 4 <===>(w, x, y, z)\n Returns:\n Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]\n \"\"\" \n norm_quat = quat\n norm_quat = norm_quat/norm_quat.norm(p=2, dim=1, keepdim=True)\n w, x, y, z = norm_quat[:,0], norm_quat[:,1], norm_quat[:,2], norm_quat[:,3]",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "rotation_matrix_to_angle_axis",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def rotation_matrix_to_angle_axis(rotation_matrix):\n \"\"\"\n This function is borrowed from https://github.com/kornia/kornia\n Convert 3x4 rotation matrix to Rodrigues vector\n Args:\n rotation_matrix (Tensor): rotation matrix.\n Returns:\n Tensor: Rodrigues vector transformation.\n Shape:\n - Input: :math:`(N, 3, 4)`",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "quaternion_to_angle_axis",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def quaternion_to_angle_axis(quaternion: torch.Tensor) -> torch.Tensor:\n \"\"\"\n This function is borrowed from https://github.com/kornia/kornia\n Convert quaternion vector to angle axis of rotation.\n Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h\n Args:\n quaternion (torch.Tensor): tensor with quaternions.\n Return:\n torch.Tensor: tensor with angle axis of rotation.\n Shape:",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "quaternion_to_angle",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def quaternion_to_angle(quaternion: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Convert quaternion vector to angle of the rotation.\n Args:\n quaternion (torch.Tensor): tensor with quaternions.\n Return:\n torch.Tensor: tensor with angle axis of rotation.\n Shape:\n - Input: :math:`(*, 4)` where `*` means, any number of dimensions\n - Output: :math:`(*, 1)`",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "rotation_matrix_to_quaternion",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6):\n \"\"\"\n This function is borrowed from https://github.com/kornia/kornia\n Convert 3x4 rotation matrix to 4d quaternion vector\n This algorithm is based on algorithm described in\n https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201\n Args:\n rotation_matrix (Tensor): the rotation matrix to convert.\n Return:\n Tensor: the rotation in quaternion",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "batch_euler2matrix",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def batch_euler2matrix(r):\n return quaternion_to_rotation_matrix(euler_to_quaternion(r))\ndef euler_to_quaternion(r):\n x = r[..., 0]\n y = r[..., 1]\n z = r[..., 2]\n z = z/2.0\n y = y/2.0\n x = x/2.0\n cz = torch.cos(z)",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "euler_to_quaternion",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def euler_to_quaternion(r):\n x = r[..., 0]\n y = r[..., 1]\n z = r[..., 2]\n z = z/2.0\n y = y/2.0\n x = x/2.0\n cz = torch.cos(z)\n sz = torch.sin(z)\n cy = torch.cos(y)",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "quaternion_to_rotation_matrix",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def quaternion_to_rotation_matrix(quat):\n \"\"\"Convert quaternion coefficients to rotation matrix.\n Args:\n quat: size = [B, 4] 4 <===>(w, x, y, z)\n Returns:\n Rotation matrix corresponding to the quaternion -- size = [B, 3, 3]\n \"\"\"\n norm_quat = quat\n norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True)\n w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:, 2], norm_quat[:, 3]",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "rot6d_to_rotmat",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def rot6d_to_rotmat(x):\n \"\"\"Convert 6D rotation representation to 3x3 rotation matrix.\n Based on Zhou et al., \"On the Continuity of Rotation Representations in Neural Networks\", CVPR 2019\n Input:\n (B,6) Batch of 6-D rotation representations\n Output:\n (B,3,3) Batch of corresponding rotation matrices\n \"\"\"\n if x.shape[-1] == 6:\n batch_size = x.shape[0]",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "rotmat_to_rot6d",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def rotmat_to_rot6d(x):\n \"\"\"Convert 3x3 rotation matrix to 6D rotation representation.\n Based on Zhou et al., \"On the Continuity of Rotation Representations in Neural Networks\", CVPR 2019\n Input:\n (B,3,3) Batch of corresponding rotation matrices\n Output:\n (B,6) Batch of 6-D rotation representations\n \"\"\"\n batch_size = x.shape[0]\n x = x[:, :, :2]",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "rotmat_to_angle",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def rotmat_to_angle(x):\n \"\"\"Convert rotation to one-D angle.\n Based on Zhou et al., \"On the Continuity of Rotation Representations in Neural Networks\", CVPR 2019\n Input:\n (B,2) Batch of corresponding rotation\n Output:\n (B,1) Batch of 1-D angle\n \"\"\"\n a = F.normalize(x)\n angle = torch.atan2(a[:, 0], a[:, 1]).unsqueeze(-1)",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "projection",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def projection(pred_joints, pred_camera, retain_z=False, iwp_mode=True):\n \"\"\" Project 3D points on the image plane based on the given camera info, \n Identity rotation and Weak Perspective (IWP) camera is used when iwp_mode=True, more about camera settings:\n SPEC: Seeing People in the Wild with an Estimated Camera, ICCV 2021\n \"\"\"\n batch_size = pred_joints.shape[0]\n if iwp_mode:\n cam_sxy = pred_camera['cam_sxy']\n pred_cam_t = torch.stack([cam_sxy[:, 1],\n cam_sxy[:, 2],",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "perspective_projection",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def perspective_projection(points, rotation, translation,\n focal_length=None, camera_center=None, cam_intrinsics=None, retain_z=False):\n \"\"\"\n This function computes the perspective projection of a set of points.\n Input:\n points (bs, N, 3): 3D points\n rotation (bs, 3, 3): Camera rotation\n translation (bs, 3): Camera translation\n focal_length (bs,) or scalar: Focal length\n camera_center (bs, 2): Camera center",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "convert_to_full_img_cam",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def convert_to_full_img_cam(\n pare_cam, bbox_height, bbox_center,\n img_w, img_h, focal_length):\n # Converts weak perspective camera estimated by PARE in\n # bbox coords to perspective camera in full image coordinates\n # from https://arxiv.org/pdf/2009.06549.pdf\n s, tx, ty = pare_cam[:, 0], pare_cam[:, 1], pare_cam[:, 2]\n res = 224\n r = bbox_height / res\n tz = 2 * focal_length / (r * res * s)",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "estimate_translation_np",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def estimate_translation_np(S, joints_2d, joints_conf, focal_length=5000, img_size=(224., 224.)):\n \"\"\"Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d.\n Input:\n S: (25, 3) 3D joint locations\n joints: (25, 3) 2D joint locations and confidence\n Returns:\n (3,) camera translation vector\n \"\"\"\n num_joints = S.shape[0]\n # focal length",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "estimate_translation",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def estimate_translation(S, joints_2d, focal_length=5000., img_size=224., use_all_kps=False):\n \"\"\"Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d.\n Input:\n S: (B, 49, 3) 3D joint locations\n joints: (B, 49, 3) 2D joint locations and confidence\n Returns:\n (B, 3) camera translation vectors\n \"\"\"\n if isinstance(focal_length, numbers.Number):\n focal_length = [focal_length,] * S.shape[0]",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "Rot_y",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def Rot_y(angle, category='torch', prepend_dim=True, device=None):\n\t'''Rotate around y-axis by angle\n\tArgs:\n\t\tcategory: 'torch' or 'numpy'\n\t\tprepend_dim: prepend an extra dimension\n\tReturn: Rotation matrix with shape [1, 3, 3] (prepend_dim=True)\n\t'''\n\tm = np.array([\n\t\t\t\t\t[np.cos(angle), 0., np.sin(angle)],\n\t\t\t\t\t[0., 1., 0.],",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "Rot_x",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def Rot_x(angle, category='torch', prepend_dim=True, device=None):\n\t'''Rotate around x-axis by angle\n\tArgs:\n\t\tcategory: 'torch' or 'numpy'\n\t\tprepend_dim: prepend an extra dimension\n\tReturn: Rotation matrix with shape [1, 3, 3] (prepend_dim=True)\n\t'''\n\tm = np.array([\n\t\t\t\t [1., 0., 0.],\n\t\t\t\t [0., np.cos(angle), -np.sin(angle)],",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "Rot_z",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def Rot_z(angle, category='torch', prepend_dim=True, device=None):\n\t'''Rotate around z-axis by angle\n\tArgs:\n\t\tcategory: 'torch' or 'numpy'\n\t\tprepend_dim: prepend an extra dimension\n\tReturn: Rotation matrix with shape [1, 3, 3] (prepend_dim=True)\n\t'''\n\tm = np.array([\n\t\t\t\t [np.cos(angle), -np.sin(angle), 0.],\n\t\t\t\t [np.sin(angle), np.cos(angle), 0.],",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "compute_twist_ratation",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "def compute_twist_ratation(rotation_matrix, twist_axis):\n '''\n Compute the twist component of given rotation and twist axis\n https://stackoverflow.com/questions/3684269/component-of-a-quaternion-rotation-around-an-axis\n Parameters\n ----------\n rotation_matrix : Tensor (B, 3, 3,)\n The rotation to convert\n twist_axis : Tensor (B, 3,)\n The twist axis",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "\tm",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "\tm = np.array([\n\t\t\t\t\t[np.cos(angle), 0., np.sin(angle)],\n\t\t\t\t\t[0., 1., 0.],\n\t\t\t\t\t[-np.sin(angle), 0., np.cos(angle)]\n\t\t\t\t])\n\tif category == 'torch':\n\t\tif prepend_dim:\n\t\t\treturn torch.tensor(m, dtype=torch.float, device=device).unsqueeze(0)\n\t\telse:\n\t\t\treturn torch.tensor(m, dtype=torch.float, device=device)",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "\tm",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "\tm = np.array([\n\t\t\t\t [1., 0., 0.],\n\t\t\t\t [0., np.cos(angle), -np.sin(angle)],\n\t\t\t\t [0., np.sin(angle), np.cos(angle)]\n\t\t\t\t])\n\tif category == 'torch':\n\t\tif prepend_dim:\n\t\t\treturn torch.tensor(m, dtype=torch.float, device=device).unsqueeze(0)\n\t\telse:\n\t\t\treturn torch.tensor(m, dtype=torch.float, device=device)",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "\tm",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.geometry",
+ "description": "modules.PyMAF.utils.geometry",
+ "peekOfCode": "\tm = np.array([\n\t\t\t\t [np.cos(angle), -np.sin(angle), 0.],\n\t\t\t\t [np.sin(angle), np.cos(angle), 0.],\n\t\t\t\t [0., 0., 1.]\n\t\t\t\t])\n\tif category == 'torch':\n\t\tif prepend_dim:\n\t\t\treturn torch.tensor(m, dtype=torch.float, device=device).unsqueeze(0)\n\t\telse:\n\t\t\treturn torch.tensor(m, dtype=torch.float, device=device)",
+ "detail": "modules.PyMAF.utils.geometry",
+ "documentation": {}
+ },
+ {
+ "label": "get_transform",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def get_transform(center, scale, res, rot=0):\n \"\"\"Generate transformation matrix.\"\"\"\n h = 200 * scale\n t = np.zeros((3, 3))\n t[0, 0] = float(res[1]) / h\n t[1, 1] = float(res[0]) / h\n t[0, 2] = res[1] * (-float(center[0]) / h + .5)\n t[1, 2] = res[0] * (-float(center[1]) / h + .5)\n t[2, 2] = 1\n if not rot == 0:",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "get_rot_transf",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def get_rot_transf(res, rot):\n \"\"\"Generate rotation transformation matrix.\"\"\"\n if rot == 0:\n return np.identity(3)\n rot = -rot # To match direction of rotation from cropping\n rot_mat = np.zeros((3,3))\n rot_rad = rot * np.pi / 180\n sn,cs = np.sin(rot_rad), np.cos(rot_rad)\n rot_mat[0,:2] = [cs, -sn]\n rot_mat[1,:2] = [sn, cs]",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "transform",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def transform(pt, center, scale, res, invert=0, rot=0):\n \"\"\"Transform pixel location to different reference.\"\"\"\n t = get_transform(center, scale, res, rot=rot)\n if invert:\n t = np.linalg.inv(t)\n new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T\n new_pt = np.dot(t, new_pt)\n return new_pt[:2].astype(int) + 1\ndef transform_pts(coords, center, scale, res, invert=0, rot=0):\n \"\"\"Transform coordinates (N x 2) to different reference.\"\"\"",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "transform_pts",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def transform_pts(coords, center, scale, res, invert=0, rot=0):\n \"\"\"Transform coordinates (N x 2) to different reference.\"\"\"\n new_coords = coords.copy()\n for p in range(coords.shape[0]):\n new_coords[p, 0:2] = transform(coords[p, 0:2], center, scale, res, invert, rot)\n return new_coords\ndef crop(img, center, scale, res, rot=0):\n \"\"\"Crop image according to the supplied bounding box.\"\"\"\n # Upper left point\n ul = np.array(transform([1, 1], center, scale, res, invert=1))-1",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "crop",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def crop(img, center, scale, res, rot=0):\n \"\"\"Crop image according to the supplied bounding box.\"\"\"\n # Upper left point\n ul = np.array(transform([1, 1], center, scale, res, invert=1))-1\n # Bottom right point\n br = np.array(transform([res[0]+1, \n res[1]+1], center, scale, res, invert=1))-1\n # Padding so that when rotated proper amount of context is included\n pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2)\n if not rot == 0:",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "uncrop",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def uncrop(img, center, scale, orig_shape, rot=0, is_rgb=True):\n \"\"\"'Undo' the image cropping/resizing.\n This function is used when evaluating mask/part segmentation.\n \"\"\"\n res = img.shape[:2]\n # Upper left point\n ul = np.array(transform([1, 1], center, scale, res, invert=1))-1\n # Bottom right point\n br = np.array(transform([res[0]+1,res[1]+1], center, scale, res, invert=1))-1\n # size of cropped image",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "rot_aa",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def rot_aa(aa, rot):\n \"\"\"Rotate axis angle parameters.\"\"\"\n # pose parameters\n R = np.array([[np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0],\n [np.sin(np.deg2rad(-rot)), np.cos(np.deg2rad(-rot)), 0],\n [0, 0, 1]])\n # find the rotation of the body in camera frame\n per_rdg, _ = cv2.Rodrigues(aa)\n # apply the global rotation to the global orientation\n resrot, _ = cv2.Rodrigues(np.dot(R,per_rdg))",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "flip_img",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def flip_img(img):\n \"\"\"Flip rgb images or masks.\n channels come last, e.g. (256,256,3).\n \"\"\"\n img = np.fliplr(img)\n return img\ndef flip_kp(kp, is_smpl=False, type='body'):\n \"\"\"Flip keypoints.\"\"\"\n assert type in ['body', 'hand', 'face', 'feet']\n if type == 'body':",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "flip_kp",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def flip_kp(kp, is_smpl=False, type='body'):\n \"\"\"Flip keypoints.\"\"\"\n assert type in ['body', 'hand', 'face', 'feet']\n if type == 'body':\n if len(kp) == 24:\n if is_smpl:\n flipped_parts = constants.SMPL_JOINTS_FLIP_PERM\n else:\n flipped_parts = constants.J24_FLIP_PERM\n elif len(kp) == 49:",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "flip_pose",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def flip_pose(pose):\n \"\"\"Flip pose.\n The flipping is based on SMPL parameters.\n \"\"\"\n flipped_parts = constants.SMPL_POSE_FLIP_PERM\n pose = pose[flipped_parts]\n # we also negate the second and the third dimension of the axis-angle\n pose[1::3] = -pose[1::3]\n pose[2::3] = -pose[2::3]\n return pose",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "flip_aa",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def flip_aa(pose):\n \"\"\"Flip aa.\n \"\"\"\n # we also negate the second and the third dimension of the axis-angle\n if len(pose.shape) == 1:\n pose[1::3] = -pose[1::3]\n pose[2::3] = -pose[2::3]\n elif len(pose.shape) == 2:\n pose[:, 1::3] = -pose[:, 1::3]\n pose[:, 2::3] = -pose[:, 2::3]",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "normalize_2d_kp",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def normalize_2d_kp(kp_2d, crop_size=224, inv=False):\n # Normalize keypoints between -1, 1\n if not inv:\n ratio = 1.0 / crop_size\n kp_2d = 2.0 * kp_2d * ratio - 1.0\n else:\n ratio = 1.0 / crop_size\n kp_2d = (kp_2d + 1.0)/(2*ratio)\n return kp_2d\ndef j2d_processing(kp, transf):",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "j2d_processing",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def j2d_processing(kp, transf):\n \"\"\"Process gt 2D keypoints and apply transforms.\"\"\"\n # nparts = kp.shape[1]\n bs, npart = kp.shape[:2]\n kp_pad = torch.cat([kp, torch.ones((bs, npart, 1)).to(kp)], dim=-1)\n kp_new = torch.bmm(transf, kp_pad.transpose(1, 2))\n kp_new = kp_new.transpose(1, 2)\n kp_new[:, :, :-1] = 2.*kp_new[:, :, :-1] / constants.IMG_RES - 1.\n return kp_new[:, :, :2]\ndef generate_heatmap(joints, heatmap_size, sigma=1, joints_vis=None):",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "generate_heatmap",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.imutils",
+ "description": "modules.PyMAF.utils.imutils",
+ "peekOfCode": "def generate_heatmap(joints, heatmap_size, sigma=1, joints_vis=None):\n '''\n param joints: [num_joints, 3]\n param joints_vis: [num_joints, 3]\n return: target, target_weight(1: visible, 0: invisible)\n '''\n num_joints = joints.shape[0]\n device = joints.device\n cur_device = torch.device(device.type, device.index)\n if not hasattr(heatmap_size, '__len__'):",
+ "detail": "modules.PyMAF.utils.imutils",
+ "documentation": {}
+ },
+ {
+ "label": "save_object",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.io",
+ "description": "modules.PyMAF.utils.io",
+ "peekOfCode": "def save_object(obj, file_name):\n \"\"\"Save a Python object by pickling it.\"\"\"\n file_name = os.path.abspath(file_name)\n with open(file_name, 'wb') as f:\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\ndef cache_url(url_or_file, cache_dir):\n \"\"\"Download the file specified by the URL to the cache_dir and return the\n path to the cached file. If the argument is not a URL, simply return it as\n is.\n \"\"\"",
+ "detail": "modules.PyMAF.utils.io",
+ "documentation": {}
+ },
+ {
+ "label": "cache_url",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.io",
+ "description": "modules.PyMAF.utils.io",
+ "peekOfCode": "def cache_url(url_or_file, cache_dir):\n \"\"\"Download the file specified by the URL to the cache_dir and return the\n path to the cached file. If the argument is not a URL, simply return it as\n is.\n \"\"\"\n is_url = re.match(r'^(?:http)s?://', url_or_file, re.IGNORECASE) is not None\n if not is_url:\n return url_or_file\n url = url_or_file\n # assert url.startswith(_DETECTRON_S3_BASE_URL), \\",
+ "detail": "modules.PyMAF.utils.io",
+ "documentation": {}
+ },
+ {
+ "label": "assert_cache_file_is_ok",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.io",
+ "description": "modules.PyMAF.utils.io",
+ "peekOfCode": "def assert_cache_file_is_ok(url, file_path):\n \"\"\"Check that cache file has the correct hash.\"\"\"\n # File is already in the cache, verify that the md5sum matches and\n # return local path\n cache_file_md5sum = _get_file_md5sum(file_path)\n ref_md5sum = _get_reference_md5sum(url)\n assert cache_file_md5sum == ref_md5sum, \\\n ('Target URL {} appears to be downloaded to the local cache file '\n '{}, but the md5 hash of the local file does not match the '\n 'reference (actual: {} vs. expected: {}). You may wish to delete '",
+ "detail": "modules.PyMAF.utils.io",
+ "documentation": {}
+ },
+ {
+ "label": "download_url",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.io",
+ "description": "modules.PyMAF.utils.io",
+ "peekOfCode": "def download_url(\n url, dst_file_path, chunk_size=8192, progress_hook=_progress_bar\n):\n \"\"\"Download url and write it to dst_file_path.\n Credit:\n https://stackoverflow.com/questions/2028517/python-urllib2-progress-hook\n \"\"\"\n response = urlopen(url)\n total_size = response.info().getheader('Content-Length').strip()\n total_size = int(total_size)",
+ "detail": "modules.PyMAF.utils.io",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.io",
+ "description": "modules.PyMAF.utils.io",
+ "peekOfCode": "logger = logging.getLogger(__name__)\n_DETECTRON_S3_BASE_URL = 'https://s3-us-west-2.amazonaws.com/detectron'\ndef save_object(obj, file_name):\n \"\"\"Save a Python object by pickling it.\"\"\"\n file_name = os.path.abspath(file_name)\n with open(file_name, 'wb') as f:\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\ndef cache_url(url_or_file, cache_dir):\n \"\"\"Download the file specified by the URL to the cache_dir and return the\n path to the cached file. If the argument is not a URL, simply return it as",
+ "detail": "modules.PyMAF.utils.io",
+ "documentation": {}
+ },
+ {
+ "label": "_DETECTRON_S3_BASE_URL",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.io",
+ "description": "modules.PyMAF.utils.io",
+ "peekOfCode": "_DETECTRON_S3_BASE_URL = 'https://s3-us-west-2.amazonaws.com/detectron'\ndef save_object(obj, file_name):\n \"\"\"Save a Python object by pickling it.\"\"\"\n file_name = os.path.abspath(file_name)\n with open(file_name, 'wb') as f:\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\ndef cache_url(url_or_file, cache_dir):\n \"\"\"Download the file specified by the URL to the cache_dir and return the\n path to the cached file. If the argument is not a URL, simply return it as\n is.",
+ "detail": "modules.PyMAF.utils.io",
+ "documentation": {}
+ },
+ {
+ "label": "iuvmap_clean",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.iuvmap",
+ "description": "modules.PyMAF.utils.iuvmap",
+ "peekOfCode": "def iuvmap_clean(U_uv, V_uv, Index_UV, AnnIndex=None):\n Index_UV_max = torch.argmax(Index_UV, dim=1).float()\n recon_Index_UV = []\n for i in range(Index_UV.size(1)):\n if i == 0:\n recon_Index_UV_i = torch.min(F.threshold(Index_UV_max + 1, 0.5, 0),\n -F.threshold(-Index_UV_max - 1, -1.5, 0))\n else:\n recon_Index_UV_i = torch.min(F.threshold(Index_UV_max, i - 0.5, 0),\n -F.threshold(-Index_UV_max, -i - 0.5, 0)) / float(i)",
+ "detail": "modules.PyMAF.utils.iuvmap",
+ "documentation": {}
+ },
+ {
+ "label": "iuv_map2img",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.iuvmap",
+ "description": "modules.PyMAF.utils.iuvmap",
+ "peekOfCode": "def iuv_map2img(U_uv, V_uv, Index_UV, AnnIndex=None, uv_rois=None, ind_mapping=None, n_part=24):\n device_id = U_uv.get_device()\n batch_size = U_uv.size(0)\n K = U_uv.size(1)\n heatmap_size = U_uv.size(2)\n Index_UV_max = torch.argmax(Index_UV, dim=1)\n if AnnIndex is None:\n Index_UV_max = Index_UV_max.to(torch.int64)\n else:\n AnnIndex_max = torch.argmax(AnnIndex, dim=1)",
+ "detail": "modules.PyMAF.utils.iuvmap",
+ "documentation": {}
+ },
+ {
+ "label": "iuv_img2map",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.iuvmap",
+ "description": "modules.PyMAF.utils.iuvmap",
+ "peekOfCode": "def iuv_img2map(uvimages, uv_rois=None, new_size=None, n_part=24):\n device_id = uvimages.get_device()\n batch_size = uvimages.size(0)\n uvimg_size = uvimages.size(-1)\n Index2mask = [[0], [1, 2], [3], [4], [5], [6], [7, 9], [8, 10], [11, 13], [12, 14], [15, 17], [16, 18], [19, 21],\n [20, 22], [23, 24]]\n part_ind = torch.round(uvimages[:, 0, :, :] * n_part)\n part_u = uvimages[:, 1, :, :]\n part_v = uvimages[:, 2, :, :]\n recon_U = []",
+ "detail": "modules.PyMAF.utils.iuvmap",
+ "documentation": {}
+ },
+ {
+ "label": "seg_img2map",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.iuvmap",
+ "description": "modules.PyMAF.utils.iuvmap",
+ "peekOfCode": "def seg_img2map(segimages, uv_rois=None, new_size=None, n_part=24):\n device_id = segimages.get_device()\n batch_size = segimages.size(0)\n uvimg_size = segimages.size(-1)\n part_ind = torch.round(segimages[:, 0, :, :] * n_part)\n recon_Index_UV = []\n for i in range(n_part+1):\n if i == 0:\n recon_Index_UV_i = torch.min(F.threshold(part_ind + 1, 0.5, 0), -F.threshold(-part_ind - 1, -1.5, 0))\n else:",
+ "detail": "modules.PyMAF.utils.iuvmap",
+ "documentation": {}
+ },
+ {
+ "label": "get_keypoints",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def get_keypoints():\n \"\"\"Get the COCO keypoints and their left/right flip coorespondence map.\"\"\"\n # Keypoints are not available in the COCO json for the test split, so we\n # provide them here.\n keypoints = [\n 'nose',\n 'left_eye',\n 'right_eye',\n 'left_ear',\n 'right_ear',",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "get_person_class_index",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def get_person_class_index():\n \"\"\"Index of the person class in COCO.\"\"\"\n return 1\ndef flip_keypoints(keypoints, keypoint_flip_map, keypoint_coords, width):\n \"\"\"Left/right flip keypoint_coords. keypoints and keypoint_flip_map are\n accessible from get_keypoints().\n \"\"\"\n flipped_kps = keypoint_coords.copy()\n for lkp, rkp in keypoint_flip_map.items():\n lid = keypoints.index(lkp)",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "flip_keypoints",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def flip_keypoints(keypoints, keypoint_flip_map, keypoint_coords, width):\n \"\"\"Left/right flip keypoint_coords. keypoints and keypoint_flip_map are\n accessible from get_keypoints().\n \"\"\"\n flipped_kps = keypoint_coords.copy()\n for lkp, rkp in keypoint_flip_map.items():\n lid = keypoints.index(lkp)\n rid = keypoints.index(rkp)\n flipped_kps[:, :, lid] = keypoint_coords[:, :, rid]\n flipped_kps[:, :, rid] = keypoint_coords[:, :, lid]",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "flip_heatmaps",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def flip_heatmaps(heatmaps):\n \"\"\"Flip heatmaps horizontally.\"\"\"\n keypoints, flip_map = get_keypoints()\n heatmaps_flipped = heatmaps.copy()\n for lkp, rkp in flip_map.items():\n lid = keypoints.index(lkp)\n rid = keypoints.index(rkp)\n heatmaps_flipped[:, rid, :, :] = heatmaps[:, lid, :, :]\n heatmaps_flipped[:, lid, :, :] = heatmaps[:, rid, :, :]\n heatmaps_flipped = heatmaps_flipped[:, :, :, ::-1]",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "heatmaps_to_keypoints",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def heatmaps_to_keypoints(maps, rois):\n \"\"\"Extract predicted keypoint locations from heatmaps. Output has shape\n (#rois, 4, #keypoints) with the 4 rows corresponding to (x, y, logit, prob)\n for each keypoint.\n \"\"\"\n # This function converts a discrete image coordinate in a HEATMAP_SIZE x\n # HEATMAP_SIZE image to a continuous keypoint coordinate. We maintain\n # consistency with keypoints_to_heatmap_labels by using the conversion from\n # Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a\n # continuous coordinate.",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "keypoints_to_heatmap_labels",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def keypoints_to_heatmap_labels(keypoints, rois):\n \"\"\"Encode keypoint location in the target heatmap for use in\n SoftmaxWithLoss.\n \"\"\"\n # Maps keypoints from the half-open interval [x1, x2) on continuous image\n # coordinates to the closed interval [0, HEATMAP_SIZE - 1] on discrete image\n # coordinates. We use the continuous <-> discrete conversion from Heckbert\n # 1990 (\"What is the coordinate of a pixel?\"): d = floor(c) and c = d + 0.5,\n # where d is a discrete coordinate and c is a continuous coordinate.\n assert keypoints.shape[2] == cfg.KRCNN.NUM_KEYPOINTS",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "scores_to_probs",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def scores_to_probs(scores):\n \"\"\"Transforms CxHxW of scores to probabilities spatially.\"\"\"\n channels = scores.shape[0]\n for c in range(channels):\n temp = scores[c, :, :]\n max_score = temp.max()\n temp = np.exp(temp - max_score) / np.sum(np.exp(temp - max_score))\n scores[c, :, :] = temp\n return scores\ndef nms_oks(kp_predictions, rois, thresh):",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "nms_oks",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def nms_oks(kp_predictions, rois, thresh):\n \"\"\"Nms based on kp predictions.\"\"\"\n scores = np.mean(kp_predictions[:, 2, :], axis=1)\n order = scores.argsort()[::-1]\n keep = []\n while order.size > 0:\n i = order[0]\n keep.append(i)\n ovr = compute_oks(\n kp_predictions[i], rois[i], kp_predictions[order[1:]],",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "compute_oks",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def compute_oks(src_keypoints, src_roi, dst_keypoints, dst_roi):\n \"\"\"Compute OKS for predicted keypoints wrt gt_keypoints.\n src_keypoints: 4xK\n src_roi: 4x1\n dst_keypoints: Nx4xK\n dst_roi: Nx4\n \"\"\"\n sigmas = np.array([\n .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87,\n .87, .89, .89]) / 10.0",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "get_max_preds",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def get_max_preds(batch_heatmaps):\n '''\n get predictions from score maps\n heatmaps: numpy.ndarray([batch_size, num_joints, height, width])\n '''\n assert isinstance(batch_heatmaps, np.ndarray), \\\n 'batch_heatmaps should be numpy.ndarray'\n assert batch_heatmaps.ndim == 4, 'batch_images should be 4-ndim'\n batch_size = batch_heatmaps.shape[0]\n num_joints = batch_heatmaps.shape[1]",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "generate_3d_integral_preds_tensor",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def generate_3d_integral_preds_tensor(heatmaps, num_joints, x_dim, y_dim, z_dim):\n assert isinstance(heatmaps, torch.Tensor)\n if z_dim is not None:\n heatmaps = heatmaps.reshape((heatmaps.shape[0], num_joints, z_dim, y_dim, x_dim))\n accu_x = heatmaps.sum(dim=2)\n accu_x = accu_x.sum(dim=2)\n accu_y = heatmaps.sum(dim=2)\n accu_y = accu_y.sum(dim=3)\n accu_z = heatmaps.sum(dim=3)\n accu_z = accu_z.sum(dim=3)",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "softmax_integral_tensor",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.keypoints",
+ "description": "modules.PyMAF.utils.keypoints",
+ "peekOfCode": "def softmax_integral_tensor(preds, num_joints, hm_width, hm_height, hm_depth=None):\n # global soft max\n preds = preds.reshape((preds.shape[0], num_joints, -1))\n preds = F.softmax(preds, 2)\n output_3d = False if hm_depth is None else True\n # integrate heatmap into joint location\n if output_3d:\n x, y, z = generate_3d_integral_preds_tensor(preds, num_joints, hm_width, hm_height, hm_depth)\n # x = x / float(hm_width) - 0.5\n # y = y / float(hm_height) - 0.5",
+ "detail": "modules.PyMAF.utils.keypoints",
+ "documentation": {}
+ },
+ {
+ "label": "Generator3D",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.mesh_generation",
+ "description": "modules.PyMAF.utils.mesh_generation",
+ "peekOfCode": "class Generator3D(object):\n ''' Generator class for DVRs.\n It provides functions to generate the final mesh as well refining options.\n Args:\n model (nn.Module): trained DVR model\n points_batch_size (int): batch size for points evaluation\n threshold (float): threshold value\n refinement_step (int): number of refinement steps\n device (device): pytorch device\n resolution0 (int): start resolution for MISE",
+ "detail": "modules.PyMAF.utils.mesh_generation",
+ "documentation": {}
+ },
+ {
+ "label": "PartRenderer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.part_utils",
+ "description": "modules.PyMAF.utils.part_utils",
+ "peekOfCode": "class PartRenderer():\n \"\"\"Renderer used to render segmentation masks and part segmentations.\n Internally it uses the Neural 3D Mesh Renderer\n \"\"\"\n def __init__(self, focal_length=5000., render_res=224):\n # Parameters for rendering\n self.focal_length = focal_length\n self.render_res = render_res\n # We use Neural 3D mesh renderer for rendering masks and part segmentations\n self.neural_renderer = nr.Renderer(dist_coeffs=None, orig_size=self.render_res,",
+ "detail": "modules.PyMAF.utils.part_utils",
+ "documentation": {}
+ },
+ {
+ "label": "run_openpose",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.pose_tracker",
+ "description": "modules.PyMAF.utils.pose_tracker",
+ "peekOfCode": "def run_openpose(\n video_file,\n output_folder,\n staf_folder,\n vis=False,\n):\n pwd = os.getcwd()\n os.chdir(staf_folder)\n render = 1 if vis else 0\n display = 2 if vis else 0",
+ "detail": "modules.PyMAF.utils.pose_tracker",
+ "documentation": {}
+ },
+ {
+ "label": "read_posetrack_keypoints",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.pose_tracker",
+ "description": "modules.PyMAF.utils.pose_tracker",
+ "peekOfCode": "def read_posetrack_keypoints(output_folder):\n people = dict()\n for idx, result_file in enumerate(sorted(os.listdir(output_folder))):\n json_file = osp.join(output_folder, result_file)\n data = json.load(open(json_file))\n # print(idx, data)\n for person in data['people']:\n person_id = person['person_id'][0]\n joints2d = person['pose_keypoints_2d']\n if person_id in people.keys():",
+ "detail": "modules.PyMAF.utils.pose_tracker",
+ "documentation": {}
+ },
+ {
+ "label": "run_posetracker",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.pose_tracker",
+ "description": "modules.PyMAF.utils.pose_tracker",
+ "peekOfCode": "def run_posetracker(video_file, staf_folder, posetrack_output_folder='/tmp', display=False):\n posetrack_output_folder = os.path.join(\n posetrack_output_folder,\n f'{os.path.basename(video_file)}_posetrack'\n )\n # run posetrack on video\n run_openpose(\n video_file,\n posetrack_output_folder,\n vis=display,",
+ "detail": "modules.PyMAF.utils.pose_tracker",
+ "documentation": {}
+ },
+ {
+ "label": "compute_similarity_transform",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.pose_utils",
+ "description": "modules.PyMAF.utils.pose_utils",
+ "peekOfCode": "def compute_similarity_transform(S1, S2):\n \"\"\"\n Computes a similarity transform (sR, t) that takes\n a set of 3D points S1 (3 x N) closest to a set of 3D points S2,\n where R is an 3x3 rotation matrix, t 3x1 translation, s scale.\n i.e. solves the orthogonal Procrutes problem.\n \"\"\"\n transposed = False\n if S1.shape[0] != 3 and S1.shape[0] != 2:\n S1 = S1.T",
+ "detail": "modules.PyMAF.utils.pose_utils",
+ "documentation": {}
+ },
+ {
+ "label": "compute_similarity_transform_batch",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.pose_utils",
+ "description": "modules.PyMAF.utils.pose_utils",
+ "peekOfCode": "def compute_similarity_transform_batch(S1, S2):\n \"\"\"Batched version of compute_similarity_transform.\"\"\"\n S1_hat = np.zeros_like(S1)\n for i in range(S1.shape[0]):\n S1_hat[i] = compute_similarity_transform(S1[i], S2[i])\n return S1_hat\ndef reconstruction_error(S1, S2, reduction='mean'):\n \"\"\"Do Procrustes alignment and compute reconstruction error.\"\"\"\n S1_hat = compute_similarity_transform_batch(S1, S2)\n re = np.sqrt( ((S1_hat - S2)** 2).sum(axis=-1)).mean(axis=-1)",
+ "detail": "modules.PyMAF.utils.pose_utils",
+ "documentation": {}
+ },
+ {
+ "label": "reconstruction_error",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.pose_utils",
+ "description": "modules.PyMAF.utils.pose_utils",
+ "peekOfCode": "def reconstruction_error(S1, S2, reduction='mean'):\n \"\"\"Do Procrustes alignment and compute reconstruction error.\"\"\"\n S1_hat = compute_similarity_transform_batch(S1, S2)\n re = np.sqrt( ((S1_hat - S2)** 2).sum(axis=-1)).mean(axis=-1)\n if reduction == 'mean':\n re = re.mean()\n elif reduction == 'sum':\n re = re.sum()\n return re, S1_hat\n# https://math.stackexchange.com/questions/382760/composition-of-two-axis-angle-rotations",
+ "detail": "modules.PyMAF.utils.pose_utils",
+ "documentation": {}
+ },
+ {
+ "label": "axis_angle_add",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.pose_utils",
+ "description": "modules.PyMAF.utils.pose_utils",
+ "peekOfCode": "def axis_angle_add(theta, roll_axis, alpha):\n \"\"\"Composition of two axis-angle rotations (PyTorch version)\n Args:\n theta: N x 3\n roll_axis: N x 3\n alph: N x 1\n Returns:\n equivalent axis-angle of the composition\n \"\"\"\n alpha = alpha / 2.",
+ "detail": "modules.PyMAF.utils.pose_utils",
+ "documentation": {}
+ },
+ {
+ "label": "axis_angle_add_np",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.pose_utils",
+ "description": "modules.PyMAF.utils.pose_utils",
+ "peekOfCode": "def axis_angle_add_np(theta, roll_axis, alpha):\n \"\"\"Composition of two axis-angle rotations (NumPy version)\n Args:\n theta: N x 3\n roll_axis: N x 3\n alph: N x 1\n Returns:\n equivalent axis-angle of the composition\n \"\"\"\n alpha = alpha / 2.",
+ "detail": "modules.PyMAF.utils.pose_utils",
+ "documentation": {}
+ },
+ {
+ "label": "WeakPerspectiveCamera",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.renderer",
+ "description": "modules.PyMAF.utils.renderer",
+ "peekOfCode": "class WeakPerspectiveCamera(pyrender.Camera):\n def __init__(self,\n scale,\n translation,\n znear=pyrender.camera.DEFAULT_Z_NEAR,\n zfar=None,\n name=None):\n super(WeakPerspectiveCamera, self).__init__(\n znear=znear,\n zfar=zfar,",
+ "detail": "modules.PyMAF.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "PyRenderer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.renderer",
+ "description": "modules.PyMAF.utils.renderer",
+ "peekOfCode": "class PyRenderer:\n def __init__(self, resolution=(224,224), orig_img=False, wireframe=False, scale_ratio=1., vis_ratio=1.):\n self.resolution = (resolution[0] * scale_ratio, resolution[1] * scale_ratio)\n # self.scale_ratio = scale_ratio\n self.faces = {'smplx': get_model_faces('smplx'),\n 'smpl': get_model_faces('smpl'),\n # 'mano': get_model_faces('mano'),\n # 'flame': get_model_faces('flame'),\n }\n self.orig_img = orig_img",
+ "detail": "modules.PyMAF.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "OpenDRenderer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.renderer",
+ "description": "modules.PyMAF.utils.renderer",
+ "peekOfCode": "class OpenDRenderer:\n def __init__(self, resolution=(224, 224), ratio=1):\n self.resolution = (resolution[0] * ratio, resolution[1] * ratio)\n self.ratio = ratio\n self.focal_length = 5000.\n self.K = np.array([[self.focal_length, 0., self.resolution[1] / 2.],\n [0., self.focal_length, self.resolution[0] / 2.],\n [0., 0., 1.]])\n self.colors_dict = {\n 'red': np.array([0.5, 0.2, 0.2]),",
+ "detail": "modules.PyMAF.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "IUV_Renderer",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.renderer",
+ "description": "modules.PyMAF.utils.renderer",
+ "peekOfCode": "class IUV_Renderer(object):\n def __init__(self, focal_length=5000., orig_size=224, output_size=56, mode='iuv', device=torch.device('cuda'), mesh_type='smpl'):\n self.focal_length = focal_length\n self.orig_size = orig_size\n self.output_size = output_size\n if mode in ['iuv']:\n if mesh_type == 'smpl':\n DP = DensePoseMethods()\n vert_mapping = DP.All_vertices.astype('int64') - 1\n self.vert_mapping = torch.from_numpy(vert_mapping)",
+ "detail": "modules.PyMAF.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "rotateY",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.renderer",
+ "description": "modules.PyMAF.utils.renderer",
+ "peekOfCode": "def rotateY(points, angle):\n \"\"\"Rotate all points in a 2D array around the y axis.\"\"\"\n ry = np.array([\n [np.cos(angle), 0., np.sin(angle)],\n [0., 1., 0. ],\n [-np.sin(angle), 0., np.cos(angle)]\n ])\n return np.dot(points, ry)\ndef rotateX( points, angle ):\n \"\"\"Rotate all points in a 2D array around the x axis.\"\"\"",
+ "detail": "modules.PyMAF.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "rotateX",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.renderer",
+ "description": "modules.PyMAF.utils.renderer",
+ "peekOfCode": "def rotateX( points, angle ):\n \"\"\"Rotate all points in a 2D array around the x axis.\"\"\"\n rx = np.array([\n [1., 0., 0. ],\n [0., np.cos(angle), -np.sin(angle)],\n [0., np.sin(angle), np.cos(angle) ]\n ])\n return np.dot(points, rx)\ndef rotateZ( points, angle ):\n \"\"\"Rotate all points in a 2D array around the z axis.\"\"\"",
+ "detail": "modules.PyMAF.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "rotateZ",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.renderer",
+ "description": "modules.PyMAF.utils.renderer",
+ "peekOfCode": "def rotateZ( points, angle ):\n \"\"\"Rotate all points in a 2D array around the z axis.\"\"\"\n rz = np.array([\n [np.cos(angle), -np.sin(angle), 0. ],\n [np.sin(angle), np.cos(angle), 0. ],\n [0., 0., 1. ]\n ])\n return np.dot(points, rz)\nclass IUV_Renderer(object):\n def __init__(self, focal_length=5000., orig_size=224, output_size=56, mode='iuv', device=torch.device('cuda'), mesh_type='smpl'):",
+ "detail": "modules.PyMAF.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.renderer",
+ "description": "modules.PyMAF.utils.renderer",
+ "peekOfCode": "logger = logging.getLogger(__name__)\nclass WeakPerspectiveCamera(pyrender.Camera):\n def __init__(self,\n scale,\n translation,\n znear=pyrender.camera.DEFAULT_Z_NEAR,\n zfar=None,\n name=None):\n super(WeakPerspectiveCamera, self).__init__(\n znear=znear,",
+ "detail": "modules.PyMAF.utils.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "get_occ_gt",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.sample_mesh",
+ "description": "modules.PyMAF.utils.sample_mesh",
+ "peekOfCode": "def get_occ_gt(in_path=None, vertices=None, faces=None, pts_num=1000, points_sigma=0.01, with_dp=False, points=None, extra_points=None):\n if in_path is not None:\n mesh = trimesh.load(in_path, process=False)\n print(type(mesh.vertices), mesh.vertices.shape, mesh.faces.shape)\n mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False)\n # print('get_occ_gt', type(mesh.vertices), mesh.vertices.shape, mesh.faces.shape)\n # points_size = 100000\n points_padding = 0.1\n # points_sigma = 0.01\n points_uniform_ratio = 0.5",
+ "detail": "modules.PyMAF.utils.sample_mesh",
+ "documentation": {}
+ },
+ {
+ "label": "CheckpointSaver",
+ "kind": 6,
+ "importPath": "modules.PyMAF.utils.saver",
+ "description": "modules.PyMAF.utils.saver",
+ "peekOfCode": "class CheckpointSaver():\n \"\"\"Class that handles saving and loading checkpoints during training.\"\"\"\n def __init__(self, save_dir, save_steps=1000, overwrite=False):\n self.save_dir = os.path.abspath(save_dir)\n self.save_steps = save_steps\n self.overwrite = overwrite\n if not os.path.exists(self.save_dir):\n os.makedirs(self.save_dir)\n self.get_latest_checkpoint()\n return",
+ "detail": "modules.PyMAF.utils.saver",
+ "documentation": {}
+ },
+ {
+ "label": "logger",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.saver",
+ "description": "modules.PyMAF.utils.saver",
+ "peekOfCode": "logger = logging.getLogger(__name__)\nclass CheckpointSaver():\n \"\"\"Class that handles saving and loading checkpoints during training.\"\"\"\n def __init__(self, save_dir, save_steps=1000, overwrite=False):\n self.save_dir = os.path.abspath(save_dir)\n self.save_steps = save_steps\n self.overwrite = overwrite\n if not os.path.exists(self.save_dir):\n os.makedirs(self.save_dir)\n self.get_latest_checkpoint()",
+ "detail": "modules.PyMAF.utils.saver",
+ "documentation": {}
+ },
+ {
+ "label": "GetDensePoseMask",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.segms",
+ "description": "modules.PyMAF.utils.segms",
+ "peekOfCode": "def GetDensePoseMask(Polys):\n MaskGen = np.zeros([256, 256])\n for i in range(1, 15):\n if (Polys[i - 1]):\n current_mask = mask_util.decode(Polys[i - 1])\n MaskGen[current_mask > 0] = i\n return MaskGen\ndef flip_segms(segms, height, width):\n \"\"\"Left/right flip each mask in a list of masks.\"\"\"\n def _flip_poly(poly, width):",
+ "detail": "modules.PyMAF.utils.segms",
+ "documentation": {}
+ },
+ {
+ "label": "flip_segms",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.segms",
+ "description": "modules.PyMAF.utils.segms",
+ "peekOfCode": "def flip_segms(segms, height, width):\n \"\"\"Left/right flip each mask in a list of masks.\"\"\"\n def _flip_poly(poly, width):\n flipped_poly = np.array(poly)\n flipped_poly[0::2] = width - np.array(poly[0::2]) - 1\n return flipped_poly.tolist()\n def _flip_rle(rle, height, width):\n if 'counts' in rle and type(rle['counts']) == list:\n # Magic RLE format handling painfully discovered by looking at the\n # COCO API showAnns function.",
+ "detail": "modules.PyMAF.utils.segms",
+ "documentation": {}
+ },
+ {
+ "label": "polys_to_mask",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.segms",
+ "description": "modules.PyMAF.utils.segms",
+ "peekOfCode": "def polys_to_mask(polygons, height, width):\n \"\"\"Convert from the COCO polygon segmentation format to a binary mask\n encoded as a 2D array of data type numpy.float32. The polygon segmentation\n is understood to be enclosed inside a height x width image. The resulting\n mask is therefore of shape (height, width).\n \"\"\"\n rle = mask_util.frPyObjects(polygons, height, width)\n mask = np.array(mask_util.decode(rle), dtype=np.float32)\n # Flatten in case polygons was a list\n mask = np.sum(mask, axis=2)",
+ "detail": "modules.PyMAF.utils.segms",
+ "documentation": {}
+ },
+ {
+ "label": "mask_to_bbox",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.segms",
+ "description": "modules.PyMAF.utils.segms",
+ "peekOfCode": "def mask_to_bbox(mask):\n \"\"\"Compute the tight bounding box of a binary mask.\"\"\"\n xs = np.where(np.sum(mask, axis=0) > 0)[0]\n ys = np.where(np.sum(mask, axis=1) > 0)[0]\n if len(xs) == 0 or len(ys) == 0:\n return None\n x0 = xs[0]\n x1 = xs[-1]\n y0 = ys[0]\n y1 = ys[-1]",
+ "detail": "modules.PyMAF.utils.segms",
+ "documentation": {}
+ },
+ {
+ "label": "polys_to_mask_wrt_box",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.segms",
+ "description": "modules.PyMAF.utils.segms",
+ "peekOfCode": "def polys_to_mask_wrt_box(polygons, box, M):\n \"\"\"Convert from the COCO polygon segmentation format to a binary mask\n encoded as a 2D array of data type numpy.float32. The polygon segmentation\n is understood to be enclosed in the given box and rasterized to an M x M\n mask. The resulting mask is therefore of shape (M, M).\n \"\"\"\n w = box[2] - box[0]\n h = box[3] - box[1]\n w = np.maximum(w, 1)\n h = np.maximum(h, 1)",
+ "detail": "modules.PyMAF.utils.segms",
+ "documentation": {}
+ },
+ {
+ "label": "polys_to_boxes",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.segms",
+ "description": "modules.PyMAF.utils.segms",
+ "peekOfCode": "def polys_to_boxes(polys):\n \"\"\"Convert a list of polygons into an array of tight bounding boxes.\"\"\"\n boxes_from_polys = np.zeros((len(polys), 4), dtype=np.float32)\n for i in range(len(polys)):\n poly = polys[i]\n x0 = min(min(p[::2]) for p in poly)\n x1 = max(max(p[::2]) for p in poly)\n y0 = min(min(p[1::2]) for p in poly)\n y1 = max(max(p[1::2]) for p in poly)\n boxes_from_polys[i, :] = [x0, y0, x1, y1]",
+ "detail": "modules.PyMAF.utils.segms",
+ "documentation": {}
+ },
+ {
+ "label": "rle_mask_voting",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.segms",
+ "description": "modules.PyMAF.utils.segms",
+ "peekOfCode": "def rle_mask_voting(top_masks,\n all_masks,\n all_dets,\n iou_thresh,\n binarize_thresh,\n method='AVG'):\n \"\"\"Returns new masks (in correspondence with `top_masks`) by combining\n multiple overlapping masks coming from the pool of `all_masks`. Two methods\n for combining masks are supported: 'AVG' uses a weighted average of\n overlapping mask pixels; 'UNION' takes the union of all mask pixels.",
+ "detail": "modules.PyMAF.utils.segms",
+ "documentation": {}
+ },
+ {
+ "label": "rle_mask_nms",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.segms",
+ "description": "modules.PyMAF.utils.segms",
+ "peekOfCode": "def rle_mask_nms(masks, dets, thresh, mode='IOU'):\n \"\"\"Performs greedy non-maximum suppression based on an overlap measurement\n between masks. The type of measurement is determined by `mode` and can be\n either 'IOU' (standard intersection over union) or 'IOMA' (intersection over\n mininum area).\n \"\"\"\n if len(masks) == 0:\n return []\n if len(masks) == 1:\n return [0]",
+ "detail": "modules.PyMAF.utils.segms",
+ "documentation": {}
+ },
+ {
+ "label": "rle_masks_to_boxes",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.segms",
+ "description": "modules.PyMAF.utils.segms",
+ "peekOfCode": "def rle_masks_to_boxes(masks):\n \"\"\"Computes the bounding box of each mask in a list of RLE encoded masks.\"\"\"\n if len(masks) == 0:\n return []\n decoded_masks = [\n np.array(mask_util.decode(rle), dtype=np.float32) for rle in masks\n ]\n def get_bounds(flat_mask):\n inds = np.where(flat_mask > 0)[0]\n return inds.min(), inds.max()",
+ "detail": "modules.PyMAF.utils.segms",
+ "documentation": {}
+ },
+ {
+ "label": "get_smooth_bbox_params",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.smooth_bbox",
+ "description": "modules.PyMAF.utils.smooth_bbox",
+ "peekOfCode": "def get_smooth_bbox_params(kps, vis_thresh=2, kernel_size=11, sigma=3):\n \"\"\"\n Computes smooth bounding box parameters from keypoints:\n 1. Computes bbox by rescaling the person to be around 150 px.\n 2. Linearly interpolates bbox params for missing annotations.\n 3. Median filtering\n 4. Gaussian filtering.\n Recommended thresholds:\n * detect-and-track: 0\n * 3DPW: 0.1",
+ "detail": "modules.PyMAF.utils.smooth_bbox",
+ "documentation": {}
+ },
+ {
+ "label": "kp_to_bbox_param",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.smooth_bbox",
+ "description": "modules.PyMAF.utils.smooth_bbox",
+ "peekOfCode": "def kp_to_bbox_param(kp, vis_thresh):\n \"\"\"\n Finds the bounding box parameters from the 2D keypoints.\n Args:\n kp (Kx3): 2D Keypoints.\n vis_thresh (float): Threshold for visibility.\n Returns:\n [center_x, center_y, scale]\n \"\"\"\n if kp is None:",
+ "detail": "modules.PyMAF.utils.smooth_bbox",
+ "documentation": {}
+ },
+ {
+ "label": "get_all_bbox_params",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.smooth_bbox",
+ "description": "modules.PyMAF.utils.smooth_bbox",
+ "peekOfCode": "def get_all_bbox_params(kps, vis_thresh=2):\n \"\"\"\n Finds bounding box parameters for all keypoints.\n Look for sequences in the middle with no predictions and linearly\n interpolate the bbox params for those\n Args:\n kps (list): List of kps (Kx3) or None.\n vis_thresh (float): Threshold for visibility.\n Returns:\n bbox_params, start_index (incl), end_index (excl)",
+ "detail": "modules.PyMAF.utils.smooth_bbox",
+ "documentation": {}
+ },
+ {
+ "label": "smooth_bbox_params",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.smooth_bbox",
+ "description": "modules.PyMAF.utils.smooth_bbox",
+ "peekOfCode": "def smooth_bbox_params(bbox_params, kernel_size=11, sigma=8):\n \"\"\"\n Applies median filtering and then gaussian filtering to bounding box\n parameters.\n Args:\n bbox_params (Nx3): [cx, cy, scale].\n kernel_size (int): Kernel size for median filtering (must be odd).\n sigma (float): Sigma for gaussian smoothing.\n Returns:\n Smoothed bounding box parameters (Nx3).",
+ "detail": "modules.PyMAF.utils.smooth_bbox",
+ "documentation": {}
+ },
+ {
+ "label": "flip_back",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.transforms",
+ "description": "modules.PyMAF.utils.transforms",
+ "peekOfCode": "def flip_back(output_flipped, matched_parts):\n '''\n ouput_flipped: numpy.ndarray(batch_size, num_joints, height, width)\n '''\n assert output_flipped.ndim == 4,\\\n 'output_flipped should be [batch_size, num_joints, height, width]'\n output_flipped = output_flipped[:, :, :, ::-1]\n for pair in matched_parts:\n tmp = output_flipped[:, pair[0], :, :].copy()\n output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :]",
+ "detail": "modules.PyMAF.utils.transforms",
+ "documentation": {}
+ },
+ {
+ "label": "fliplr_joints",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.transforms",
+ "description": "modules.PyMAF.utils.transforms",
+ "peekOfCode": "def fliplr_joints(joints, joints_vis, width, matched_parts):\n \"\"\"\n flip coords\n \"\"\"\n # Flip horizontal\n joints[:, 0] = width - joints[:, 0] - 1\n # Change left-right parts\n for pair in matched_parts:\n joints[pair[0], :], joints[pair[1], :] = \\\n joints[pair[1], :], joints[pair[0], :].copy()",
+ "detail": "modules.PyMAF.utils.transforms",
+ "documentation": {}
+ },
+ {
+ "label": "transform_preds",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.transforms",
+ "description": "modules.PyMAF.utils.transforms",
+ "peekOfCode": "def transform_preds(coords, center, scale, output_size):\n target_coords = np.zeros(coords.shape)\n trans = get_affine_transform(center, scale, 0, output_size, inv=1)\n for p in range(coords.shape[0]):\n target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)\n return target_coords\ndef get_affine_transform(\n center, scale, rot, output_size,\n shift=np.array([0, 0], dtype=np.float32), inv=0\n):",
+ "detail": "modules.PyMAF.utils.transforms",
+ "documentation": {}
+ },
+ {
+ "label": "get_affine_transform",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.transforms",
+ "description": "modules.PyMAF.utils.transforms",
+ "peekOfCode": "def get_affine_transform(\n center, scale, rot, output_size,\n shift=np.array([0, 0], dtype=np.float32), inv=0\n):\n if not isinstance(scale, np.ndarray) and not isinstance(scale, list):\n # print(scale)\n scale = np.array([scale, scale])\n scale_tmp = scale * 200.0\n src_w = scale_tmp[0]\n dst_w = output_size[0]",
+ "detail": "modules.PyMAF.utils.transforms",
+ "documentation": {}
+ },
+ {
+ "label": "affine_transform",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.transforms",
+ "description": "modules.PyMAF.utils.transforms",
+ "peekOfCode": "def affine_transform(pt, t):\n new_pt = np.array([pt[0], pt[1], 1.]).T\n new_pt = np.dot(t, new_pt)\n return new_pt[:2]\ndef get_3rd_point(a, b):\n direct = a - b\n return b + np.array([-direct[1], direct[0]], dtype=np.float32)\ndef get_dir(src_point, rot_rad):\n sn, cs = np.sin(rot_rad), np.cos(rot_rad)\n src_result = [0, 0]",
+ "detail": "modules.PyMAF.utils.transforms",
+ "documentation": {}
+ },
+ {
+ "label": "get_3rd_point",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.transforms",
+ "description": "modules.PyMAF.utils.transforms",
+ "peekOfCode": "def get_3rd_point(a, b):\n direct = a - b\n return b + np.array([-direct[1], direct[0]], dtype=np.float32)\ndef get_dir(src_point, rot_rad):\n sn, cs = np.sin(rot_rad), np.cos(rot_rad)\n src_result = [0, 0]\n src_result[0] = src_point[0] * cs - src_point[1] * sn\n src_result[1] = src_point[0] * sn + src_point[1] * cs\n return src_result\ndef crop(img, center, scale, output_size, rot=0):",
+ "detail": "modules.PyMAF.utils.transforms",
+ "documentation": {}
+ },
+ {
+ "label": "get_dir",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.transforms",
+ "description": "modules.PyMAF.utils.transforms",
+ "peekOfCode": "def get_dir(src_point, rot_rad):\n sn, cs = np.sin(rot_rad), np.cos(rot_rad)\n src_result = [0, 0]\n src_result[0] = src_point[0] * cs - src_point[1] * sn\n src_result[1] = src_point[0] * sn + src_point[1] * cs\n return src_result\ndef crop(img, center, scale, output_size, rot=0):\n trans = get_affine_transform(center, scale, rot, output_size)\n dst_img = cv2.warpAffine(\n img, trans, (int(output_size[0]), int(output_size[1])),",
+ "detail": "modules.PyMAF.utils.transforms",
+ "documentation": {}
+ },
+ {
+ "label": "crop",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.transforms",
+ "description": "modules.PyMAF.utils.transforms",
+ "peekOfCode": "def crop(img, center, scale, output_size, rot=0):\n trans = get_affine_transform(center, scale, rot, output_size)\n dst_img = cv2.warpAffine(\n img, trans, (int(output_size[0]), int(output_size[1])),\n flags=cv2.INTER_LINEAR\n )\n return dst_img",
+ "detail": "modules.PyMAF.utils.transforms",
+ "documentation": {}
+ },
+ {
+ "label": "iuv_map2img",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.uv_vis",
+ "description": "modules.PyMAF.utils.uv_vis",
+ "peekOfCode": "def iuv_map2img(U_uv, V_uv, Index_UV, AnnIndex=None, uv_rois=None, ind_mapping=None):\n device_id = U_uv.get_device()\n batch_size = U_uv.size(0)\n K = U_uv.size(1)\n heatmap_size = U_uv.size(2)\n Index_UV_max = torch.argmax(Index_UV, dim=1)\n if AnnIndex is None:\n Index_UV_max = Index_UV_max.to(torch.int64)\n else:\n AnnIndex_max = torch.argmax(AnnIndex, dim=1)",
+ "detail": "modules.PyMAF.utils.uv_vis",
+ "documentation": {}
+ },
+ {
+ "label": "vis_smpl_iuv",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.uv_vis",
+ "description": "modules.PyMAF.utils.uv_vis",
+ "peekOfCode": "def vis_smpl_iuv(image, cam_pred, vert_pred, face, pred_uv, vert_errors_batch, image_name, save_path, opt, ratio=1):\n # save_path = os.path.join('./notebooks/output/demo_results-wild', ids[f_id][0])\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n # dr_render = OpenDRenderer(ratio=ratio)\n dr_render = PyRenderer()\n focal_length = 5000.\n orig_size = 224.\n if pred_uv is not None:\n iuv_img = iuv_map2img(*pred_uv)",
+ "detail": "modules.PyMAF.utils.uv_vis",
+ "documentation": {}
+ },
+ {
+ "label": "get_colors",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "def get_colors():\n colors = {\n 'pink': np.array([197, 27, 125]), # L lower leg\n 'light_pink': np.array([233, 163, 201]), # L upper leg\n 'light_green': np.array([161, 215, 106]), # L lower arm\n 'green': np.array([77, 146, 33]), # L upper arm\n 'red': np.array([215, 48, 39]), # head\n 'light_red': np.array([252, 146, 114]), # head\n 'light_orange': np.array([252, 141, 89]), # chest\n 'purple': np.array([118, 42, 131]), # R lower leg",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "kp_connections",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "def kp_connections(keypoints):\n kp_lines = [\n [keypoints.index('left_eye'), keypoints.index('right_eye')],\n [keypoints.index('left_eye'), keypoints.index('nose')],\n [keypoints.index('right_eye'), keypoints.index('nose')],\n [keypoints.index('right_eye'), keypoints.index('right_ear')],\n [keypoints.index('left_eye'), keypoints.index('left_ear')],\n [keypoints.index('right_shoulder'), keypoints.index('right_elbow')],\n [keypoints.index('right_elbow'), keypoints.index('right_wrist')],\n [keypoints.index('left_shoulder'), keypoints.index('left_elbow')],",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "convert_from_cls_format",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "def convert_from_cls_format(cls_boxes, cls_segms, cls_keyps):\n \"\"\"Convert from the class boxes/segms/keyps format generated by the testing\n code.\n \"\"\"\n box_list = [b for b in cls_boxes if len(b) > 0]\n if len(box_list) > 0:\n boxes = np.concatenate(box_list)\n else:\n boxes = None\n if cls_segms is not None:",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "vis_bbox_opencv",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "def vis_bbox_opencv(img, bbox, thick=1):\n \"\"\"Visualizes a bounding box.\"\"\"\n (x0, y0, w, h) = bbox\n x1, y1 = int(x0 + w), int(y0 + h)\n x0, y0 = int(x0), int(y0)\n cv2.rectangle(img, (x0, y0), (x1, y1), _GREEN, thickness=thick)\n return img\ndef get_class_string(class_index, score, dataset):\n class_text = dataset.classes[class_index] if dataset is not None else \\\n 'id{:d}'.format(class_index)",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "get_class_string",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "def get_class_string(class_index, score, dataset):\n class_text = dataset.classes[class_index] if dataset is not None else \\\n 'id{:d}'.format(class_index)\n return class_text + ' {:0.2f}'.format(score).lstrip('0')\ndef vis_one_image(\n im, im_name, output_dir, boxes, segms=None, keypoints=None, body_uv=None, thresh=0.9,\n kp_thresh=2, dpi=200, box_alpha=0.0, dataset=None, show_class=False,\n ext='pdf'):\n \"\"\"Visual debugging of detections.\"\"\"\n if not os.path.exists(output_dir):",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "vis_one_image",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "def vis_one_image(\n im, im_name, output_dir, boxes, segms=None, keypoints=None, body_uv=None, thresh=0.9,\n kp_thresh=2, dpi=200, box_alpha=0.0, dataset=None, show_class=False,\n ext='pdf'):\n \"\"\"Visual debugging of detections.\"\"\"\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n if isinstance(boxes, list):\n boxes, segms, keypoints, classes = convert_from_cls_format(\n boxes, segms, keypoints)",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "vis_batch_image_with_joints",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "def vis_batch_image_with_joints(batch_image, batch_joints, batch_joints_vis,\n file_name=None, nrow=8, padding=0, pad_value=1, add_text=True):\n '''\n batch_image: [batch_size, channel, height, width]\n batch_joints: [batch_size, num_joints, 3],\n batch_joints_vis: [batch_size, num_joints, 1],\n }\n '''\n grid = torchvision.utils.make_grid(batch_image, nrow, padding, True, pad_value=pad_value)\n ndarr = grid.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy()",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "vis_img_3Djoint",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "def vis_img_3Djoint(batch_img, joints, pairs=None, joint_group=None):\n n_sample = joints.shape[0]\n max_show = 2\n if n_sample > max_show:\n if batch_img is not None:\n batch_img = batch_img[:max_show]\n joints = joints[:max_show]\n n_sample = max_show\n color = ['#00B0F0', '#00B050', '#DC6464', '#207070', '#BC4484']\n # color = ['g', 'b', 'r']",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "vis_img_2Djoint",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "def vis_img_2Djoint(batch_img, joints, pairs=None, joint_group=None):\n n_sample = joints.shape[0]\n max_show = 2\n if n_sample > max_show:\n if batch_img is not None:\n batch_img = batch_img[:max_show]\n joints = joints[:max_show]\n n_sample = max_show\n color = ['#00B0F0', '#00B050', '#DC6464', '#207070', '#BC4484']\n # color = ['g', 'b', 'r']",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "draw_skeleton",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "def draw_skeleton(image, kp_2d, dataset='common', unnormalize=True, thickness=2):\n if unnormalize:\n kp_2d[:,:2] = normalize_2d_kp(kp_2d[:,:2], 224, inv=True)\n kp_2d[:,2] = kp_2d[:,2] > 0.3\n kp_2d = np.array(kp_2d, dtype=int)\n rcolor = get_colors()['red'].tolist()\n pcolor = get_colors()['green'].tolist()\n lcolor = get_colors()['blue'].tolist()\n common_lr = [0,0,1,1,0,0,0,0,1,0,0,1,1,1,0]\n for idx,pt in enumerate(kp_2d):",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "set_axes_equal",
+ "kind": 2,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "def set_axes_equal(ax):\n '''Make axes of 3D plot have equal scale so that spheres appear as spheres,\n cubes as cubes, etc.. This is one possible solution to Matplotlib's\n ax.set_aspect('equal') and ax.axis('equal') not working for 3D.\n Input\n ax: a matplotlib axis, e.g., as output from plt.gca().\n '''\n x_limits = ax.get_xlim3d()\n y_limits = ax.get_ylim3d()\n z_limits = ax.get_zlim3d()",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "plt.rcParams['pdf.fonttype']",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "plt.rcParams['pdf.fonttype'] = 42 # For editing in Adobe Illustrator\n_GRAY = (218, 227, 218)\n_GREEN = (18, 127, 15)\n_WHITE = (255, 255, 255)\ndef get_colors():\n colors = {\n 'pink': np.array([197, 27, 125]), # L lower leg\n 'light_pink': np.array([233, 163, 201]), # L upper leg\n 'light_green': np.array([161, 215, 106]), # L lower arm\n 'green': np.array([77, 146, 33]), # L upper arm",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "_GRAY",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "_GRAY = (218, 227, 218)\n_GREEN = (18, 127, 15)\n_WHITE = (255, 255, 255)\ndef get_colors():\n colors = {\n 'pink': np.array([197, 27, 125]), # L lower leg\n 'light_pink': np.array([233, 163, 201]), # L upper leg\n 'light_green': np.array([161, 215, 106]), # L lower arm\n 'green': np.array([77, 146, 33]), # L upper arm\n 'red': np.array([215, 48, 39]), # head",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "_GREEN",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "_GREEN = (18, 127, 15)\n_WHITE = (255, 255, 255)\ndef get_colors():\n colors = {\n 'pink': np.array([197, 27, 125]), # L lower leg\n 'light_pink': np.array([233, 163, 201]), # L upper leg\n 'light_green': np.array([161, 215, 106]), # L lower arm\n 'green': np.array([77, 146, 33]), # L upper arm\n 'red': np.array([215, 48, 39]), # head\n 'light_red': np.array([252, 146, 114]), # head",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "_WHITE",
+ "kind": 5,
+ "importPath": "modules.PyMAF.utils.vis",
+ "description": "modules.PyMAF.utils.vis",
+ "peekOfCode": "_WHITE = (255, 255, 255)\ndef get_colors():\n colors = {\n 'pink': np.array([197, 27, 125]), # L lower leg\n 'light_pink': np.array([233, 163, 201]), # L upper leg\n 'light_green': np.array([161, 215, 106]), # L lower arm\n 'green': np.array([77, 146, 33]), # L upper arm\n 'red': np.array([215, 48, 39]), # head\n 'light_red': np.array([252, 146, 114]), # head\n 'light_orange': np.array([252, 141, 89]), # chest",
+ "detail": "modules.PyMAF.utils.vis",
+ "documentation": {}
+ },
+ {
+ "label": "video",
+ "kind": 2,
+ "importPath": "modules.PyMAF.pymaf_x_demo",
+ "description": "modules.PyMAF.pymaf_x_demo",
+ "peekOfCode": "def video(path):\n mp4 = open(path,'rb').read()\n data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n return HTML('' % data_url)\nvideo('output/dancer/dancer_result.mp4')",
+ "detail": "modules.PyMAF.pymaf_x_demo",
+ "documentation": {}
+ },
+ {
+ "label": "hand_pca_to_axis",
+ "kind": 2,
+ "importPath": "SHOW.datasets.model_func_atach",
+ "description": "SHOW.datasets.model_func_atach",
+ "peekOfCode": "def hand_pca_to_axis(self, lhand_pca, rhand_pca):\n # device=self.left_hand_mean.device\n lhand_axis = torch.einsum('bi,ij->bj', [lhand_pca, self.left_hand_components])\n rhand_axis = torch.einsum('bi,ij->bj', [rhand_pca, self.right_hand_components])\n if not self.flat_hand_mean:\n lhand_axis=lhand_axis+self.left_hand_mean\n rhand_axis=rhand_axis+self.right_hand_mean\n return lhand_axis,rhand_axis\ndef hand_axis_to_pca(self, lhand_axis, rhand_axis):\n device=self.left_hand_mean.device",
+ "detail": "SHOW.datasets.model_func_atach",
+ "documentation": {}
+ },
+ {
+ "label": "hand_axis_to_pca",
+ "kind": 2,
+ "importPath": "SHOW.datasets.model_func_atach",
+ "description": "SHOW.datasets.model_func_atach",
+ "peekOfCode": "def hand_axis_to_pca(self, lhand_axis, rhand_axis):\n device=self.left_hand_mean.device\n if isinstance(lhand_axis, np.ndarray):\n lhand_axis = torch.from_numpy(lhand_axis)\n if isinstance(rhand_axis, np.ndarray):\n rhand_axis = torch.from_numpy(rhand_axis)\n lhand_axis = lhand_axis.reshape(-1, 45).to(device)\n rhand_axis = rhand_axis.reshape(-1, 45).to(device)\n if not self.flat_hand_mean:\n lhand_axis=lhand_axis-self.left_hand_mean",
+ "detail": "SHOW.datasets.model_func_atach",
+ "documentation": {}
+ },
+ {
+ "label": "atach_model_func",
+ "kind": 2,
+ "importPath": "SHOW.datasets.model_func_atach",
+ "description": "SHOW.datasets.model_func_atach",
+ "peekOfCode": "def atach_model_func(model):\n if not hasattr(model, 'hand_axis_to_pca'):\n setattr(model, 'hand_axis_to_pca',hand_axis_to_pca)\n if not hasattr(model, 'hand_pca_to_axis'):\n setattr(model, 'hand_pca_to_axis',hand_pca_to_axis)\n if not hasattr(model, 'l_comp'):\n l_comp = torch.linalg.pinv(model.left_hand_components)\n r_comp = torch.linalg.pinv(model.right_hand_components)\n setattr(model, 'l_comp', l_comp)\n setattr(model, 'r_comp', r_comp)",
+ "detail": "SHOW.datasets.model_func_atach",
+ "documentation": {}
+ },
+ {
+ "label": "op_base",
+ "kind": 6,
+ "importPath": "SHOW.datasets.op_base",
+ "description": "SHOW.datasets.op_base",
+ "peekOfCode": "class op_base(object):\n def get_joint_weights(self) -> torch.Tensor:\n # @return optim_weights: [1,135,1]\n self.optim_weights = torch.ones(\n self.num_joints +\n 2 * self.use_hands +\n 51 * self.use_face +\n 17 * self.use_face_contour,\n dtype=self.dtype\n ).to(self.device)",
+ "detail": "SHOW.datasets.op_base",
+ "documentation": {}
+ },
+ {
+ "label": "return_item_tuple",
+ "kind": 5,
+ "importPath": "SHOW.datasets.op_base",
+ "description": "SHOW.datasets.op_base",
+ "peekOfCode": "return_item_tuple = namedtuple(\n 'return_item_tuple',\n ['keypoints_2d', 'gender_gt']\n)\nreturn_item_tuple.__new__.__defaults__ = (None,)*len(return_item_tuple._fields)\nclass op_base(object):\n def get_joint_weights(self) -> torch.Tensor:\n # @return optim_weights: [1,135,1]\n self.optim_weights = torch.ones(\n self.num_joints +",
+ "detail": "SHOW.datasets.op_base",
+ "documentation": {}
+ },
+ {
+ "label": "return_item_tuple.__new__.__defaults__",
+ "kind": 5,
+ "importPath": "SHOW.datasets.op_base",
+ "description": "SHOW.datasets.op_base",
+ "peekOfCode": "return_item_tuple.__new__.__defaults__ = (None,)*len(return_item_tuple._fields)\nclass op_base(object):\n def get_joint_weights(self) -> torch.Tensor:\n # @return optim_weights: [1,135,1]\n self.optim_weights = torch.ones(\n self.num_joints +\n 2 * self.use_hands +\n 51 * self.use_face +\n 17 * self.use_face_contour,\n dtype=self.dtype",
+ "detail": "SHOW.datasets.op_base",
+ "documentation": {}
+ },
+ {
+ "label": "op_dataset",
+ "kind": 6,
+ "importPath": "SHOW.datasets.op_dataset",
+ "description": "SHOW.datasets.op_dataset",
+ "peekOfCode": "class op_dataset(op_base):\n NUM_BODY_JOINTS = 25\n NUM_HAND_JOINTS = 20\n def __init__(\n self,\n dtype=torch.float32,\n device='cpu',\n batch_size=-1,\n config=None,\n face_ider=None,",
+ "detail": "SHOW.datasets.op_dataset",
+ "documentation": {}
+ },
+ {
+ "label": "op_post_process",
+ "kind": 6,
+ "importPath": "SHOW.datasets.op_post_process",
+ "description": "SHOW.datasets.op_post_process",
+ "peekOfCode": "class op_post_process(object):\n def __init__(self, all_processed_item, device, dtype):\n self.all_processed_item = all_processed_item\n self.device = device\n self.dtype = dtype\n def run(self):\n self.parse_batch()\n self.merge_list_to_tensor()\n return self.parse_data\n def check_valid(self):",
+ "detail": "SHOW.datasets.op_post_process",
+ "documentation": {}
+ },
+ {
+ "label": "deca_exp_to_smplx",
+ "kind": 2,
+ "importPath": "SHOW.datasets.pre_dataset",
+ "description": "SHOW.datasets.pre_dataset",
+ "peekOfCode": "def deca_exp_to_smplx(e_deca):\n e_deca = np.concatenate([e_deca, np.zeros(50)])\n e_smplx = deca_exp_to_smplx_X.dot(e_deca)\n e_smplx = e_smplx[:50]\n return e_smplx\ndef read_mp(mp_npz_file, height, width):\n try:\n mp_npz = np.load(mp_npz_file, allow_pickle=True)\n except:\n import traceback",
+ "detail": "SHOW.datasets.pre_dataset",
+ "documentation": {}
+ },
+ {
+ "label": "read_mp",
+ "kind": 2,
+ "importPath": "SHOW.datasets.pre_dataset",
+ "description": "SHOW.datasets.pre_dataset",
+ "peekOfCode": "def read_mp(mp_npz_file, height, width):\n try:\n mp_npz = np.load(mp_npz_file, allow_pickle=True)\n except:\n import traceback\n traceback.print_exc()\n return None\n mp_npz = list(mp_npz.values())\n return_list = []\n for ret in mp_npz:",
+ "detail": "SHOW.datasets.pre_dataset",
+ "documentation": {}
+ },
+ {
+ "label": "read_pixie",
+ "kind": 2,
+ "importPath": "SHOW.datasets.pre_dataset",
+ "description": "SHOW.datasets.pre_dataset",
+ "peekOfCode": "def read_pixie(pixie_mat_file, height, width, cvt_hand_func=None):\n pixie_ret_list = mmcv.load(pixie_mat_file)\n assert cvt_hand_func, 'cvt_hand_func must set'\n return_list = []\n for ret in pixie_ret_list:\n for key, val in ret.items():\n if isinstance(val, np.ndarray) and val.shape[0] == 1:\n ret[key] = ret[key][0]\n face_bbox = lmk2d_to_bbox(ret['face_kpt'], height, width)\n if 1:",
+ "detail": "SHOW.datasets.pre_dataset",
+ "documentation": {}
+ },
+ {
+ "label": "read_deca",
+ "kind": 2,
+ "importPath": "SHOW.datasets.pre_dataset",
+ "description": "SHOW.datasets.pre_dataset",
+ "peekOfCode": "def read_deca(deca_mat_file):\n assert(osp.exists(deca_mat_file))\n deca_ret_list = mmcv.load(deca_mat_file)\n assert(deca_ret_list != [])\n return_list = []\n for ret in deca_ret_list:\n for key, val in ret.items():\n if isinstance(val, np.ndarray) and val.shape[0] == 1:\n ret[key] = ret[key][0]\n deca_lmk = torch.tensor(ret['landmarks2d'])",
+ "detail": "SHOW.datasets.pre_dataset",
+ "documentation": {}
+ },
+ {
+ "label": "deca_exp_to_smplx_X",
+ "kind": 5,
+ "importPath": "SHOW.datasets.pre_dataset",
+ "description": "SHOW.datasets.pre_dataset",
+ "peekOfCode": "deca_exp_to_smplx_X = np.load(\n osp.join(os.path.dirname(__file__),\n '../../../data/flame2020to2019_exp_trafo.npy')\n)\ndef deca_exp_to_smplx(e_deca):\n e_deca = np.concatenate([e_deca, np.zeros(50)])\n e_smplx = deca_exp_to_smplx_X.dot(e_deca)\n e_smplx = e_smplx[:50]\n return e_smplx\ndef read_mp(mp_npz_file, height, width):",
+ "detail": "SHOW.datasets.pre_dataset",
+ "documentation": {}
+ },
+ {
+ "label": "path_enter",
+ "kind": 6,
+ "importPath": "SHOW.datasets.pre_runner",
+ "description": "SHOW.datasets.pre_runner",
+ "peekOfCode": "class path_enter(object):\n def __init__(self,target_path=None):\n self.origin_path=None\n self.target_path=target_path\n def __enter__(self):\n if sys.path[0]!=self.target_path:\n sys.path.insert(\n 0,self.target_path\n )\n if self.target_path:",
+ "detail": "SHOW.datasets.pre_runner",
+ "documentation": {}
+ },
+ {
+ "label": "run_smplifyx_org",
+ "kind": 2,
+ "importPath": "SHOW.datasets.pre_runner",
+ "description": "SHOW.datasets.pre_runner",
+ "peekOfCode": "def run_smplifyx_org(\n image_folder, \n output_folder,\n smplifyx_code_dir,\n log_cmds=True,\n **kwargs,\n): \n with path_enter(smplifyx_code_dir):\n data_folder=os.path.dirname(image_folder)\n cmds=[",
+ "detail": "SHOW.datasets.pre_runner",
+ "documentation": {}
+ },
+ {
+ "label": "run_pymafx",
+ "kind": 2,
+ "importPath": "SHOW.datasets.pre_runner",
+ "description": "SHOW.datasets.pre_runner",
+ "peekOfCode": "def run_pymafx(\n image_folder, \n output_folder,\n pymaf_code_dir,\n log_cmds=True,\n no_render=True,\n):\n with path_enter(pymaf_code_dir):\n cmds=[\n 'python apps/demo_smplx.py',",
+ "detail": "SHOW.datasets.pre_runner",
+ "documentation": {}
+ },
+ {
+ "label": "run_psfr",
+ "kind": 2,
+ "importPath": "SHOW.datasets.pre_runner",
+ "description": "SHOW.datasets.pre_runner",
+ "peekOfCode": "def run_psfr(\n image_folder,\n image_sup_folder,\n log_cmds=True,\n):\n psfr_code_dir=os.path.join(os.path.dirname(__file__),'../../modules/PSFRGAN')\n with path_enter(psfr_code_dir):\n cmds=[\n 'python test_enhance_dir_unalign.py',\n '--src_dir',f'\"{image_folder}\"', ",
+ "detail": "SHOW.datasets.pre_runner",
+ "documentation": {}
+ },
+ {
+ "label": "FaceDetector",
+ "kind": 6,
+ "importPath": "SHOW.detector.face_detector",
+ "description": "SHOW.detector.face_detector",
+ "peekOfCode": "class FaceDetector:\n def __init__(self, type='google', device='cpu'):\n self.type = type\n self.detector = mp_face_mesh.FaceMesh(\n static_image_mode=False,\n max_num_faces=3,\n refine_landmarks=True,\n min_detection_confidence=0.5,\n min_tracking_confidence=0.5)\n def dense_multi_face(self, image):",
+ "detail": "SHOW.detector.face_detector",
+ "documentation": {}
+ },
+ {
+ "label": "mp_drawing",
+ "kind": 5,
+ "importPath": "SHOW.detector.face_detector",
+ "description": "SHOW.detector.face_detector",
+ "peekOfCode": "mp_drawing = mp.solutions.drawing_utils\nmp_drawing_styles = mp.solutions.drawing_styles\nmp_face_mesh = mp.solutions.face_mesh\nclass FaceDetector:\n def __init__(self, type='google', device='cpu'):\n self.type = type\n self.detector = mp_face_mesh.FaceMesh(\n static_image_mode=False,\n max_num_faces=3,\n refine_landmarks=True,",
+ "detail": "SHOW.detector.face_detector",
+ "documentation": {}
+ },
+ {
+ "label": "mp_drawing_styles",
+ "kind": 5,
+ "importPath": "SHOW.detector.face_detector",
+ "description": "SHOW.detector.face_detector",
+ "peekOfCode": "mp_drawing_styles = mp.solutions.drawing_styles\nmp_face_mesh = mp.solutions.face_mesh\nclass FaceDetector:\n def __init__(self, type='google', device='cpu'):\n self.type = type\n self.detector = mp_face_mesh.FaceMesh(\n static_image_mode=False,\n max_num_faces=3,\n refine_landmarks=True,\n min_detection_confidence=0.5,",
+ "detail": "SHOW.detector.face_detector",
+ "documentation": {}
+ },
+ {
+ "label": "mp_face_mesh",
+ "kind": 5,
+ "importPath": "SHOW.detector.face_detector",
+ "description": "SHOW.detector.face_detector",
+ "peekOfCode": "mp_face_mesh = mp.solutions.face_mesh\nclass FaceDetector:\n def __init__(self, type='google', device='cpu'):\n self.type = type\n self.detector = mp_face_mesh.FaceMesh(\n static_image_mode=False,\n max_num_faces=3,\n refine_landmarks=True,\n min_detection_confidence=0.5,\n min_tracking_confidence=0.5)",
+ "detail": "SHOW.detector.face_detector",
+ "documentation": {}
+ },
+ {
+ "label": "FAN_Detector",
+ "kind": 6,
+ "importPath": "SHOW.detector.fan_detector",
+ "description": "SHOW.detector.fan_detector",
+ "peekOfCode": "class FAN_Detector(object):\n def __init__(self,device='cuda'):\n if self.__dict__.get('face_detector',None) is None:\n import face_alignment\n self.face_detector = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device=device)\n def predict(self,\n img_folder,fan_npy_folder,\n save_vis,fan_vis_dir):\n if save_vis:\n os.makedirs(fan_vis_dir,exist_ok=True)",
+ "detail": "SHOW.detector.fan_detector",
+ "documentation": {}
+ },
+ {
+ "label": "pifpaf_detector",
+ "kind": 6,
+ "importPath": "SHOW.detector.pifpaf_detector",
+ "description": "SHOW.detector.pifpaf_detector",
+ "peekOfCode": "class pifpaf_detector(object):\n # chk_type:str = 'shufflenetv2k30-wholebody'\n # def __post_init__(self):\n def __init__(self):\n chk_type:str = 'shufflenetv2k30-wholebody'\n self.predictor = openpifpaf.Predictor(checkpoint=chk_type)\n def process(self,full_file_name,out_file_name,out_img_name):\n pil_im = PIL.Image.open(full_file_name).convert('RGB')\n predictions, _, _ = self.predictor.pil_image(pil_im)\n # sample_img=cv2.cvtColor(sample_img,cv2.COLOR_BGR2RGB)",
+ "detail": "SHOW.detector.pifpaf_detector",
+ "documentation": {}
+ },
+ {
+ "label": "arcface_ider",
+ "kind": 6,
+ "importPath": "SHOW.face_iders.arcface_ider",
+ "description": "SHOW.face_iders.arcface_ider",
+ "peekOfCode": "class arcface_ider(ider_base):\n def __init__(self,\n weight=default_weight_path,\n name='r100', fp16=True, \n det='fan', threshold=0.45, **kwargs\n ):\n self.threshold = threshold\n self.det = det\n from modules.arcface_torch.backbones import get_model\n self.net = get_model(name, fp16=fp16)",
+ "detail": "SHOW.face_iders.arcface_ider",
+ "documentation": {}
+ },
+ {
+ "label": "ider_base",
+ "kind": 6,
+ "importPath": "SHOW.face_iders.base",
+ "description": "SHOW.face_iders.base",
+ "peekOfCode": "class ider_base(object):\n def get_all_emb(self, im: np.ndarray = None) -> np.ndarray:\n # im：bgr\n faces = self.app.get(im)\n return faces\n def get_face_emb(self, im: np.ndarray = None) -> np.ndarray:\n # im：bgr\n faces = self.app.get(im)\n emb = faces[0].normed_embedding\n return emb",
+ "detail": "SHOW.face_iders.base",
+ "documentation": {}
+ },
+ {
+ "label": "insightface_ider",
+ "kind": 6,
+ "importPath": "SHOW.face_iders.base",
+ "description": "SHOW.face_iders.base",
+ "peekOfCode": "class insightface_ider(ider_base):\n def __init__(self, threshold=0.6, **kwargs):\n self.threshold = threshold\n from insightface.app import FaceAnalysis\n self.app = FaceAnalysis(providers=['CUDAExecutionProvider'])\n self.app.prepare(ctx_id=-1, det_size=(640, 640))",
+ "detail": "SHOW.face_iders.base",
+ "documentation": {}
+ },
+ {
+ "label": "build_ider",
+ "kind": 2,
+ "importPath": "SHOW.face_iders.builder",
+ "description": "SHOW.face_iders.builder",
+ "peekOfCode": "def build_ider(config):\n return mmcv.build_from_cfg(config,IDER)\ndef build_ider2(config):\n return IDER.build(config)",
+ "detail": "SHOW.face_iders.builder",
+ "documentation": {}
+ },
+ {
+ "label": "build_ider2",
+ "kind": 2,
+ "importPath": "SHOW.face_iders.builder",
+ "description": "SHOW.face_iders.builder",
+ "peekOfCode": "def build_ider2(config):\n return IDER.build(config)",
+ "detail": "SHOW.face_iders.builder",
+ "documentation": {}
+ },
+ {
+ "label": "IDER",
+ "kind": 5,
+ "importPath": "SHOW.face_iders.builder",
+ "description": "SHOW.face_iders.builder",
+ "peekOfCode": "IDER = Registry('ider')\ndef build_ider(config):\n return mmcv.build_from_cfg(config,IDER)\ndef build_ider2(config):\n return IDER.build(config)",
+ "detail": "SHOW.face_iders.builder",
+ "documentation": {}
+ },
+ {
+ "label": "match_faces",
+ "kind": 2,
+ "importPath": "SHOW.face_iders.utils",
+ "description": "SHOW.face_iders.utils",
+ "peekOfCode": "def match_faces(img, face_ider, person_face_emb):\n # img: bgr,hw3,uint8\n faces = face_ider.get(img)\n if faces is None:\n return None, None\n # face_ider: 1.func:get(np_img) --> {2.normed_embedding,3.bbox}\n for face in faces:\n cur_emb = face.normed_embedding\n sim = face_ider.cal_emb_sim(cur_emb, person_face_emb)\n if sim >= face_ider.threshold:",
+ "detail": "SHOW.face_iders.utils",
+ "documentation": {}
+ },
+ {
+ "label": "Struct",
+ "kind": 6,
+ "importPath": "SHOW.flame.FLAME",
+ "description": "SHOW.flame.FLAME",
+ "peekOfCode": "class Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)\ndef rot_mat_to_euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +\n rot_mats[:, 1, 0] * rot_mats[:, 1, 0])\n return torch.atan2(-rot_mats[:, 2, 0], sy)",
+ "detail": "SHOW.flame.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "FLAME",
+ "kind": 6,
+ "importPath": "SHOW.flame.FLAME",
+ "description": "SHOW.flame.FLAME",
+ "peekOfCode": "class FLAME(nn.Module):\n \"\"\"\n borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py\n Given FLAME parameters for shape, pose, and expression, this class generates a differentiable FLAME function\n which outputs the a mesh and 2D/3D facial landmarks\n \"\"\"\n def __init__(self, config):\n super(FLAME, self).__init__()\n print(\"Creating the FLAME Decoder\")\n with open(config.flame_geom_path, 'rb') as f:",
+ "detail": "SHOW.flame.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "FLAMETex",
+ "kind": 6,
+ "importPath": "SHOW.flame.FLAME",
+ "description": "SHOW.flame.FLAME",
+ "peekOfCode": "class FLAMETex(nn.Module):\n \"\"\"\n current FLAME texture are adapted from BFM Texture Model\n \"\"\"\n def __init__(self, config):\n super(FLAMETex, self).__init__()\n tex_space = np.load(config.tex_space_path)\n mu_key = 'MU'\n pc_key = 'PC' \n n_pc = 199",
+ "detail": "SHOW.flame.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "to_tensor",
+ "kind": 2,
+ "importPath": "SHOW.flame.FLAME",
+ "description": "SHOW.flame.FLAME",
+ "peekOfCode": "def to_tensor(array, dtype=torch.float32):\n if 'torch.tensor' not in str(type(array)):\n return torch.tensor(array, dtype=dtype)\ndef to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():",
+ "detail": "SHOW.flame.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "to_np",
+ "kind": 2,
+ "importPath": "SHOW.flame.FLAME",
+ "description": "SHOW.flame.FLAME",
+ "peekOfCode": "def to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)\ndef rot_mat_to_euler(rot_mats):\n # Calculates rotation matrix to euler angles",
+ "detail": "SHOW.flame.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "rot_mat_to_euler",
+ "kind": 2,
+ "importPath": "SHOW.flame.FLAME",
+ "description": "SHOW.flame.FLAME",
+ "peekOfCode": "def rot_mat_to_euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +\n rot_mats[:, 1, 0] * rot_mats[:, 1, 0])\n return torch.atan2(-rot_mats[:, 2, 0], sy)\nclass FLAME(nn.Module):\n \"\"\"\n borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py\n Given FLAME parameters for shape, pose, and expression, this class generates a differentiable FLAME function",
+ "detail": "SHOW.flame.FLAME",
+ "documentation": {}
+ },
+ {
+ "label": "stereographic_unproject_old",
+ "kind": 2,
+ "importPath": "SHOW.flame.lbs",
+ "description": "SHOW.flame.lbs",
+ "peekOfCode": "def stereographic_unproject_old(a):\n s2 = torch.pow(a, 2).sum(1) # batch\n unproj = 2 * a / (s2 + 1).view(-1, 1).repeat(1, 5) # batch*5\n w = (s2 - 1) / (s2 + 1) # batch\n out = torch.cat((unproj, w.view(-1, 1)), 1) # batch*6\n return out\n# in a batch*5, axis int\ndef stereographic_unproject(a, axis=None):\n \"\"\"\n\tInverse of stereographic projection: increases dimension by one.",
+ "detail": "SHOW.flame.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "stereographic_unproject",
+ "kind": 2,
+ "importPath": "SHOW.flame.lbs",
+ "description": "SHOW.flame.lbs",
+ "peekOfCode": "def stereographic_unproject(a, axis=None):\n \"\"\"\n\tInverse of stereographic projection: increases dimension by one.\n\t\"\"\"\n batch = a.shape[0]\n if axis is None:\n axis = a.shape[1]\n s2 = torch.pow(a, 2).sum(1) # batch\n ans = torch.autograd.Variable(torch.zeros(batch, a.shape[1] + 1).cuda()) # batch*6\n unproj = 2 * a / (s2 + 1).view(batch, 1).repeat(1, a.shape[1]) # batch*5",
+ "detail": "SHOW.flame.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "rot_mat_to_euler",
+ "kind": 2,
+ "importPath": "SHOW.flame.lbs",
+ "description": "SHOW.flame.lbs",
+ "peekOfCode": "def rot_mat_to_euler(rot_mats):\n # Calculates rotation matrix to euler angles\n # Careful for extreme cases of eular angles like [0.0, pi, 0.0]\n sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] +\n rot_mats[:, 1, 0] * rot_mats[:, 1, 0])\n return torch.atan2(-rot_mats[:, 2, 0], sy)\ndef find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx,\n dynamic_lmk_b_coords,\n neck_kin_chain, dtype=torch.float32):\n ''' Compute the faces, barycentric coordinates for the dynamic landmarks",
+ "detail": "SHOW.flame.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "find_dynamic_lmk_idx_and_bcoords",
+ "kind": 2,
+ "importPath": "SHOW.flame.lbs",
+ "description": "SHOW.flame.lbs",
+ "peekOfCode": "def find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx,\n dynamic_lmk_b_coords,\n neck_kin_chain, dtype=torch.float32):\n ''' Compute the faces, barycentric coordinates for the dynamic landmarks\n To do so, we first compute the rotation of the neck around the y-axis\n and then use a pre-computed look-up table to find the faces and the\n barycentric coordinates that will be used.\n Special thanks to Soubhik Sanyal (soubhik.sanyal@tuebingen.mpg.de)\n for providing the original TensorFlow implementation and for the LUT.\n Parameters",
+ "detail": "SHOW.flame.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "vertices2landmarks",
+ "kind": 2,
+ "importPath": "SHOW.flame.lbs",
+ "description": "SHOW.flame.lbs",
+ "peekOfCode": "def vertices2landmarks(vertices, faces, lmk_faces_idx, lmk_bary_coords):\n ''' Calculates landmarks by barycentric interpolation\n Parameters\n ----------\n vertices: torch.tensor BxVx3, dtype = torch.float32\n The tensor of input vertices\n faces: torch.tensor Fx3, dtype = torch.long\n The faces of the mesh\n lmk_faces_idx: torch.tensor L, dtype = torch.long\n The tensor with the indices of the faces used to calculate the",
+ "detail": "SHOW.flame.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "lbs",
+ "kind": 2,
+ "importPath": "SHOW.flame.lbs",
+ "description": "SHOW.flame.lbs",
+ "peekOfCode": "def lbs(betas, pose, v_template, shapedirs, posedirs, J_regressor, parents,\n lbs_weights, pose2rot=True, dtype=torch.float32):\n ''' Performs Linear Blend Skinning with the given shape and pose parameters\n Parameters\n ----------\n betas : torch.tensor BxNB\n The tensor of shape parameters\n pose : torch.tensor Bx(J + 1) * 3\n The pose parameters in axis-angle format\n v_template torch.tensor BxVx3",
+ "detail": "SHOW.flame.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "vertices2joints",
+ "kind": 2,
+ "importPath": "SHOW.flame.lbs",
+ "description": "SHOW.flame.lbs",
+ "peekOfCode": "def vertices2joints(J_regressor, vertices):\n ''' Calculates the 3D joint locations from the vertices\n Parameters\n ----------\n J_regressor : torch.tensor JxV\n The regressor array that is used to calculate the joints from the\n position of the vertices\n vertices : torch.tensor BxVx3\n The tensor of mesh vertices\n Returns",
+ "detail": "SHOW.flame.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "blend_shapes",
+ "kind": 2,
+ "importPath": "SHOW.flame.lbs",
+ "description": "SHOW.flame.lbs",
+ "peekOfCode": "def blend_shapes(betas, shape_disps):\n ''' Calculates the per vertex displacement due to the blend shapes\n Parameters\n ----------\n betas : torch.tensor Bx(num_betas)\n Blend shape coefficients\n shape_disps: torch.tensor Vx3x(num_betas)\n Blend shapes\n Returns\n -------",
+ "detail": "SHOW.flame.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "transform_mat",
+ "kind": 2,
+ "importPath": "SHOW.flame.lbs",
+ "description": "SHOW.flame.lbs",
+ "peekOfCode": "def transform_mat(R, t):\n ''' Creates a batch of transformation matrices\n Args:\n - R: Bx3x3 array of a batch of rotation matrices\n - t: Bx3x1 array of a batch of translation vectors\n Returns:\n - T: Bx4x4 Transformation matrix\n '''\n # No padding left or right, only add an extra row\n return torch.cat([F.pad(R, [0, 0, 0, 1]),",
+ "detail": "SHOW.flame.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "batch_rigid_transform",
+ "kind": 2,
+ "importPath": "SHOW.flame.lbs",
+ "description": "SHOW.flame.lbs",
+ "peekOfCode": "def batch_rigid_transform(rot_mats, joints, parents, dtype=torch.float32):\n \"\"\"\n Applies a batch of rigid transformations to the joints\n Parameters\n ----------\n rot_mats : torch.tensor BxNx3x3\n Tensor of rotation matrices\n joints : torch.tensor BxNx3\n Locations of joints\n parents : torch.tensor BxN",
+ "detail": "SHOW.flame.lbs",
+ "documentation": {}
+ },
+ {
+ "label": "my_logger",
+ "kind": 6,
+ "importPath": "SHOW.loggers.base",
+ "description": "SHOW.loggers.base",
+ "peekOfCode": "class my_logger(ABCMeta):\n __metaclass__ = ABCMeta\n @abstractmethod\n def log(self,*args,**kwargs):\n pass\n @abstractmethod\n def log_bs(self,*args,**kwargs):\n pass\n @abstractmethod\n def update_config(self,*args,**kwargs):",
+ "detail": "SHOW.loggers.base",
+ "documentation": {}
+ },
+ {
+ "label": "img_preprocess",
+ "kind": 2,
+ "importPath": "SHOW.loggers.base",
+ "description": "SHOW.loggers.base",
+ "peekOfCode": "def img_preprocess(img):\n # img: 0-1\n # img: tensor or ndarray\n # img: (1,c,h,w)\n # img: (c,h,w)\n # img: (h,w,c)\n # img: cpu or gpu\n # img: frad or no_grad\n if isinstance(img,torch.Tensor):\n img=img.cpu().detach()",
+ "detail": "SHOW.loggers.base",
+ "documentation": {}
+ },
+ {
+ "label": "batch_logger",
+ "kind": 6,
+ "importPath": "SHOW.loggers.builder",
+ "description": "SHOW.loggers.builder",
+ "peekOfCode": "class batch_logger:\n def __init__(self, loggers=[]):\n self.loggers = loggers\n self.warn_filter_list = []\n def __iter__(self):\n pass\n def __getstate__(self):\n pass\n def __getattr__(self, name):\n # print(f'__getattr__：{name}')",
+ "detail": "SHOW.loggers.builder",
+ "documentation": {}
+ },
+ {
+ "label": "build_my_logger",
+ "kind": 2,
+ "importPath": "SHOW.loggers.builder",
+ "description": "SHOW.loggers.builder",
+ "peekOfCode": "def build_my_logger(log_config, init_run=True):\n logger_list = []\n log_interval = log_config['interval']\n for info in log_config['hooks']:\n logger_hook = mmcv.build_from_cfg(\n info, MMYLOGGER, default_args=dict(interval=log_interval))\n if init_run:\n # logger_hook.before_run(None)\n logger_hook.initialize()\n logger_list.append(logger_hook)",
+ "detail": "SHOW.loggers.builder",
+ "documentation": {}
+ },
+ {
+ "label": "MMYLOGGER",
+ "kind": 5,
+ "importPath": "SHOW.loggers.builder",
+ "description": "SHOW.loggers.builder",
+ "peekOfCode": "MMYLOGGER = Registry('mylogger')\nclass batch_logger:\n def __init__(self, loggers=[]):\n self.loggers = loggers\n self.warn_filter_list = []\n def __iter__(self):\n pass\n def __getstate__(self):\n pass\n def __getattr__(self, name):",
+ "detail": "SHOW.loggers.builder",
+ "documentation": {}
+ },
+ {
+ "label": "StreamToLoguru",
+ "kind": 6,
+ "importPath": "SHOW.loggers.logger",
+ "description": "SHOW.loggers.logger",
+ "peekOfCode": "class StreamToLoguru:\n \"\"\"\n stream object that redirects writes to a logger instance.\n \"\"\"\n def __init__(self, level=\"INFO\", caller_names=(\"apex\", \"pycocotools\")):\n \"\"\"\n Args:\n level(str): log level string of loguru. Default value: \"INFO\".\n caller_names(tuple): caller names of redirected module.\n Default value: (apex, pycocotools).",
+ "detail": "SHOW.loggers.logger",
+ "documentation": {}
+ },
+ {
+ "label": "get_caller_name",
+ "kind": 2,
+ "importPath": "SHOW.loggers.logger",
+ "description": "SHOW.loggers.logger",
+ "peekOfCode": "def get_caller_name(depth=0):\n \"\"\"\n Args:\n depth (int): Depth of caller conext, use 0 for caller depth. Default value: 0.\n Returns:\n str: module name of the caller\n \"\"\"\n # the following logic is a little bit faster than inspect.stack() logic\n frame = inspect.currentframe().f_back\n for _ in range(depth):",
+ "detail": "SHOW.loggers.logger",
+ "documentation": {}
+ },
+ {
+ "label": "setup_logger",
+ "kind": 2,
+ "importPath": "SHOW.loggers.logger",
+ "description": "SHOW.loggers.logger",
+ "peekOfCode": "def setup_logger(save_dir, distributed_rank=0, filename=\"log.txt\", mode=\"a\"):\n \"\"\"setup logger for training and testing.\n Args:\n save_dir(str): location to save log file\n distributed_rank(int): device rank when multi-gpu environment\n filename (string): log save name.\n mode(str): log file write mode, `append` or `override`. default is `a`.\n Return:\n logger instance.\n \"\"\"",
+ "detail": "SHOW.loggers.logger",
+ "documentation": {}
+ },
+ {
+ "label": "MyNeptuneLogger",
+ "kind": 6,
+ "importPath": "SHOW.loggers.MyNeptuneLogger",
+ "description": "SHOW.loggers.MyNeptuneLogger",
+ "peekOfCode": "class MyNeptuneLogger(NeptuneLoggerHook):\n def __init__(self,*args, **kwargs):\n super().__init__(*args, **kwargs)\n from neptune.new.types import File\n self.File=File\n def initialize(self):\n if self.init_kwargs:\n self.run = self.neptune.init(**self.init_kwargs)\n else:\n self.run = self.neptune.init()",
+ "detail": "SHOW.loggers.MyNeptuneLogger",
+ "documentation": {}
+ },
+ {
+ "label": "MyTextLogger",
+ "kind": 6,
+ "importPath": "SHOW.loggers.MyTextLogger",
+ "description": "SHOW.loggers.MyTextLogger",
+ "peekOfCode": "class MyTextLogger(object):\n def __init__(self,save_dir,filename=\"log.txt\", mode=\"a\",*args, **kwargs):\n from SHOW.loggers.logger import setup_logger\n setup_logger(save_dir,filename,mode=mode)\n @logger.catch\n def log(self, tag_name:str, tag_value,print_to_screen=False,**kwargs):\n logger.log(f\"{tag_name}:{tag_value}\")\n @logger.catch\n def log_bs(self,append=True,print_to_screen=False,**kwargs):\n for key,val in kwargs.items():",
+ "detail": "SHOW.loggers.MyTextLogger",
+ "documentation": {}
+ },
+ {
+ "label": "MyTFLogger",
+ "kind": 6,
+ "importPath": "SHOW.loggers.MyTFLogger",
+ "description": "SHOW.loggers.MyTFLogger",
+ "peekOfCode": "class MyTFLogger(TensorboardLoggerHook,my_logger):\n def __init__(self,*args, **kwargs):\n super().__init__(*args, **kwargs)\n if Path(self.log_dir).exists():\n shutil.rmtree(self.log_dir)\n @logger.catch\n def log(self, tags:dict,iters=0):\n for tag, val in tags.items():\n if isinstance(val, str):\n self.writer.add_text(tag, val, iters)",
+ "detail": "SHOW.loggers.MyTFLogger",
+ "documentation": {}
+ },
+ {
+ "label": "MyWandbLogger",
+ "kind": 6,
+ "importPath": "SHOW.loggers.MyWandbLogger",
+ "description": "SHOW.loggers.MyWandbLogger",
+ "peekOfCode": "class MyWandbLogger(WandbLoggerHook):\n def __init__(self,wandb_key,wandb_name,*args, **kwargs):\n os.environ['WANDB_API_KEY'] = wandb_key\n os.environ['WANDB_NAME'] = wandb_name\n super().__init__(*args, **kwargs)\n def initialize(self):\n if self.wandb is None:\n self.import_wandb()\n if self.init_kwargs:\n self.wandb.init(**self.init_kwargs)",
+ "detail": "SHOW.loggers.MyWandbLogger",
+ "documentation": {}
+ },
+ {
+ "label": "GMoF",
+ "kind": 6,
+ "importPath": "SHOW.losses.losses",
+ "description": "SHOW.losses.losses",
+ "peekOfCode": "class GMoF(nn.Module):\n def __init__(self, rho=100):\n super().__init__()\n self.rho = rho\n def extra_repr(self):\n return 'rho = {}'.format(self.rho)\n def forward(self, residual):\n squared_res = residual ** 2\n dist = torch.div(squared_res, squared_res + self.rho ** 2)\n return self.rho ** 2 * dist",
+ "detail": "SHOW.losses.losses",
+ "documentation": {}
+ },
+ {
+ "label": "temporary_loss",
+ "kind": 2,
+ "importPath": "SHOW.losses.losses",
+ "description": "SHOW.losses.losses",
+ "peekOfCode": "def temporary_loss(o_w, i_w, gmof, param):\n return (o_w ** 2) * (gmof(\n i_w*(param[2:, ...] + param[:-2, ...] - 2 * param[1:-1, ...]))).mean()\ndef cal_deg_delta(deta, deg_interval):\n # deg_loss=0\n theta = torch.arctan((deta[:, 2]) / (deta[:, 1]))\n theta_deg = torch.rad2deg(theta)\n diff_up = theta_deg - deg_interval[1]\n diff_up = torch.where(diff_up > 0, diff_up, torch.zeros(\n diff_up.shape, device=diff_up.device))",
+ "detail": "SHOW.losses.losses",
+ "documentation": {}
+ },
+ {
+ "label": "cal_deg_delta",
+ "kind": 2,
+ "importPath": "SHOW.losses.losses",
+ "description": "SHOW.losses.losses",
+ "peekOfCode": "def cal_deg_delta(deta, deg_interval):\n # deg_loss=0\n theta = torch.arctan((deta[:, 2]) / (deta[:, 1]))\n theta_deg = torch.rad2deg(theta)\n diff_up = theta_deg - deg_interval[1]\n diff_up = torch.where(diff_up > 0, diff_up, torch.zeros(\n diff_up.shape, device=diff_up.device))\n diff_down = deg_interval[0] - theta_deg\n diff_down = torch.where(diff_down > 0, diff_down, torch.zeros(\n diff_down.shape, device=diff_down.device))",
+ "detail": "SHOW.losses.losses",
+ "documentation": {}
+ },
+ {
+ "label": "get_body_height",
+ "kind": 2,
+ "importPath": "SHOW.losses.losses",
+ "description": "SHOW.losses.losses",
+ "peekOfCode": "def get_body_height(verts, faces):\n if verts.shape[0] == 0:\n return 0.0\n else:\n head_faces = faces[2581]\n head_verts = verts[head_faces]\n top_head_v = 0.8277337276382795 * head_verts[0] + 0.1422200962169292 * head_verts[1] + \\\n 0.030046176144791284 * head_verts[2]\n feet_faces = faces[15605]\n feet_verts = verts[feet_faces]",
+ "detail": "SHOW.losses.losses",
+ "documentation": {}
+ },
+ {
+ "label": "compute_mass",
+ "kind": 2,
+ "importPath": "SHOW.losses.losses",
+ "description": "SHOW.losses.losses",
+ "peekOfCode": "def compute_mass(tris, DENSITY=1):\n ''' Computes the mass from volume and average body density\n '''\n x = tris[:, :, :, 0]\n y = tris[:, :, :, 1]\n z = tris[:, :, :, 2]\n volume = (\n -x[:, :, 2] * y[:, :, 1] * z[:, :, 0] +\n x[:, :, 1] * y[:, :, 2] * z[:, :, 0] +\n x[:, :, 2] * y[:, :, 0] * z[:, :, 1] -",
+ "detail": "SHOW.losses.losses",
+ "documentation": {}
+ },
+ {
+ "label": "cvt_dict_to_grad",
+ "kind": 2,
+ "importPath": "SHOW.losses.losses",
+ "description": "SHOW.losses.losses",
+ "peekOfCode": "def cvt_dict_to_grad(params,device,dtype):\n # params：{ np.ndarray/torch.Tensor(cpu/gpu) }\n for key in params.keys():\n if isinstance(params[key],np.ndarray):\n params[key]=torch.from_numpy(params[key])\n params[key]=nn.Parameter(params[key].clone().detach().to(device).type(dtype))\n return params\ndef cal_model_output(vposer=None,body_model=None,body_params=None):\n cur_pose = None\n if vposer is not None:",
+ "detail": "SHOW.losses.losses",
+ "documentation": {}
+ },
+ {
+ "label": "cal_model_output",
+ "kind": 2,
+ "importPath": "SHOW.losses.losses",
+ "description": "SHOW.losses.losses",
+ "peekOfCode": "def cal_model_output(vposer=None,body_model=None,body_params=None):\n cur_pose = None\n if vposer is not None:\n cur_pose = vposer.decode(\n body_params['pose_embedding'],\n output_type='aa'\n ).view(-1,63)\n cur_bs=cur_pose.shape[0]\n model_output = body_model(\n return_verts=True,",
+ "detail": "SHOW.losses.losses",
+ "documentation": {}
+ },
+ {
+ "label": "get_tpose_vertice",
+ "kind": 2,
+ "importPath": "SHOW.losses.losses",
+ "description": "SHOW.losses.losses",
+ "peekOfCode": "def get_tpose_vertice(body_model,betas,**kwargs):\n batch_size=body_model.batch_size\n tpose_body = body_model(\n return_verts=True,\n body_pose=torch.zeros(batch_size, 63).type_as(betas),\n betas=betas.expand(batch_size, -1),\n **kwargs\n )\n tpose_vertices = tpose_body.vertices[0]\n return tpose_vertices",
+ "detail": "SHOW.losses.losses",
+ "documentation": {}
+ },
+ {
+ "label": "cal_smplx_head_transl",
+ "kind": 2,
+ "importPath": "SHOW.losses.losses",
+ "description": "SHOW.losses.losses",
+ "peekOfCode": "def cal_smplx_head_transl(tpose_vertices,smplx2flame_idx):\n pre_flame_vertices = torch.index_select(\n tpose_vertices, 0, smplx2flame_idx)\n smplx_shape_mean = pre_flame_vertices.mean(0)\n return smplx_shape_mean\nclass GMoF(nn.Module):\n def __init__(self, rho=100):\n super().__init__()\n self.rho = rho\n def extra_repr(self):",
+ "detail": "SHOW.losses.losses",
+ "documentation": {}
+ },
+ {
+ "label": "create_closure",
+ "kind": 2,
+ "importPath": "SHOW.losses.losses",
+ "description": "SHOW.losses.losses",
+ "peekOfCode": "def create_closure(\n optimizer,\n vposer,body_model, \n body_params,\n camera_org,\n lmk_faces_idx,\n lmk_bary_coords,\n op_2dkpts,op_j_weight,\n op_gt_conf,op_valid_flag,\n robustifier,",
+ "detail": "SHOW.losses.losses",
+ "documentation": {}
+ },
+ {
+ "label": "attr_dict",
+ "kind": 6,
+ "importPath": "SHOW.utils.attrdict",
+ "description": "SHOW.utils.attrdict",
+ "peekOfCode": "class attr_dict(dict):\n def __init__(self, d=None, **kwargs):\n if d is None:\n d = {}\n if kwargs:\n d.update(**kwargs)\n for k, v in d.items():\n setattr(self, k, v)\n # Class attributes\n for k in self.__class__.__dict__.keys():",
+ "detail": "SHOW.utils.attrdict",
+ "documentation": {}
+ },
+ {
+ "label": "msk_to_xywh",
+ "kind": 2,
+ "importPath": "SHOW.utils.bbox",
+ "description": "SHOW.utils.bbox",
+ "peekOfCode": "def msk_to_xywh(msk):\n \"\"\"\n calculate box [left upper width height] from mask.\n :param msk: nd.array, single-channel or 3-channels mask\n :return: float, iou score \n \"\"\"\n if len(msk.shape) > 2:\n msk = msk[..., 0]\n nonzeros = np.nonzero(msk.astype(np.uint8))\n u, l = np.min(nonzeros, axis=1)",
+ "detail": "SHOW.utils.bbox",
+ "documentation": {}
+ },
+ {
+ "label": "msk_to_xyxy",
+ "kind": 2,
+ "importPath": "SHOW.utils.bbox",
+ "description": "SHOW.utils.bbox",
+ "peekOfCode": "def msk_to_xyxy(msk):\n \"\"\"\n calculate box [left upper right bottom] from mask.\n :param msk: nd.array, single-channel or 3-channels mask\n :return: float, iou score \n \"\"\"\n if len(msk.shape) > 2:\n msk = msk[..., 0]\n nonzeros = np.nonzero(msk.astype(np.uint8))\n u, l = np.min(nonzeros, axis=1)",
+ "detail": "SHOW.utils.bbox",
+ "documentation": {}
+ },
+ {
+ "label": "get_edges",
+ "kind": 2,
+ "importPath": "SHOW.utils.bbox",
+ "description": "SHOW.utils.bbox",
+ "peekOfCode": "def get_edges(msk):\n \"\"\"\n get edge from mask\n :param msk: nd.array, single-channel or 3-channel mask\n :return: edges: nd.array, edges with same shape with mask\n \"\"\"\n msk_sp = msk.shape\n if len(msk_sp) == 2:\n c = 1 # single channel\n elif (len(msk_sp) == 3) and (msk_sp[2] == 3):",
+ "detail": "SHOW.utils.bbox",
+ "documentation": {}
+ },
+ {
+ "label": "prj_vtx_cam",
+ "kind": 2,
+ "importPath": "SHOW.utils.cam",
+ "description": "SHOW.utils.cam",
+ "peekOfCode": "def prj_vtx_cam(vtx, cam_K):\n \"\"\"\n project 3D vertices to 2-dimensional image plane\n :param vtx: (N, 3) or vertices\n :param cam_K: (3, 3), intrinsic camera parameter\n :return: pts_2D: (N, 2), pixel coordinates; z: (N,), depth\n \"\"\"\n pts_3d_c = np.matmul(cam_K, vtx.T) \n pts_2d = pts_3d_c[:2] / pts_3d_c[2]\n z = pts_3d_c[2]",
+ "detail": "SHOW.utils.cam",
+ "documentation": {}
+ },
+ {
+ "label": "prj_vtx_pose",
+ "kind": 2,
+ "importPath": "SHOW.utils.cam",
+ "description": "SHOW.utils.cam",
+ "peekOfCode": "def prj_vtx_pose(vtx, pose, cam_K):\n \"\"\"\n project 3D vertices to 2-dimensional image plane by pose\n :param vtx: (N, 3), vertices\n :param pose: (3, 4)\n :param cam_K: (3, 3), intrinsic camera parameter\n :return: pts_2D: (N, 2), pixel coordinates; z: (N,), depth\n \"\"\"\n # pts_3d_w = torch.mm(pose[:, :3], vtx.t) + pose[:, 3].reshape((3, 1)) # (3, N)\n # pts_3d_c = torch.mm(cam_K, pts_3d_w) ",
+ "detail": "SHOW.utils.cam",
+ "documentation": {}
+ },
+ {
+ "label": "show_im",
+ "kind": 2,
+ "importPath": "SHOW.utils.colab",
+ "description": "SHOW.utils.colab",
+ "peekOfCode": "def show_im():\n from IPython.display import Image, display\n import tempfile\n import os.path as osp\n with tempfile.TemporaryDirectory() as tmpdir:\n file_name = osp.join(tmpdir, 'pose_results.png')\n cv2.imwrite(file_name, vis_result)\n display(Image(file_name))\ndef video(path):\n mp4 = open(path,'rb').read()",
+ "detail": "SHOW.utils.colab",
+ "documentation": {}
+ },
+ {
+ "label": "video",
+ "kind": 2,
+ "importPath": "SHOW.utils.colab",
+ "description": "SHOW.utils.colab",
+ "peekOfCode": "def video(path):\n mp4 = open(path,'rb').read()\n data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n return HTML('' % data_url)",
+ "detail": "SHOW.utils.colab",
+ "documentation": {}
+ },
+ {
+ "label": "try_statement",
+ "kind": 2,
+ "importPath": "SHOW.utils.decorator",
+ "description": "SHOW.utils.decorator",
+ "peekOfCode": "def try_statement(func):\n def wrapper(*args, **kwargs):\n try:\n func(*args, **kwargs)\n except:\n import traceback\n traceback.print_exc()\n return wrapper \n# convert a function into recursive style to \n# handle nested dict/list/tuple variables",
+ "detail": "SHOW.utils.decorator",
+ "documentation": {}
+ },
+ {
+ "label": "make_recursive_func",
+ "kind": 2,
+ "importPath": "SHOW.utils.decorator",
+ "description": "SHOW.utils.decorator",
+ "peekOfCode": "def make_recursive_func(func):\n def wrapper(vars):\n if isinstance(vars, list):\n return [wrapper(x) for x in vars]\n elif isinstance(vars, tuple):\n return tuple([wrapper(x) for x in vars])\n elif isinstance(vars, dict):\n return {k: wrapper(v) for k, v in vars.items()}\n else:\n return func(vars)",
+ "detail": "SHOW.utils.decorator",
+ "documentation": {}
+ },
+ {
+ "label": "tensor2float",
+ "kind": 2,
+ "importPath": "SHOW.utils.decorator",
+ "description": "SHOW.utils.decorator",
+ "peekOfCode": "def tensor2float(vars):\n if isinstance(vars, float) or isinstance(vars, int):\n return vars\n elif isinstance(vars, torch.Tensor):\n return vars.data.item()\n else:\n raise NotImplementedError(\"invalid input type {} for tensor2float\".format(type(vars)))\n@make_recursive_func\ndef tensor2numpy(vars):\n if isinstance(vars, np.ndarray):",
+ "detail": "SHOW.utils.decorator",
+ "documentation": {}
+ },
+ {
+ "label": "tensor2numpy",
+ "kind": 2,
+ "importPath": "SHOW.utils.decorator",
+ "description": "SHOW.utils.decorator",
+ "peekOfCode": "def tensor2numpy(vars):\n if isinstance(vars, np.ndarray):\n return vars\n elif isinstance(vars, torch.Tensor):\n return vars.detach().cpu().numpy().copy()\n else:\n raise NotImplementedError(\"invalid input type {} for tensor2numpy\".format(type(vars)))\n@make_recursive_func\ndef to_tensor(vars):\n if isinstance(vars, np.ndarray):",
+ "detail": "SHOW.utils.decorator",
+ "documentation": {}
+ },
+ {
+ "label": "to_tensor",
+ "kind": 2,
+ "importPath": "SHOW.utils.decorator",
+ "description": "SHOW.utils.decorator",
+ "peekOfCode": "def to_tensor(vars):\n if isinstance(vars, np.ndarray):\n return torch.from_numpy(vars)\n elif isinstance(vars, (int,float)):\n return torch.tensor(vars)\n else:\n return vars\n@make_recursive_func\ndef tocuda(vars):\n if isinstance(vars, torch.Tensor):",
+ "detail": "SHOW.utils.decorator",
+ "documentation": {}
+ },
+ {
+ "label": "tocuda",
+ "kind": 2,
+ "importPath": "SHOW.utils.decorator",
+ "description": "SHOW.utils.decorator",
+ "peekOfCode": "def tocuda(vars):\n if isinstance(vars, torch.Tensor):\n return vars.cuda()\n elif isinstance(vars, str):\n return vars\n else:\n raise NotImplementedError(\"invalid input type {} for tensor2numpy\".format(type(vars)))",
+ "detail": "SHOW.utils.decorator",
+ "documentation": {}
+ },
+ {
+ "label": "img_preprocess",
+ "kind": 2,
+ "importPath": "SHOW.utils.disp_img",
+ "description": "SHOW.utils.disp_img",
+ "peekOfCode": "def img_preprocess(img):\n # img: 0-1\n # img: tensor or ndarray\n # img: (1,c,h,w)\n # img: (c,h,w)\n # img: (h,w,c)\n # img: cpu or gpu\n # img: frad or no_grad\n if isinstance(img,torch.Tensor):\n img=img.cpu().detach()",
+ "detail": "SHOW.utils.disp_img",
+ "documentation": {}
+ },
+ {
+ "label": "show_PIL_im_window",
+ "kind": 2,
+ "importPath": "SHOW.utils.disp_img",
+ "description": "SHOW.utils.disp_img",
+ "peekOfCode": "def show_PIL_im_window(tensor):\n import PIL\n img=img_preprocess(tensor)\n if isinstance(img,torch.Tensor):\n img=img.numpy()\n scale=255/img.max()\n img = (img.copy() * scale)\n if img.shape[-1]==3:\n img=img[:, :, [2, 1, 0]]\n if img.shape[-1]==1:",
+ "detail": "SHOW.utils.disp_img",
+ "documentation": {}
+ },
+ {
+ "label": "save_tensor_to_file",
+ "kind": 2,
+ "importPath": "SHOW.utils.disp_img",
+ "description": "SHOW.utils.disp_img",
+ "peekOfCode": "def save_tensor_to_file(tensor, path='tensor.jpg'):\n if isinstance(tensor, torch.Tensor):\n tensor = tensor[0].detach().cpu().numpy()\n img = (tensor.transpose(1, 2, 0).copy() * 255)[:, :, [2, 1, 0]]\n img = np.minimum(np.maximum(img, 0), 255).astype(np.uint8)\n cv2.imwrite(path, img)\ndef show_plt_fig_face(datas):\n plt.figure()\n ax = plt.gca()\n ax.xaxis.set_ticks_position('top')",
+ "detail": "SHOW.utils.disp_img",
+ "documentation": {}
+ },
+ {
+ "label": "show_plt_fig_face",
+ "kind": 2,
+ "importPath": "SHOW.utils.disp_img",
+ "description": "SHOW.utils.disp_img",
+ "peekOfCode": "def show_plt_fig_face(datas):\n plt.figure()\n ax = plt.gca()\n ax.xaxis.set_ticks_position('top')\n ax.invert_yaxis()\n plt.axis('equal')\n plt.scatter(datas[:, 0], datas[:, 1])\n for i, p in enumerate(datas):\n plt.annotate(str(i), p)\n plt.show()",
+ "detail": "SHOW.utils.disp_img",
+ "documentation": {}
+ },
+ {
+ "label": "show_plt_fig_im",
+ "kind": 2,
+ "importPath": "SHOW.utils.disp_img",
+ "description": "SHOW.utils.disp_img",
+ "peekOfCode": "def show_plt_fig_im(X):\n X = X[0].detach().cpu().numpy()*255\n X = X.astype(np.uint8).transpose(1, 2, 0)\n plt.imshow(X)\ndef gen_cheers_board(\n im_size = 8,\n im_size2 = 8\n):\n a=np.zeros([im_size,im_size2])\n b=np.zeros([im_size,im_size2])",
+ "detail": "SHOW.utils.disp_img",
+ "documentation": {}
+ },
+ {
+ "label": "gen_cheers_board",
+ "kind": 2,
+ "importPath": "SHOW.utils.disp_img",
+ "description": "SHOW.utils.disp_img",
+ "peekOfCode": "def gen_cheers_board(\n im_size = 8,\n im_size2 = 8\n):\n a=np.zeros([im_size,im_size2])\n b=np.zeros([im_size,im_size2])\n for i in range(a.shape[0]):\n if i%2==0:\n a[i,:]=1\n else:",
+ "detail": "SHOW.utils.disp_img",
+ "documentation": {}
+ },
+ {
+ "label": "fig2data",
+ "kind": 2,
+ "importPath": "SHOW.utils.disp_img",
+ "description": "SHOW.utils.disp_img",
+ "peekOfCode": "def fig2data(fig):\n \"\"\"\n fig = plt.figure()\n image = fig2data(fig)\n @brief Convert a Matplotlib figure to a 4D numpy array with RGBA channels and return it\n @param fig a matplotlib figure\n @return a numpy 3D array of RGBA values\n \"\"\"\n import PIL.Image as Image\n # draw the renderer",
+ "detail": "SHOW.utils.disp_img",
+ "documentation": {}
+ },
+ {
+ "label": "ffmpeg_merge_api",
+ "kind": 2,
+ "importPath": "SHOW.utils.ffmpeg",
+ "description": "SHOW.utils.ffmpeg",
+ "peekOfCode": "def ffmpeg_merge_api(\n print_cmd=False,\n loglevel='error',\n fontcolor='red',\n out_name='out.mp4',\n ffmpeg_path='ffmpeg',\n font_file_path=DEFAULT_FONT_FILE_PATH,\n title2vpath_map=None,\n resolution=None,\n fontsize = 'h/15',",
+ "detail": "SHOW.utils.ffmpeg",
+ "documentation": {}
+ },
+ {
+ "label": "temporal_concat_video",
+ "kind": 2,
+ "importPath": "SHOW.utils.ffmpeg",
+ "description": "SHOW.utils.ffmpeg",
+ "peekOfCode": "def temporal_concat_video(\n input_path_list: List[str],\n input_title_list: List[str],\n output_path: str,\n resolution=(720, 1080 * 3),\n remove_raw_files: bool = False,\n disable_log: bool = False,\n font_file_path=DEFAULT_FONT_FILE_PATH,\n fontcolor='red',\n fontsize = 'h/20',",
+ "detail": "SHOW.utils.ffmpeg",
+ "documentation": {}
+ },
+ {
+ "label": "DEFAULT_FONT_FILE_PATH",
+ "kind": 5,
+ "importPath": "SHOW.utils.ffmpeg",
+ "description": "SHOW.utils.ffmpeg",
+ "peekOfCode": "DEFAULT_FONT_FILE_PATH = os.path.join(\n os.path.dirname(__file__), '../../../data/AdobeHeitiStd-Regular2.otf')\ndef ffmpeg_merge_api(\n print_cmd=False,\n loglevel='error',\n fontcolor='red',\n out_name='out.mp4',\n ffmpeg_path='ffmpeg',\n font_file_path=DEFAULT_FONT_FILE_PATH,\n title2vpath_map=None,",
+ "detail": "SHOW.utils.ffmpeg",
+ "documentation": {}
+ },
+ {
+ "label": "func_factory",
+ "kind": 6,
+ "importPath": "SHOW.utils.fun_factory",
+ "description": "SHOW.utils.fun_factory",
+ "peekOfCode": "class func_factory(object):\n def __init__(self,factory_name='task'):\n self.factory_name=factory_name\n self.__func_name_to_mem_map={}\n def register_module(self):\n def decorator(func):\n logger.info(f'{func.__name__} is registered')\n self.__func_name_to_mem_map[func.__name__] = func\n def wrapper(*args, **kwargs):\n func(*args, **kwargs)",
+ "detail": "SHOW.utils.fun_factory",
+ "documentation": {}
+ },
+ {
+ "label": "dict2obj",
+ "kind": 2,
+ "importPath": "SHOW.utils.fun_factory",
+ "description": "SHOW.utils.fun_factory",
+ "peekOfCode": "def dict2obj(d):\n if isinstance(d, list):\n d = [dict2obj(x) for x in d]\n if not isinstance(d, dict):\n return d\n class C(object):\n pass\n o = C()\n for k in d:\n o.__dict__[k] = dict2obj(d[k])",
+ "detail": "SHOW.utils.fun_factory",
+ "documentation": {}
+ },
+ {
+ "label": "AverageMeter",
+ "kind": 6,
+ "importPath": "SHOW.utils.metric",
+ "description": "SHOW.utils.metric",
+ "peekOfCode": "class AverageMeter:\n \"\"\"Track a series of values and provide access to smoothed values over a\n window or the global series average.\n \"\"\"\n def __init__(self, window_size=50):\n self._deque = deque(maxlen=window_size)\n self._total = 0.0\n self._count = 0\n def update(self, value):\n self._deque.append(value)",
+ "detail": "SHOW.utils.metric",
+ "documentation": {}
+ },
+ {
+ "label": "MeterBuffer",
+ "kind": 6,
+ "importPath": "SHOW.utils.metric",
+ "description": "SHOW.utils.metric",
+ "peekOfCode": "class MeterBuffer(defaultdict):\n \"\"\"Computes and stores the average and current value\"\"\"\n def __init__(self, window_size=20):\n factory = functools.partial(AverageMeter, window_size=window_size)\n super().__init__(factory)\n def reset(self):\n for v in self.values():\n v.reset()\n def get_filtered_meter(self, filter_key=\"time\"):\n return {k: v for k, v in self.items() if filter_key in k}",
+ "detail": "SHOW.utils.metric",
+ "documentation": {}
+ },
+ {
+ "label": "get_total_and_free_memory_in_Mb",
+ "kind": 2,
+ "importPath": "SHOW.utils.metric",
+ "description": "SHOW.utils.metric",
+ "peekOfCode": "def get_total_and_free_memory_in_Mb(cuda_device):\n devices_info_str = os.popen(\n \"nvidia-smi --query-gpu=memory.total,memory.used --format=csv,nounits,noheader\"\n )\n devices_info = devices_info_str.read().strip().split(\"\\n\")\n total, used = devices_info[int(cuda_device)].split(\",\")\n return int(total), int(used)\ndef occupy_mem(cuda_device, mem_ratio=0.9):\n \"\"\"\n pre-allocate gpu memory for training to avoid memory Fragmentation.",
+ "detail": "SHOW.utils.metric",
+ "documentation": {}
+ },
+ {
+ "label": "occupy_mem",
+ "kind": 2,
+ "importPath": "SHOW.utils.metric",
+ "description": "SHOW.utils.metric",
+ "peekOfCode": "def occupy_mem(cuda_device, mem_ratio=0.9):\n \"\"\"\n pre-allocate gpu memory for training to avoid memory Fragmentation.\n \"\"\"\n total, used = get_total_and_free_memory_in_Mb(cuda_device)\n max_mem = int(total * mem_ratio)\n block_mem = max_mem - used\n x = torch.cuda.FloatTensor(256, 1024, block_mem)\n del x\n time.sleep(5)",
+ "detail": "SHOW.utils.metric",
+ "documentation": {}
+ },
+ {
+ "label": "gpu_mem_usage",
+ "kind": 2,
+ "importPath": "SHOW.utils.metric",
+ "description": "SHOW.utils.metric",
+ "peekOfCode": "def gpu_mem_usage():\n \"\"\"\n Compute the GPU memory usage for the current device (MB).\n \"\"\"\n mem_usage_bytes = torch.cuda.max_memory_allocated()\n return mem_usage_bytes / (1024 * 1024)\nclass AverageMeter:\n \"\"\"Track a series of values and provide access to smoothed values over a\n window or the global series average.\n \"\"\"",
+ "detail": "SHOW.utils.metric",
+ "documentation": {}
+ },
+ {
+ "label": "__all__",
+ "kind": 5,
+ "importPath": "SHOW.utils.metric",
+ "description": "SHOW.utils.metric",
+ "peekOfCode": "__all__ = [\n \"AverageMeter\",\n \"MeterBuffer\",\n \"get_total_and_free_memory_in_Mb\",\n \"occupy_mem\",\n \"gpu_mem_usage\",\n]\ndef get_total_and_free_memory_in_Mb(cuda_device):\n devices_info_str = os.popen(\n \"nvidia-smi --query-gpu=memory.total,memory.used --format=csv,nounits,noheader\"",
+ "detail": "SHOW.utils.metric",
+ "documentation": {}
+ },
+ {
+ "label": "replace_spec_code",
+ "kind": 2,
+ "importPath": "SHOW.utils.misc",
+ "description": "SHOW.utils.misc",
+ "peekOfCode": "def replace_spec_code(in_name):\n disp_name=re.sub(\n re.compile(\n r'[^a-zA-Z0-9]'\n # r'[-,$()#+&*]'\n ),\n \"_\",\n in_name)\n return disp_name\ndef cvt_dict_to_tensor(data,device,dtype):",
+ "detail": "SHOW.utils.misc",
+ "documentation": {}
+ },
+ {
+ "label": "cvt_dict_to_tensor",
+ "kind": 2,
+ "importPath": "SHOW.utils.misc",
+ "description": "SHOW.utils.misc",
+ "peekOfCode": "def cvt_dict_to_tensor(data,device,dtype):\n if isinstance(data,list):\n return [cvt_dict_to_tensor(v,device,dtype) for v in data]\n elif isinstance(data,dict):\n return {k : (cvt_dict_to_tensor(v,device,dtype) if k!='seg_stack' else v)\n for k,v in data.items()}\n else:\n if isinstance(data,np.ndarray):\n return torch.from_numpy(data).to(device=device,dtype=dtype)\n elif isinstance(data,torch.Tensor):",
+ "detail": "SHOW.utils.misc",
+ "documentation": {}
+ },
+ {
+ "label": "expand_var_shape",
+ "kind": 2,
+ "importPath": "SHOW.utils.misc",
+ "description": "SHOW.utils.misc",
+ "peekOfCode": "def expand_var_shape(var,target_len=300,expand_axis=-1):\n if isinstance(var,np.ndarray):\n org_len=var.shape[expand_axis]\n if target_len==org_len:\n return\n oth_len=var.shape[:expand_axis]\n new_var=np.concatenate([var,np.zeros(*oth_len,target_len-org_len)],axis=expand_axis)\n return new_var\n if isinstance(var,torch.Tensor):\n org_len=var.shape[expand_axis]",
+ "detail": "SHOW.utils.misc",
+ "documentation": {}
+ },
+ {
+ "label": "str_to_torch_dtype",
+ "kind": 2,
+ "importPath": "SHOW.utils.misc",
+ "description": "SHOW.utils.misc",
+ "peekOfCode": "def str_to_torch_dtype(s):\n dtype = torch.float32\n if s == 'float64':\n dtype = torch.float64\n elif s == 'float32':\n dtype = torch.float32\n return dtype\ndef reload_module(s:str='SHOW.smplx_dataset'):\n import imp\n eval(f'import {s} as target')",
+ "detail": "SHOW.utils.misc",
+ "documentation": {}
+ },
+ {
+ "label": "reload_module",
+ "kind": 2,
+ "importPath": "SHOW.utils.misc",
+ "description": "SHOW.utils.misc",
+ "peekOfCode": "def reload_module(s:str='SHOW.smplx_dataset'):\n import imp\n eval(f'import {s} as target')\n imp.reload(locals()['target'])\ndef print_args(args:dict):\n print(\"################################ args ################################\")\n for k, v in args.__dict__.items():\n print(\"{0: <10}\\t{1: <30}\\t{2: <20}\".format(k, str(v), str(type(v))))\n print(\"########################################################################\")\ndef print_dict_losses(losses):",
+ "detail": "SHOW.utils.misc",
+ "documentation": {}
+ },
+ {
+ "label": "print_args",
+ "kind": 2,
+ "importPath": "SHOW.utils.misc",
+ "description": "SHOW.utils.misc",
+ "peekOfCode": "def print_args(args:dict):\n print(\"################################ args ################################\")\n for k, v in args.__dict__.items():\n print(\"{0: <10}\\t{1: <30}\\t{2: <20}\".format(k, str(v), str(type(v))))\n print(\"########################################################################\")\ndef print_dict_losses(losses):\n return reduce(\n lambda a, b: a + f' {b}={round(losses[b].item(), 4)}', \n [\"\"] + list(losses.keys())\n )",
+ "detail": "SHOW.utils.misc",
+ "documentation": {}
+ },
+ {
+ "label": "print_dict_losses",
+ "kind": 2,
+ "importPath": "SHOW.utils.misc",
+ "description": "SHOW.utils.misc",
+ "peekOfCode": "def print_dict_losses(losses):\n return reduce(\n lambda a, b: a + f' {b}={round(losses[b].item(), 4)}', \n [\"\"] + list(losses.keys())\n )\ndef platform_init():\n import platform\n if platform.system() == \"Linux\":\n os.environ['PYOPENGL_PLATFORM'] = 'egl'\n else:",
+ "detail": "SHOW.utils.misc",
+ "documentation": {}
+ },
+ {
+ "label": "platform_init",
+ "kind": 2,
+ "importPath": "SHOW.utils.misc",
+ "description": "SHOW.utils.misc",
+ "peekOfCode": "def platform_init():\n import platform\n if platform.system() == \"Linux\":\n os.environ['PYOPENGL_PLATFORM'] = 'egl'\n else:\n if 'PYOPENGL_PLATFORM' in os.environ:\n os.environ.__delitem__('PYOPENGL_PLATFORM')\ndef work_seek_init(rank = 42):\n import torch\n import torch.backends.cudnn",
+ "detail": "SHOW.utils.misc",
+ "documentation": {}
+ },
+ {
+ "label": "work_seek_init",
+ "kind": 2,
+ "importPath": "SHOW.utils.misc",
+ "description": "SHOW.utils.misc",
+ "peekOfCode": "def work_seek_init(rank = 42):\n import torch\n import torch.backends.cudnn\n import numpy as np\n os.environ[\"OPENCV_IO_ENABLE_OPENEXR\"] = \"1\"\n torch.manual_seed(rank)\n torch.cuda.manual_seed(rank)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n np.random.seed(rank)",
+ "detail": "SHOW.utils.misc",
+ "documentation": {}
+ },
+ {
+ "label": "get_gpu_info",
+ "kind": 2,
+ "importPath": "SHOW.utils.misc",
+ "description": "SHOW.utils.misc",
+ "peekOfCode": "def get_gpu_info():\n try:\n import pynvml\n pynvml.nvmlInit()\n gpu_count= pynvml.nvmlDeviceGetCount()\n handle = pynvml.nvmlDeviceGetHandleByIndex(0)\n gpu_name=pynvml.nvmlDeviceGetName(handle)\n gpu_version=pynvml.nvmlSystemGetDriverVersion()\n info = pynvml.nvmlDeviceGetMemoryInfo(handle)\n B_to_MB=1024*1024",
+ "detail": "SHOW.utils.misc",
+ "documentation": {}
+ },
+ {
+ "label": "get_machine_info",
+ "kind": 2,
+ "importPath": "SHOW.utils.misc",
+ "description": "SHOW.utils.misc",
+ "peekOfCode": "def get_machine_info():\n host_name=platform.node()\n gpu_info = get_gpu_info()\n machine_info=dict(\n host_name=host_name,\n gpu_info=gpu_info,\n )\n return machine_info",
+ "detail": "SHOW.utils.misc",
+ "documentation": {}
+ },
+ {
+ "label": "ObjectFactory",
+ "kind": 6,
+ "importPath": "SHOW.utils.obj_factory",
+ "description": "SHOW.utils.obj_factory",
+ "peekOfCode": "class ObjectFactory:\n def add_to_parser(self, parser):\n raise NotImplementedError()\n def from_dict(self, arguments):\n raise NotImplementedError()\nclass EmptyFactory(ObjectFactory):\n \"\"\"An EmptyFactory simply returns the passed in object.\"\"\"\n def __init__(self, _object):\n self._object = _object\n def add_to_parser(self, parser):",
+ "detail": "SHOW.utils.obj_factory",
+ "documentation": {}
+ },
+ {
+ "label": "EmptyFactory",
+ "kind": 6,
+ "importPath": "SHOW.utils.obj_factory",
+ "description": "SHOW.utils.obj_factory",
+ "peekOfCode": "class EmptyFactory(ObjectFactory):\n \"\"\"An EmptyFactory simply returns the passed in object.\"\"\"\n def __init__(self, _object):\n self._object = _object\n def add_to_parser(self, parser):\n pass\n def from_dict(self, arguments):\n return self._object\nclass FactoryList(ObjectFactory):\n def __init__(self, factories):",
+ "detail": "SHOW.utils.obj_factory",
+ "documentation": {}
+ },
+ {
+ "label": "FactoryList",
+ "kind": 6,
+ "importPath": "SHOW.utils.obj_factory",
+ "description": "SHOW.utils.obj_factory",
+ "peekOfCode": "class FactoryList(ObjectFactory):\n def __init__(self, factories):\n self.factories = factories\n def add_to_parser(self, parser):\n for f in self.factories:\n f.add_to_parser(parser)\n def from_dict(self, arguments):\n return [\n f.from_dict(arguments)\n for f in self.factories",
+ "detail": "SHOW.utils.obj_factory",
+ "documentation": {}
+ },
+ {
+ "label": "CallableFactory",
+ "kind": 6,
+ "importPath": "SHOW.utils.obj_factory",
+ "description": "SHOW.utils.obj_factory",
+ "peekOfCode": "class CallableFactory(ObjectFactory):\n \"\"\"CallableFactory creates an ObjectFactory instance from a callable using\n Python's reflection to define the arguments, argument types and default\n parameters.\"\"\"\n def __init__(self, func, namespace=\"\"):\n self._func = func\n self._signature = signature(self._func)\n self._namespace = namespace\n @property\n def arg_pattern(self):",
+ "detail": "SHOW.utils.obj_factory",
+ "documentation": {}
+ },
+ {
+ "label": "run_openpose",
+ "kind": 2,
+ "importPath": "SHOW.utils.op_utils",
+ "description": "SHOW.utils.op_utils",
+ "peekOfCode": "def run_openpose(\n openpose_root_path,\n openpose_bin_path,\n img_dir,\n out_dir,\n video_out=None,\n img_out=None,\n low_res=False,\n limit_num=False\n):",
+ "detail": "SHOW.utils.op_utils",
+ "documentation": {}
+ },
+ {
+ "label": "set_op_build_version",
+ "kind": 2,
+ "importPath": "SHOW.utils.op_utils",
+ "description": "SHOW.utils.op_utils",
+ "peekOfCode": "def set_op_build_version(cfg,version='build_Q6000'):\n cfg.openpose_bin_path=os.path.join(\n cfg.openpose_root_path,version,cfg.openpose_bin_path_str\n )\n # assert(Path(cfg.openpose_bin_path).exists())\n if not Path(cfg.openpose_bin_path).exists():\n logger.warning(f'openpose_bin_path not exist: {cfg.openpose_bin_path}')\ndef set_op_path_by_gpu_name(cfg,gpu_name='Q6000'):\n for k,v in cfg.gpu_name2op_build_map.items():\n if k in gpu_name:",
+ "detail": "SHOW.utils.op_utils",
+ "documentation": {}
+ },
+ {
+ "label": "set_op_path_by_gpu_name",
+ "kind": 2,
+ "importPath": "SHOW.utils.op_utils",
+ "description": "SHOW.utils.op_utils",
+ "peekOfCode": "def set_op_path_by_gpu_name(cfg,gpu_name='Q6000'):\n for k,v in cfg.gpu_name2op_build_map.items():\n if k in gpu_name:\n set_op_build_version(cfg,v)\n return\n # raise RuntimeError \n logger.warning(f'gpu_name has not build version: {gpu_name}')",
+ "detail": "SHOW.utils.op_utils",
+ "documentation": {}
+ },
+ {
+ "label": "get_file_size",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def get_file_size(fpath):\n statinfo = os.stat(fpath)\n size = statinfo.st_size\n return size\ndef recursive_walk(rootdir):\n \"\"\"\n Yields:\n str: All files in rootdir, recursively.\n \"\"\"\n for r, dirs, files in os.walk(rootdir):",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "recursive_walk",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def recursive_walk(rootdir):\n \"\"\"\n Yields:\n str: All files in rootdir, recursively.\n \"\"\"\n for r, dirs, files in os.walk(rootdir):\n for f in files:\n yield os.path.join(r, f)\ndef glob_exts_in_path(path, img_ext=['png', 'jpg']):\n return reduce(",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "glob_exts_in_path",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def glob_exts_in_path(path, img_ext=['png', 'jpg']):\n return reduce(\n lambda before, ext: before+glob.glob(\n os.path.join(path, f'*.{ext}')\n ),\n [[]]+img_ext)\ndef find_full_impath_by_name(root, name):\n for ext in ['jpg', 'png', 'bmp', 'jpeg']:\n input_img = os.path.join(root, f'{name}.{ext}')\n if Path(input_img).exists():",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "find_full_impath_by_name",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def find_full_impath_by_name(root, name):\n for ext in ['jpg', 'png', 'bmp', 'jpeg']:\n input_img = os.path.join(root, f'{name}.{ext}')\n if Path(input_img).exists():\n return input_img\n return None\ndef files_num_in_dir(dir_name):\n if not Path(dir_name).exists():\n return -1\n return len(os.listdir(dir_name))",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "files_num_in_dir",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def files_num_in_dir(dir_name):\n if not Path(dir_name).exists():\n return -1\n return len(os.listdir(dir_name))\ndef ext_files_num_in_dir(dir_name, exts=['*.pkl', '*.pkl.empty']):\n if not Path(dir_name).exists():\n return -1\n all_list = []\n for ext in exts:\n all_list += glob.glob(os.path.join(dir_name, ext))",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "ext_files_num_in_dir",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def ext_files_num_in_dir(dir_name, exts=['*.pkl', '*.pkl.empty']):\n if not Path(dir_name).exists():\n return -1\n all_list = []\n for ext in exts:\n all_list += glob.glob(os.path.join(dir_name, ext))\n return len(all_list)\ndef img_files_num_in_dir(dir_name):\n if not Path(dir_name).exists():\n return -1",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "img_files_num_in_dir",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def img_files_num_in_dir(dir_name):\n if not Path(dir_name).exists():\n return -1\n i = glob.glob(os.path.join(dir_name, '*.jpg')) +\\\n glob.glob(os.path.join(dir_name, '*.png'))\n return len(i)\ndef is_empty_dir(dir_name):\n if not os.path.exists(dir_name):\n return 1\n return int(files_num_in_dir(dir_name) == 0)",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "is_empty_dir",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def is_empty_dir(dir_name):\n if not os.path.exists(dir_name):\n return 1\n return int(files_num_in_dir(dir_name) == 0)\ndef is_notexist_file(dir_name):\n return 0 if os.path.exists(dir_name) else 1\ndef purge_dir(target_dir):\n if os.path.exists(target_dir):\n if os.path.isfile(target_dir):\n os.remove(target_dir)",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "is_notexist_file",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def is_notexist_file(dir_name):\n return 0 if os.path.exists(dir_name) else 1\ndef purge_dir(target_dir):\n if os.path.exists(target_dir):\n if os.path.isfile(target_dir):\n os.remove(target_dir)\n else:\n shutil.rmtree(target_dir)\n # os.makedirs(target_dir, exist_ok=True)\ndef check_makedir(dir):",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "purge_dir",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def purge_dir(target_dir):\n if os.path.exists(target_dir):\n if os.path.isfile(target_dir):\n os.remove(target_dir)\n else:\n shutil.rmtree(target_dir)\n # os.makedirs(target_dir, exist_ok=True)\ndef check_makedir(dir):\n dir = osp.abspath(dir)\n root = osp.dirname(dir)",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "check_makedir",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def check_makedir(dir):\n dir = osp.abspath(dir)\n root = osp.dirname(dir)\n if not os.path.exists(root):\n os.makedirs(root, exist_ok=True)\ndef remake_dir(root):\n if os.path.exists(root):\n shutil.rmtree(root)\n os.makedirs(root, exist_ok=True)\ndef parse_abs_path(cureent_file_path, path):",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "remake_dir",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def remake_dir(root):\n if os.path.exists(root):\n shutil.rmtree(root)\n os.makedirs(root, exist_ok=True)\ndef parse_abs_path(cureent_file_path, path):\n return os.path.abspath(\n os.path.join(\n os.path.dirname(cureent_file_path), path)\n )\ndef from_rela_path(cureent_file_path, path):",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "parse_abs_path",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def parse_abs_path(cureent_file_path, path):\n return os.path.abspath(\n os.path.join(\n os.path.dirname(cureent_file_path), path)\n )\ndef from_rela_path(cureent_file_path, path):\n return mmcv.Config.fromfile(\n parse_abs_path(cureent_file_path, path)\n )",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "from_rela_path",
+ "kind": 2,
+ "importPath": "SHOW.utils.paths",
+ "description": "SHOW.utils.paths",
+ "peekOfCode": "def from_rela_path(cureent_file_path, path):\n return mmcv.Config.fromfile(\n parse_abs_path(cureent_file_path, path)\n )",
+ "detail": "SHOW.utils.paths",
+ "documentation": {}
+ },
+ {
+ "label": "View",
+ "kind": 6,
+ "importPath": "SHOW.utils.render",
+ "description": "SHOW.utils.render",
+ "peekOfCode": "class View(Enum):\n GROUND_TRUTH = 1\n COLOR_OVERLAY = 2\n SHAPE_OVERLAY = 4\n SHAPE = 8\n LANDMARKS = 16\n HEATMAP = 32\n DEPTH = 64\ndef render_shape(vertices, flame_faces, mesh_rasterizer, debug_renderer, cameras, white=True):\n # mesh_file=('./../data/head_template_mesh.obj')",
+ "detail": "SHOW.utils.render",
+ "documentation": {}
+ },
+ {
+ "label": "render_shape",
+ "kind": 2,
+ "importPath": "SHOW.utils.render",
+ "description": "SHOW.utils.render",
+ "peekOfCode": "def render_shape(vertices, flame_faces, mesh_rasterizer, debug_renderer, cameras, white=True):\n # mesh_file=('./../data/head_template_mesh.obj')\n # flame_faces = load_obj(mesh_file)[1]\n # mesh_rasterizer: MeshRasterizer\n # debug_renderer: MeshRenderer\n # cameras: PerspectiveCameras\n B = vertices.shape[0]\n V = vertices.shape[1]\n faces = flame_faces.verts_idx.cuda()[None].repeat(B, 1, 1)\n if not white:",
+ "detail": "SHOW.utils.render",
+ "documentation": {}
+ },
+ {
+ "label": "IterTimer",
+ "kind": 6,
+ "importPath": "SHOW.utils.timer",
+ "description": "SHOW.utils.timer",
+ "peekOfCode": "class IterTimer:\n def __init__(self, name='time', sync=True, enabled=True,print_block=True):\n self.ll=20\n self.name = name\n self.times = []\n self.timer = Timer(start=False)\n self.sync = sync\n self.enabled = enabled\n self.print_block=print_block\n def __enter__(self):",
+ "detail": "SHOW.utils.timer",
+ "documentation": {}
+ },
+ {
+ "label": "IterTimers",
+ "kind": 6,
+ "importPath": "SHOW.utils.timer",
+ "description": "SHOW.utils.timer",
+ "peekOfCode": "class IterTimers(dict):\n def __init__(self, *args, **kwargs):\n super(IterTimers, self).__init__(*args, **kwargs)\n # self.register_list=[]\n def disable_all(self):\n for timer in self.values():\n timer.enabled = False\n def enable_all(self):\n for timer in self.values():\n timer.enabled = True",
+ "detail": "SHOW.utils.timer",
+ "documentation": {}
+ },
+ {
+ "label": "tic",
+ "kind": 2,
+ "importPath": "SHOW.utils.timer",
+ "description": "SHOW.utils.timer",
+ "peekOfCode": "def tic():\n global start_time\n start_time = time.time()\n return start_time\ndef toc():\n if 'start_time' in globals():\n end_time = time.time()\n return end_time - start_time\n else:\n return None",
+ "detail": "SHOW.utils.timer",
+ "documentation": {}
+ },
+ {
+ "label": "toc",
+ "kind": 2,
+ "importPath": "SHOW.utils.timer",
+ "description": "SHOW.utils.timer",
+ "peekOfCode": "def toc():\n if 'start_time' in globals():\n end_time = time.time()\n return end_time - start_time\n else:\n return None\n@contextlib.contextmanager\ndef test_time():\n st=time.time()\n yield True",
+ "detail": "SHOW.utils.timer",
+ "documentation": {}
+ },
+ {
+ "label": "test_time",
+ "kind": 2,
+ "importPath": "SHOW.utils.timer",
+ "description": "SHOW.utils.timer",
+ "peekOfCode": "def test_time():\n st=time.time()\n yield True\n et=time.time()\n logger.info(f'used time: {et-st}')\ndef timeit(func):\n def _warp(*args, **kwargs):\n start_time = time.time()\n result = func(*args, **kwargs)\n elastic_time = time.time() - start_time",
+ "detail": "SHOW.utils.timer",
+ "documentation": {}
+ },
+ {
+ "label": "timeit",
+ "kind": 2,
+ "importPath": "SHOW.utils.timer",
+ "description": "SHOW.utils.timer",
+ "peekOfCode": "def timeit(func):\n def _warp(*args, **kwargs):\n start_time = time.time()\n result = func(*args, **kwargs)\n elastic_time = time.time() - start_time\n print(\"The execution time of the function '%s' is %.6fs\\n\" % (\n func.__name__, elastic_time))\n return result\n return _warp\nclass IterTimer:",
+ "detail": "SHOW.utils.timer",
+ "documentation": {}
+ },
+ {
+ "label": "default_timers",
+ "kind": 5,
+ "importPath": "SHOW.utils.timer",
+ "description": "SHOW.utils.timer",
+ "peekOfCode": "default_timers = IterTimers()",
+ "detail": "SHOW.utils.timer",
+ "documentation": {}
+ },
+ {
+ "label": "video",
+ "kind": 2,
+ "importPath": "SHOW.utils.video",
+ "description": "SHOW.utils.video",
+ "peekOfCode": "def video():\n for actor in tqdm(filter(os.path.isdir, glob(f'./test_results/*_rgb_*'))):\n os.system(f'ffmpeg -y -framerate 30 -pattern_type glob -i \\'{actor}/video/*.jpg\\' -c:v libx264 {actor}.mp4')\n@logger.catch\ndef video_to_images(\n vid_file,\n prefix='',\n start=0,\n duration=10,\n img_folder=None,",
+ "detail": "SHOW.utils.video",
+ "documentation": {}
+ },
+ {
+ "label": "video_to_images",
+ "kind": 2,
+ "importPath": "SHOW.utils.video",
+ "description": "SHOW.utils.video",
+ "peekOfCode": "def video_to_images(\n vid_file,\n prefix='',\n start=0,\n duration=10,\n img_folder=None,\n return_info=False,\n fps=15\n):\n '''",
+ "detail": "SHOW.utils.video",
+ "documentation": {}
+ },
+ {
+ "label": "frame_to_video",
+ "kind": 2,
+ "importPath": "SHOW.utils.video",
+ "description": "SHOW.utils.video",
+ "peekOfCode": "def frame_to_video(\n image_path,\n video_name,\n fps=9,\n cut=-1,\n size = (960,540)\n):\n # fps=15,\n # size = (960,540)\n # size = (480,720)",
+ "detail": "SHOW.utils.video",
+ "documentation": {}
+ },
+ {
+ "label": "images_to_sorted_images",
+ "kind": 2,
+ "importPath": "SHOW.utils.video",
+ "description": "SHOW.utils.video",
+ "peekOfCode": "def images_to_sorted_images(input_folder, output_folder, img_format='%06d'):\n \"\"\"Copy and rename a folder of images into a new folder following the\n `img_format`.\n Args:\n input_folder (str): input folder.\n output_folder (str): output folder.\n img_format (str, optional): image format name, do not need extension.\n Defaults to '%06d'.\n Returns:\n str: image format of the rename images.",
+ "detail": "SHOW.utils.video",
+ "documentation": {}
+ },
+ {
+ "label": "images_to_video",
+ "kind": 2,
+ "importPath": "SHOW.utils.video",
+ "description": "SHOW.utils.video",
+ "peekOfCode": "def images_to_video(input_folder: str,\n output_path: str,\n remove_raw_file: bool = False,\n img_format: str = '%06d.png',\n fps: Union[int, float] = 30,\n resolution: Optional[Union[Tuple[int, int],\n Tuple[float, float]]] = None,\n start: int = 0,\n end: Optional[int] = None,\n disable_log: bool = False) -> None:",
+ "detail": "SHOW.utils.video",
+ "documentation": {}
+ },
+ {
+ "label": "deeplab_seg",
+ "kind": 6,
+ "importPath": "SHOW.video_filter.deeplab_seg",
+ "description": "SHOW.video_filter.deeplab_seg",
+ "peekOfCode": "class deeplab_seg(object):\n def __init__(self):\n self.device = torch.device(\n 'cuda:0') if torch.cuda.is_available() else torch.device('cpu')\n self.deeplab_model = torch.hub.load('pytorch/vision:v0.6.0',\n 'deeplabv3_resnet101',\n pretrained=True).to(self.device)\n self.deeplab_model.eval()\n self.deeplab_preprocess = transforms.Compose([\n transforms.ToTensor(),",
+ "detail": "SHOW.video_filter.deeplab_seg",
+ "documentation": {}
+ },
+ {
+ "label": "MMPoseAnalyzer",
+ "kind": 6,
+ "importPath": "SHOW.video_filter.MMposer",
+ "description": "SHOW.video_filter.MMposer",
+ "peekOfCode": "class MMPoseAnalyzer():\n def __init__(self):\n from mmpose.apis import (inference_top_down_pose_model, init_pose_model,\n vis_pose_result, process_mmdet_results)\n from mmdet.apis import inference_detector, init_detector\n import os\n mmpose_root = os.environ.get('mmpose_root')\n pose_config = os.path.join(mmpose_root,'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192.py')\n det_config = os.path.join(mmpose_root,'demo/mmdetection_cfg/faster_rcnn_r50_fpn_coco.py')\n pose_checkpoint = 'https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'",
+ "detail": "SHOW.video_filter.MMposer",
+ "documentation": {}
+ },
+ {
+ "label": "sitting_pose",
+ "kind": 5,
+ "importPath": "SHOW.constants",
+ "description": "SHOW.constants",
+ "peekOfCode": "sitting_pose = [\n 0.0, 0.0, 0.0, -1.1826512813568115, 0.23866955935955048, 0.15146760642528534, -1.2604516744613647,\n -0.3160211145877838, -0.1603458970785141, 0.0, 0.0, 0.0, 1.1654603481292725, 0.0, 0.0,\n 1.2521806955337524, 0.041598282754421234, -0.06312154978513718, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,",
+ "detail": "SHOW.constants",
+ "documentation": {}
+ },
+ {
+ "label": "lmk2d_to_bbox",
+ "kind": 2,
+ "importPath": "SHOW.image",
+ "description": "SHOW.image",
+ "peekOfCode": "def lmk2d_to_bbox(lmks,h,w,bb_scale=2.0):\n # lmks:68,2\n x_min, x_max, y_min, y_max = np.min(lmks[:, 0]), np.max(lmks[:, 0]), np.min(lmks[:, 1]), np.max(lmks[:, 1])\n x_center, y_center = int((x_max + x_min) / 2.0), int((y_max + y_min) / 2.0)\n size = int(bb_scale * 2 * max(x_center - x_min, y_center - y_min))\n xb_min, xb_max, yb_min, yb_max = max(x_center - size // 2, 0), min(x_center + size // 2, w - 1), \\\n max(y_center - size // 2, 0), min(y_center + size // 2, h - 1)\n yb_max = min(yb_max, h-1)\n xb_max = min(xb_max, w-1)\n yb_min = max(yb_min, 0)",
+ "detail": "SHOW.image",
+ "documentation": {}
+ },
+ {
+ "label": "landmark_crop",
+ "kind": 2,
+ "importPath": "SHOW.image",
+ "description": "SHOW.image",
+ "peekOfCode": "def landmark_crop(image, lmks, dense_lmk, bb_scale=2.0):\n h, w = image.shape[1:]\n xb_min,yb_min, xb_max,yb_max=lmk2d_to_bbox(lmks,h,w,bb_scale=bb_scale)\n if (xb_max - xb_min) % 2 != 0:\n xb_min += 1\n if (yb_max - yb_min) % 2 != 0:\n yb_min += 1\n cropped_image = crop_image(image, xb_min, yb_min, xb_max, yb_max)\n cropped_image_lmks = np.vstack((lmks[:, 0] - xb_min, lmks[:, 1] - yb_min)).T\n cropped_dense_lmk = np.vstack((dense_lmk[:, 0] - xb_min, dense_lmk[:, 1] - yb_min)).T",
+ "detail": "SHOW.image",
+ "documentation": {}
+ },
+ {
+ "label": "crop_image",
+ "kind": 2,
+ "importPath": "SHOW.image",
+ "description": "SHOW.image",
+ "peekOfCode": "def crop_image(image, x_min, y_min, x_max, y_max):\n # image：C,H,W or c,y,x\n return image[:, max(y_min, 0):min(y_max, image.shape[1] - 1), max(x_min, 0):min(x_max, image.shape[2] - 1)]\ndef squarefiy(image, lmk, dense_lmk, size=512):\n _, h, w = image.shape\n px = py = 0\n max_wh = max(w, h)\n if w != h:\n px = int((max_wh - w) / 2)\n py = int((max_wh - h) / 2)",
+ "detail": "SHOW.image",
+ "documentation": {}
+ },
+ {
+ "label": "squarefiy",
+ "kind": 2,
+ "importPath": "SHOW.image",
+ "description": "SHOW.image",
+ "peekOfCode": "def squarefiy(image, lmk, dense_lmk, size=512):\n _, h, w = image.shape\n px = py = 0\n max_wh = max(w, h)\n if w != h:\n px = int((max_wh - w) / 2)\n py = int((max_wh - h) / 2)\n image = F.pad(image, (px, px, py, py), 'constant', 0)\n img = F.interpolate(image[None], (size, size), mode='bilinear', align_corners=False)[0]\n if False:",
+ "detail": "SHOW.image",
+ "documentation": {}
+ },
+ {
+ "label": "tensor2im",
+ "kind": 2,
+ "importPath": "SHOW.image",
+ "description": "SHOW.image",
+ "peekOfCode": "def tensor2im(input_image, imtype=np.uint8):\n if isinstance(input_image, torch.Tensor):\n input_image = torch.clamp(input_image, -1.0, 1.0)\n image_tensor = input_image.data\n else:\n return input_image.reshape(3, 512, 512).transpose()\n image_numpy = image_tensor[0].cpu().float().numpy()\n if image_numpy.shape[0] == 1:\n image_numpy = np.tile(image_numpy, (3, 1, 1))\n image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0",
+ "detail": "SHOW.image",
+ "documentation": {}
+ },
+ {
+ "label": "get_heatmap",
+ "kind": 2,
+ "importPath": "SHOW.image",
+ "description": "SHOW.image",
+ "peekOfCode": "def get_heatmap( values):\n import cv2\n l2 = tensor2im(values)\n l2 = cv2.cvtColor(l2, cv2.COLOR_RGB2BGR)\n l2 = cv2.normalize(l2, None, 0, 255, cv2.NORM_MINMAX)\n heatmap = cv2.applyColorMap(l2, cv2.COLORMAP_JET)\n heatmap = cv2.cvtColor(cv2.addWeighted(heatmap, 0.75, l2, 0.25, 0).astype(np.uint8), cv2.COLOR_BGR2RGB) / 255.\n heatmap = torch.from_numpy(heatmap).permute(2, 0, 1)\n return heatmap\ndef crop_image_bbox(image, lmks, dense_lmk, bbox):",
+ "detail": "SHOW.image",
+ "documentation": {}
+ },
+ {
+ "label": "crop_image_bbox",
+ "kind": 2,
+ "importPath": "SHOW.image",
+ "description": "SHOW.image",
+ "peekOfCode": "def crop_image_bbox(image, lmks, dense_lmk, bbox):\n xb_min = bbox['xb_min']\n yb_min = bbox['yb_min']\n xb_max = bbox['xb_max']\n yb_max = bbox['yb_max']\n cropped = crop_image(image, xb_min, yb_min, xb_max, yb_max)\n cropped_image_lmks = np.vstack((lmks[:, 0] - xb_min, lmks[:, 1] - yb_min)).T\n cropped_image_dense_lmk = np.vstack((dense_lmk[:, 0] - xb_min, dense_lmk[:, 1] - yb_min)).T\n return cropped_image_lmks, cropped_image_dense_lmk, cropped",
+ "detail": "SHOW.image",
+ "documentation": {}
+ },
+ {
+ "label": "get_possible_person",
+ "kind": 2,
+ "importPath": "SHOW.load_assets",
+ "description": "SHOW.load_assets",
+ "peekOfCode": "def get_possible_person(poser, template_im):\n def is_small_person(bbox, org_im: np.ndarray):\n img_height, img_width, _ = org_im.shape\n box_height = bbox[3] - bbox[1]\n box_width = bbox[2] - bbox[0]\n is_small_person = 0\n if ((box_height / img_height) < 0.4 or (box_width / img_width) < 0.3):\n is_small_person = 1\n return is_small_person\n def is_kpts_whole(kpts):",
+ "detail": "SHOW.load_assets",
+ "documentation": {}
+ },
+ {
+ "label": "read_shape",
+ "kind": 2,
+ "importPath": "SHOW.load_assets",
+ "description": "SHOW.load_assets",
+ "peekOfCode": "def read_shape(speaker_ply_file_path):\n # return： (5023, 3)\n ply_data = PlyData.read(speaker_ply_file_path)\n speaker_shape = np.stack([\n ply_data['vertex']['x'], ply_data['vertex']['y'],\n ply_data['vertex']['z']\n ], 1)\n return speaker_shape\ndef load_assets(config, face_ider=None, template_im=None, **kwargs):\n assets_root = config.assets_root",
+ "detail": "SHOW.load_assets",
+ "documentation": {}
+ },
+ {
+ "label": "load_assets",
+ "kind": 2,
+ "importPath": "SHOW.load_assets",
+ "description": "SHOW.load_assets",
+ "peekOfCode": "def load_assets(config, face_ider=None, template_im=None, **kwargs):\n assets_root = config.assets_root\n dtype = config.dtype\n device = config.device\n ret_dict = EasyDict({})\n shape_res_factory_dir = f'{assets_root}/id_pic/shape_factory2'\n emb_res_factory_dir = f'{assets_root}/id_pic/{config.ider_cfg.npy_folder_name}'\n emb_res_factory_path = f'{assets_root}/id_pic/emb_factory2.pkl'\n emb_res_factory_is_changed_flag = False\n if Path(emb_res_factory_path).exists():",
+ "detail": "SHOW.load_assets",
+ "documentation": {}
+ },
+ {
+ "label": "JointMapper",
+ "kind": 6,
+ "importPath": "SHOW.load_models",
+ "description": "SHOW.load_models",
+ "peekOfCode": "class JointMapper(nn.Module):\n def __init__(self, joint_maps=None):\n super().__init__()\n self.register_buffer('joint_maps',\n torch.tensor(joint_maps, dtype=torch.long))\n def forward(self, joints, **kwargs):\n return torch.index_select(joints, 1, self.joint_maps)\ndef load_save_pkl(ours_pkl_file_path, device='cuda'):\n data = mmcv.load(ours_pkl_file_path)[0]\n for key in data.keys():",
+ "detail": "SHOW.load_models",
+ "documentation": {}
+ },
+ {
+ "label": "load_save_pkl",
+ "kind": 2,
+ "importPath": "SHOW.load_models",
+ "description": "SHOW.load_models",
+ "peekOfCode": "def load_save_pkl(ours_pkl_file_path, device='cuda'):\n data = mmcv.load(ours_pkl_file_path)[0]\n for key in data.keys():\n if isinstance(data[key], np.ndarray):\n data[key] = torch.from_numpy(data[key]).to(device)\n data['batch_size'] = data['expression'].shape[0]\n return data\ndef load_smplx_model(device='cuda', **kwargs):\n body_model = smplx.create(joint_mapper=JointMapper(\n op_dataset.smpl_to_openpose()),",
+ "detail": "SHOW.load_models",
+ "documentation": {}
+ },
+ {
+ "label": "load_smplx_model",
+ "kind": 2,
+ "importPath": "SHOW.load_models",
+ "description": "SHOW.load_models",
+ "peekOfCode": "def load_smplx_model(device='cuda', **kwargs):\n body_model = smplx.create(joint_mapper=JointMapper(\n op_dataset.smpl_to_openpose()),\n **DEFAULT_SMPLX_CONFIG,\n **kwargs).to(device=device)\n return body_model\ndef load_vposer_model(device='cuda', vposer_ckpt=''):\n vposer_ckpt = osp.expandvars(vposer_ckpt)\n vposer, _ = load_vposer(vposer_ckpt, vp_model='snapshot')\n vposer = vposer.to(device=device)",
+ "detail": "SHOW.load_models",
+ "documentation": {}
+ },
+ {
+ "label": "load_vposer_model",
+ "kind": 2,
+ "importPath": "SHOW.load_models",
+ "description": "SHOW.load_models",
+ "peekOfCode": "def load_vposer_model(device='cuda', vposer_ckpt=''):\n vposer_ckpt = osp.expandvars(vposer_ckpt)\n vposer, _ = load_vposer(vposer_ckpt, vp_model='snapshot')\n vposer = vposer.to(device=device)\n vposer.eval()\n return vposer",
+ "detail": "SHOW.load_models",
+ "documentation": {}
+ },
+ {
+ "label": "DEFAULT_SMPLX_CONFIG2",
+ "kind": 5,
+ "importPath": "SHOW.load_models",
+ "description": "SHOW.load_models",
+ "peekOfCode": "DEFAULT_SMPLX_CONFIG2 = dict(\n dtype=torch.float32,\n num_betas=200,\n num_expression_coeffs=50,\n num_pca_comps=12,\n flat_hand_mean=True,\n use_pca=True,\n model_type='smplx',\n use_face_contour=True,\n)",
+ "detail": "SHOW.load_models",
+ "documentation": {}
+ },
+ {
+ "label": "DEFAULT_SMPLX_CONFIG",
+ "kind": 5,
+ "importPath": "SHOW.load_models",
+ "description": "SHOW.load_models",
+ "peekOfCode": "DEFAULT_SMPLX_CONFIG = dict(\n create_global_orient=True,\n create_body_pose=True,\n create_betas=True,\n create_left_hand_pose=True,\n create_right_hand_pose=True,\n create_expression=True,\n create_jaw_pose=True,\n create_leye_pose=True,\n create_reye_pose=True,",
+ "detail": "SHOW.load_models",
+ "documentation": {}
+ },
+ {
+ "label": "Struct",
+ "kind": 6,
+ "importPath": "SHOW.masking",
+ "description": "SHOW.masking",
+ "peekOfCode": "class Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)\nclass Masking(nn.Module):\n def __init__(self):\n dir = os.path.abspath(os.path.join(os.path.dirname(__file__),'..'))\n super(Masking, self).__init__()\n with open(f'{dir}/../data/FLAME_masks.pkl', 'rb') as f:\n ss = pickle.load(f, encoding='latin1')",
+ "detail": "SHOW.masking",
+ "documentation": {}
+ },
+ {
+ "label": "Masking",
+ "kind": 6,
+ "importPath": "SHOW.masking",
+ "description": "SHOW.masking",
+ "peekOfCode": "class Masking(nn.Module):\n def __init__(self):\n dir = os.path.abspath(os.path.join(os.path.dirname(__file__),'..'))\n super(Masking, self).__init__()\n with open(f'{dir}/../data/FLAME_masks.pkl', 'rb') as f:\n ss = pickle.load(f, encoding='latin1')\n self.masks = Struct(**ss)\n with open(f'{dir}/../data/generic_model.pkl', 'rb') as f:\n ss = pickle.load(f, encoding='latin1')\n flame_model = Struct(**ss)",
+ "detail": "SHOW.masking",
+ "documentation": {}
+ },
+ {
+ "label": "to_tensor",
+ "kind": 2,
+ "importPath": "SHOW.masking",
+ "description": "SHOW.masking",
+ "peekOfCode": "def to_tensor(array, dtype=torch.float32):\n if 'torch.tensor' not in str(type(array)):\n return torch.tensor(array, dtype=dtype)\ndef to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():",
+ "detail": "SHOW.masking",
+ "documentation": {}
+ },
+ {
+ "label": "to_np",
+ "kind": 2,
+ "importPath": "SHOW.masking",
+ "description": "SHOW.masking",
+ "peekOfCode": "def to_np(array, dtype=np.float32):\n if 'scipy.sparse' in str(type(array)):\n array = array.todense()\n return np.array(array, dtype=dtype)\nclass Struct(object):\n def __init__(self, **kwargs):\n for key, val in kwargs.items():\n setattr(self, key, val)\nclass Masking(nn.Module):\n def __init__(self):",
+ "detail": "SHOW.masking",
+ "documentation": {}
+ },
+ {
+ "label": "parse_config_to_weight_dict",
+ "kind": 2,
+ "importPath": "SHOW.parse_weight",
+ "description": "SHOW.parse_weight",
+ "peekOfCode": "def parse_config_to_weight_dict(config)->dict:\n opt_weights_dict=config.opt_weights_dict\n if config.use_pre_compute_betas:\n opt_weights_dict.update(config.pre_compute_betas_weight)\n len_set=[len(val) for _,val in opt_weights_dict.items()]\n # assert(len(set(len_set))==1)\n max_weight_len=max(len_set)\n for k,v in opt_weights_dict.items():\n assert isinstance(v,list), \"Invalid weights type\"\n if len(v)list:\n keys = opt_weights_dict.keys()\n opt_weights = [dict(zip(keys, vals)) for vals in\n zip(*(opt_weights_dict[k] for k in keys))]\n for weight_list in opt_weights:\n for key in weight_list:\n weight_list[key] = torch.tensor(\n weight_list[key], device=device, dtype=dtype)\n return opt_weights\ndef parse_weight(config,device,dtype)->list:",
+ "detail": "SHOW.parse_weight",
+ "documentation": {}
+ },
+ {
+ "label": "parse_weight",
+ "kind": 2,
+ "importPath": "SHOW.parse_weight",
+ "description": "SHOW.parse_weight",
+ "peekOfCode": "def parse_weight(config,device,dtype)->list:\n ret=parse_config_to_weight_dict(config)\n ret=parse_weight_dict_to_list(ret,device,dtype)\n return ret",
+ "detail": "SHOW.parse_weight",
+ "documentation": {}
+ },
+ {
+ "label": "L2Prior",
+ "kind": 6,
+ "importPath": "SHOW.prior",
+ "description": "SHOW.prior",
+ "peekOfCode": "class L2Prior(nn.Module):\n def __init__(self,**kwargs):\n super().__init__()\n def forward(self, module_input):\n return torch.sum(module_input.pow(2))\n@PRIOR.register_module()\nclass SMPLifyAnglePrior(nn.Module):\n def __init__(self, dtype=torch.float32, **kwargs):\n super().__init__()\n # 55: left elbow, 90deg bend at -np.pi/2",
+ "detail": "SHOW.prior",
+ "documentation": {}
+ },
+ {
+ "label": "SMPLifyAnglePrior",
+ "kind": 6,
+ "importPath": "SHOW.prior",
+ "description": "SHOW.prior",
+ "peekOfCode": "class SMPLifyAnglePrior(nn.Module):\n def __init__(self, dtype=torch.float32, **kwargs):\n super().__init__()\n # 55: left elbow, 90deg bend at -np.pi/2\n # 58: right elbow, 90deg bend at np.pi/2\n # 12: left knee, 90deg bend at np.pi/2\n # 15: right knee, 90deg bend at np.pi/2\n angle_prior_idxs = torch.tensor([55, 58, 12, 15], dtype=torch.long)\n self.register_buffer('angle_prior_idxs', angle_prior_idxs)\n angle_prior_signs = torch.tensor([1, -1, -1, -1], dtype=dtype)",
+ "detail": "SHOW.prior",
+ "documentation": {}
+ },
+ {
+ "label": "MaxMixturePrior",
+ "kind": 6,
+ "importPath": "SHOW.prior",
+ "description": "SHOW.prior",
+ "peekOfCode": "class MaxMixturePrior(nn.Module):\n def __init__(\n self, \n prior_folder='prior',\n num_gaussians=6, \n dtype=torch.float32, \n epsilon=1e-16,\n use_merged=True,\n **kwargs\n ):",
+ "detail": "SHOW.prior",
+ "documentation": {}
+ },
+ {
+ "label": "build_prior",
+ "kind": 2,
+ "importPath": "SHOW.prior",
+ "description": "SHOW.prior",
+ "peekOfCode": "def build_prior(cfg):\n return PRIOR.build(cfg)\n@PRIOR.register_module()\nclass L2Prior(nn.Module):\n def __init__(self,**kwargs):\n super().__init__()\n def forward(self, module_input):\n return torch.sum(module_input.pow(2))\n@PRIOR.register_module()\nclass SMPLifyAnglePrior(nn.Module):",
+ "detail": "SHOW.prior",
+ "documentation": {}
+ },
+ {
+ "label": "PRIOR",
+ "kind": 5,
+ "importPath": "SHOW.prior",
+ "description": "SHOW.prior",
+ "peekOfCode": "PRIOR = Registry('PRIOR')\ndef build_prior(cfg):\n return PRIOR.build(cfg)\n@PRIOR.register_module()\nclass L2Prior(nn.Module):\n def __init__(self,**kwargs):\n super().__init__()\n def forward(self, module_input):\n return torch.sum(module_input.pow(2))\n@PRIOR.register_module()",
+ "detail": "SHOW.prior",
+ "documentation": {}
+ },
+ {
+ "label": "Renderer",
+ "kind": 6,
+ "importPath": "SHOW.renderer",
+ "description": "SHOW.renderer",
+ "peekOfCode": "class Renderer(nn.Module):\n def __init__(self, image_size, obj_filename, uv_size=512, flip=False):\n super(Renderer, self).__init__()\n self.image_size = image_size\n self.uv_size = uv_size\n verts, faces, aux = load_obj(obj_filename)\n uvcoords = aux.verts_uvs[None, ...] # (N, V, 2)\n uvfaces = faces.textures_idx[None, ...] # (N, F, 3)\n faces = faces.verts_idx[None, ...]\n mask = torch.from_numpy(imread(parse_abs_path(__file__,'../../data/uv_mask_eyes.jpg')) / 255.).permute(2, 0, 1).cuda()[0:3, :, :]",
+ "detail": "SHOW.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "apply_gamma",
+ "kind": 2,
+ "importPath": "SHOW.renderer",
+ "description": "SHOW.renderer",
+ "peekOfCode": "def apply_gamma(rgb, gamma=\"srgb\"):\n if gamma == \"srgb\":\n T = 0.0031308\n rgb1 = torch.max(rgb, rgb.new_tensor(T))\n return torch.where(rgb < T, 12.92 * rgb, (1.055 * torch.pow(torch.abs(rgb1), 1 / 2.4) - 0.055))\n elif gamma is None:\n return rgb\n else:\n return torch.pow(torch.max(rgb, rgb.new_tensor(0.0)), 1.0 / gamma)\ndef remove_gamma(rgb, gamma=\"srgb\"):",
+ "detail": "SHOW.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "remove_gamma",
+ "kind": 2,
+ "importPath": "SHOW.renderer",
+ "description": "SHOW.renderer",
+ "peekOfCode": "def remove_gamma(rgb, gamma=\"srgb\"):\n if gamma == \"srgb\":\n T = 0.04045\n rgb1 = torch.max(rgb, rgb.new_tensor(T))\n return torch.where(rgb < T, rgb / 12.92, torch.pow(torch.abs(rgb1 + 0.055) / 1.055, 2.4))\n elif gamma is None:\n return rgb\n else:\n res = torch.pow(torch.max(rgb, rgb.new_tensor(0.0)), gamma) + torch.min(rgb, rgb.new_tensor(0.0))\n return res",
+ "detail": "SHOW.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "sky",
+ "kind": 5,
+ "importPath": "SHOW.renderer",
+ "description": "SHOW.renderer",
+ "peekOfCode": "sky = torch.from_numpy(np.array([80, 140, 200]) / 255.).cuda()\ndef apply_gamma(rgb, gamma=\"srgb\"):\n if gamma == \"srgb\":\n T = 0.0031308\n rgb1 = torch.max(rgb, rgb.new_tensor(T))\n return torch.where(rgb < T, 12.92 * rgb, (1.055 * torch.pow(torch.abs(rgb1), 1 / 2.4) - 0.055))\n elif gamma is None:\n return rgb\n else:\n return torch.pow(torch.max(rgb, rgb.new_tensor(0.0)), 1.0 / gamma)",
+ "detail": "SHOW.renderer",
+ "documentation": {}
+ },
+ {
+ "label": "save_one_results",
+ "kind": 2,
+ "importPath": "SHOW.save_results",
+ "description": "SHOW.save_results",
+ "peekOfCode": "def save_one_results(\n vertices,faces,\n img_size,#(height,width)\n center,#(cx,cy)\n focal_length,#(focalx,focaly)\n camera_pose,#K:(4,4)\n meta_data={},\n color_type='sky',\n input_renderer=None,\n):",
+ "detail": "SHOW.save_results",
+ "documentation": {}
+ },
+ {
+ "label": "colors_dict",
+ "kind": 5,
+ "importPath": "SHOW.save_results",
+ "description": "SHOW.save_results",
+ "peekOfCode": "colors_dict = {\n 'red': np.array([0.5, 0.2, 0.2]),\n 'pink': np.array([0.7, 0.5, 0.5]),\n 'neutral': np.array([0.7, 0.7, 0.6]),\n # 'purple': np.array([0.5, 0.5, 0.7]),\n 'purple': np.array([0.55, 0.4, 0.9]),\n 'green': np.array([0.5, 0.55, 0.3]),\n 'sky': np.array([0.3, 0.5, 0.55]),\n 'white': np.array([1.0, 0.98, 0.94]),\n }",
+ "detail": "SHOW.save_results",
+ "documentation": {}
+ },
+ {
+ "label": "save_tracker",
+ "kind": 2,
+ "importPath": "SHOW.save_tracker",
+ "description": "SHOW.save_tracker",
+ "peekOfCode": "def save_tracker(\n img,valid_bool,valid_bs,\n ops,vertices,cameras,image_lmks,proj_lmks,\n flame_faces,mesh_rasterizer,debug_renderer,\n save_callback,\n):\n with torch.no_grad():\n images=img[valid_bool.bool(),...]\n mask = SHOW.utils.parse_mask(ops)\n gt_images = images",
+ "detail": "SHOW.save_tracker",
+ "documentation": {}
+ },
+ {
+ "label": "ImagesDataset",
+ "kind": 6,
+ "importPath": "SHOW.smplx_dataset",
+ "description": "SHOW.smplx_dataset",
+ "peekOfCode": "class ImagesDataset(Dataset):\n def __init__(\n self,\n config,\n start_frame=0,\n bbox=None,\n person_face_emb: np.ndarray = None,\n face_ider=None,\n face_detector=None,\n face_detector_mediapipe=None,",
+ "detail": "SHOW.smplx_dataset",
+ "documentation": {}
+ },
+ {
+ "label": "TrackerRasterizer",
+ "kind": 6,
+ "importPath": "SHOW.tracker_rasterizer",
+ "description": "SHOW.tracker_rasterizer",
+ "peekOfCode": "class TrackerRasterizer(MeshRasterizer):\n def __init__(self, image_size, cameras) -> None:\n settings = RasterizationSettings()\n settings.image_size = (image_size, image_size)\n settings.perspective_correct = True\n settings.cull_backfaces = True\n super().__init__(cameras, settings)\n self.reset()\n def reset(self):\n self.bary_coords = None",
+ "detail": "SHOW.tracker_rasterizer",
+ "documentation": {}
+ },
+ {
+ "label": "RadToDeg",
+ "kind": 6,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "class RadToDeg(nn.Module):\n r\"\"\"Creates an object that converts angles from radians to degrees.\n Args:\n tensor (Tensor): Tensor of arbitrary shape.\n Returns:\n Tensor: Tensor with same shape as input.\n Examples::\n >>> input = tgm.pi * torch.rand(1, 3, 3)\n >>> output = tgm.RadToDeg()(input)\n \"\"\"",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "DegToRad",
+ "kind": 6,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "class DegToRad(nn.Module):\n r\"\"\"Function that converts angles from degrees to radians.\n Args:\n tensor (Tensor): Tensor of arbitrary shape.\n Returns:\n Tensor: Tensor with same shape as input.\n Examples::\n >>> input = 360. * torch.rand(1, 3, 3)\n >>> output = tgm.DegToRad()(input)\n \"\"\"",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "ConvertPointsFromHomogeneous",
+ "kind": 6,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "class ConvertPointsFromHomogeneous(nn.Module):\n r\"\"\"Creates a transformation that converts points from homogeneous to\n Euclidean space.\n Args:\n points (Tensor): tensor of N-dimensional points.\n Returns:\n Tensor: tensor of N-1-dimensional points.\n Shape:\n - Input: :math:`(B, D, N)` or :math:`(D, N)`\n - Output: :math:`(B, D, N + 1)` or :math:`(D, N + 1)`",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "ConvertPointsToHomogeneous",
+ "kind": 6,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "class ConvertPointsToHomogeneous(nn.Module):\n r\"\"\"Creates a transformation to convert points from Euclidean to\n homogeneous space.\n Args:\n points (Tensor): tensor of N-dimensional points.\n Returns:\n Tensor: tensor of N+1-dimensional points.\n Shape:\n - Input: :math:`(B, D, N)` or :math:`(D, N)`\n - Output: :math:`(B, D, N + 1)` or :math:`(D, N + 1)`",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "rad2deg",
+ "kind": 2,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "def rad2deg(tensor):\n r\"\"\"Function that converts angles from radians to degrees.\n See :class:`~torchgeometry.RadToDeg` for details.\n Args:\n tensor (Tensor): Tensor of arbitrary shape.\n Returns:\n Tensor: Tensor with same shape as input.\n Example:\n >>> input = tgm.pi * torch.rand(1, 3, 3)\n >>> output = tgm.rad2deg(input)",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "deg2rad",
+ "kind": 2,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "def deg2rad(tensor):\n r\"\"\"Function that converts angles from degrees to radians.\n See :class:`~torchgeometry.DegToRad` for details.\n Args:\n tensor (Tensor): Tensor of arbitrary shape.\n Returns:\n Tensor: Tensor with same shape as input.\n Examples::\n >>> input = 360. * torch.rand(1, 3, 3)\n >>> output = tgm.deg2rad(input)",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "convert_points_from_homogeneous",
+ "kind": 2,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "def convert_points_from_homogeneous(points):\n r\"\"\"Function that converts points from homogeneous to Euclidean space.\n See :class:`~torchgeometry.ConvertPointsFromHomogeneous` for details.\n Examples::\n >>> input = torch.rand(2, 4, 3) # BxNx3\n >>> output = tgm.convert_points_from_homogeneous(input) # BxNx2\n \"\"\"\n if not torch.is_tensor(points):\n raise TypeError(\"Input type is not a torch.Tensor. Got {}\".format(\n type(points)))",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "convert_points_to_homogeneous",
+ "kind": 2,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "def convert_points_to_homogeneous(points):\n r\"\"\"Function that converts points from Euclidean to homogeneous space.\n See :class:`~torchgeometry.ConvertPointsToHomogeneous` for details.\n Examples::\n >>> input = torch.rand(2, 4, 3) # BxNx3\n >>> output = tgm.convert_points_to_homogeneous(input) # BxNx4\n \"\"\"\n if not torch.is_tensor(points):\n raise TypeError(\"Input type is not a torch.Tensor. Got {}\".format(\n type(points)))",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "angle_axis_to_rotation_matrix",
+ "kind": 2,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "def angle_axis_to_rotation_matrix(angle_axis):\n \"\"\"Convert 3d vector of axis-angle rotation to 4x4 rotation matrix\n Args:\n angle_axis (Tensor): tensor of 3d vector of axis-angle rotations.\n Returns:\n Tensor: tensor of 4x4 rotation matrices.\n Shape:\n - Input: :math:`(N, 3)`\n - Output: :math:`(N, 4, 4)`\n Example:",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "rtvec_to_pose",
+ "kind": 2,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "def rtvec_to_pose(rtvec):\n \"\"\"\n Convert axis-angle rotation and translation vector to 4x4 pose matrix\n Args:\n rtvec (Tensor): Rodrigues vector transformations\n Returns:\n Tensor: transformation matrices\n Shape:\n - Input: :math:`(N, 6)`\n - Output: :math:`(N, 4, 4)`",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "rotation_matrix_to_angle_axis",
+ "kind": 2,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "def rotation_matrix_to_angle_axis(rotation_matrix):\n \"\"\"Convert 3x4 rotation matrix to Rodrigues vector\n Args:\n rotation_matrix (Tensor): rotation matrix.\n Returns:\n Tensor: Rodrigues vector transformation.\n Shape:\n - Input: :math:`(N, 3, 4)`\n - Output: :math:`(N, 3)`\n Example:",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "rotation_matrix_to_quaternion",
+ "kind": 2,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6):\n \"\"\"Convert 3x4 rotation matrix to 4d quaternion vector\n This algorithm is based on algorithm described in\n https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201\n Args:\n rotation_matrix (Tensor): the rotation matrix to convert.\n Return:\n Tensor: the rotation in quaternion\n Shape:\n - Input: :math:`(N, 3, 4)`",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "quaternion_to_angle_axis",
+ "kind": 2,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "def quaternion_to_angle_axis(quaternion: torch.Tensor) -> torch.Tensor:\n \"\"\"Convert quaternion vector to angle axis of rotation.\n Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h\n Args:\n quaternion (torch.Tensor): tensor with quaternions.\n Return:\n torch.Tensor: tensor with angle axis of rotation.\n Shape:\n - Input: :math:`(*, 4)` where `*` means, any number of dimensions\n - Output: :math:`(*, 3)`",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "angle_axis_to_quaternion",
+ "kind": 2,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "def angle_axis_to_quaternion(angle_axis: torch.Tensor) -> torch.Tensor:\n \"\"\"Convert an angle axis to a quaternion.\n Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h\n Args:\n angle_axis (torch.Tensor): tensor with angle axis.\n Return:\n torch.Tensor: tensor with quaternion.\n Shape:\n - Input: :math:`(*, 3)` where `*` means, any number of dimensions\n - Output: :math:`(*, 4)`",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "__all__",
+ "kind": 5,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "__all__ = [\n # functional api\n \"pi\",\n \"rad2deg\",\n \"deg2rad\",\n \"convert_points_from_homogeneous\",\n \"convert_points_to_homogeneous\",\n \"angle_axis_to_rotation_matrix\",\n \"rotation_matrix_to_angle_axis\",\n \"rotation_matrix_to_quaternion\",",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "pi",
+ "kind": 5,
+ "importPath": "conversions",
+ "description": "conversions",
+ "peekOfCode": "pi = torch.Tensor([3.14159265358979323846])\ndef rad2deg(tensor):\n r\"\"\"Function that converts angles from radians to degrees.\n See :class:`~torchgeometry.RadToDeg` for details.\n Args:\n tensor (Tensor): Tensor of arbitrary shape.\n Returns:\n Tensor: Tensor with same shape as input.\n Example:\n >>> input = tgm.pi * torch.rand(1, 3, 3)",
+ "detail": "conversions",
+ "documentation": {}
+ },
+ {
+ "label": "parser",
+ "kind": 5,
+ "importPath": "download_youtube",
+ "description": "download_youtube",
+ "peekOfCode": "parser = argparse.ArgumentParser()\nparser.add_argument('-base_path', '--base_path', help='base folder path of dataset')\nparser.add_argument('-speaker', '--speaker',\n help='download videos of a specific speaker {oliver, jon, conan, rock, chemistry, ellen, almaram, angelica, seth, shelly}')\nargs = parser.parse_args()\nBASE_PATH = args.base_path\ndf = pd.read_csv(os.path.join(BASE_PATH, \"videos_links.csv\"))\nif args.speaker:\n df = df[df['speaker'] == args.speaker]\ntemp_output_path = './tmp2/temp_video.mp4'",
+ "detail": "download_youtube",
+ "documentation": {}
+ },
+ {
+ "label": "args",
+ "kind": 5,
+ "importPath": "download_youtube",
+ "description": "download_youtube",
+ "peekOfCode": "args = parser.parse_args()\nBASE_PATH = args.base_path\ndf = pd.read_csv(os.path.join(BASE_PATH, \"videos_links.csv\"))\nif args.speaker:\n df = df[df['speaker'] == args.speaker]\ntemp_output_path = './tmp2/temp_video.mp4'\nfor _, row in tqdm(df.iterrows(), total=df.shape[0]):\n i, name, link = row\n if 'youtube' in link:\n try:",
+ "detail": "download_youtube",
+ "documentation": {}
+ },
+ {
+ "label": "BASE_PATH",
+ "kind": 5,
+ "importPath": "download_youtube",
+ "description": "download_youtube",
+ "peekOfCode": "BASE_PATH = args.base_path\ndf = pd.read_csv(os.path.join(BASE_PATH, \"videos_links.csv\"))\nif args.speaker:\n df = df[df['speaker'] == args.speaker]\ntemp_output_path = './tmp2/temp_video.mp4'\nfor _, row in tqdm(df.iterrows(), total=df.shape[0]):\n i, name, link = row\n if 'youtube' in link:\n try:\n output_path = os.path.join(BASE_PATH, row[\"speaker\"], \"videos\", row[\"video_fn\"])",
+ "detail": "download_youtube",
+ "documentation": {}
+ },
+ {
+ "label": "df",
+ "kind": 5,
+ "importPath": "download_youtube",
+ "description": "download_youtube",
+ "peekOfCode": "df = pd.read_csv(os.path.join(BASE_PATH, \"videos_links.csv\"))\nif args.speaker:\n df = df[df['speaker'] == args.speaker]\ntemp_output_path = './tmp2/temp_video.mp4'\nfor _, row in tqdm(df.iterrows(), total=df.shape[0]):\n i, name, link = row\n if 'youtube' in link:\n try:\n output_path = os.path.join(BASE_PATH, row[\"speaker\"], \"videos\", row[\"video_fn\"])\n if not (os.path.exists(os.path.dirname(output_path))):",
+ "detail": "download_youtube",
+ "documentation": {}
+ },
+ {
+ "label": "temp_output_path",
+ "kind": 5,
+ "importPath": "download_youtube",
+ "description": "download_youtube",
+ "peekOfCode": "temp_output_path = './tmp2/temp_video.mp4'\nfor _, row in tqdm(df.iterrows(), total=df.shape[0]):\n i, name, link = row\n if 'youtube' in link:\n try:\n output_path = os.path.join(BASE_PATH, row[\"speaker\"], \"videos\", row[\"video_fn\"])\n if not (os.path.exists(os.path.dirname(output_path))):\n os.makedirs(os.path.dirname(output_path))\n command = 'yt-dlp -o {temp_path} -f mp4 {link}'.format(link=link, temp_path=temp_output_path)\n res1 = call(command, shell=True)",
+ "detail": "download_youtube",
+ "documentation": {}
+ },
+ {
+ "label": "my_cmd",
+ "kind": 5,
+ "importPath": "download_youtube",
+ "description": "download_youtube",
+ "peekOfCode": "my_cmd = 'ls ' + os.path.join(BASE_PATH, row[\"speaker\"], \"videos\") + ' | wc -l'\nos.system(my_cmd)",
+ "detail": "download_youtube",
+ "documentation": {}
+ },
+ {
+ "label": "cvt_cfg",
+ "kind": 2,
+ "importPath": "main",
+ "description": "main",
+ "peekOfCode": "def cvt_cfg(val):\n if val == 'True':\n return True\n elif val == 'False':\n return False\n elif val.isdigit():\n return int(val)\n else:\n return val\ndef parse_other_Cfg(other_cfg: list):",
+ "detail": "main",
+ "documentation": {}
+ },
+ {
+ "label": "parse_other_Cfg",
+ "kind": 2,
+ "importPath": "main",
+ "description": "main",
+ "peekOfCode": "def parse_other_Cfg(other_cfg: list):\n parse_dict = {}\n parse_key = None\n parse_val = None\n def reg_key():\n nonlocal parse_dict\n nonlocal parse_key\n nonlocal parse_val\n if parse_key != None:\n parse_dict[parse_key] = parse_val",
+ "detail": "main",
+ "documentation": {}
+ },
+ {
+ "label": "parse_overwrite_flag",
+ "kind": 2,
+ "importPath": "main",
+ "description": "main",
+ "peekOfCode": "def parse_overwrite_flag(over_write_cfg):\n over_write_cfg = over_write_cfg.strip(',')\n over_write_cfg = over_write_cfg.split(',')\n ret = {}\n for i in over_write_cfg:\n k, v = i.split('=')\n ret[k] = cvt_cfg(v)\n return ret\nif __name__ == '__main__':\n SHOW.utils.work_seek_init()",
+ "detail": "main",
+ "documentation": {}
+ },
+ {
+ "label": "render_pkl_api",
+ "kind": 2,
+ "importPath": "pkl2img",
+ "description": "pkl2img",
+ "peekOfCode": "def render_pkl_api(\n img_folder,\n ours_pkl_file_path,\n ours_images_path,\n output_video_path=None,\n output_obj_path=None,\n mica_all_dir=None,\n replace_from_mica=False,\n # use_npy_betas_ver='betas_2019_male',\n model_path='../models/smplx/SMPLX_NEUTRAL_2020_org.npz',",
+ "detail": "pkl2img",
+ "documentation": {}
+ },
+ {
+ "label": "global_orient[:,:]",
+ "kind": 5,
+ "importPath": "post_process",
+ "description": "post_process",
+ "peekOfCode": "global_orient[:,:] = global_orient[0,:]\ntransl[:,:] = transl[0,:]\nif (\n speaker == \"oliver\" or\n speaker == \"seth\" or\n speaker == \"chemistry\"\n):\n pose_type='sitting'\nelse:\n pose_type='standing'",
+ "detail": "post_process",
+ "documentation": {}
+ },
+ {
+ "label": "transl[:,:]",
+ "kind": 5,
+ "importPath": "post_process",
+ "description": "post_process",
+ "peekOfCode": "transl[:,:] = transl[0,:]\nif (\n speaker == \"oliver\" or\n speaker == \"seth\" or\n speaker == \"chemistry\"\n):\n pose_type='sitting'\nelse:\n pose_type='standing'\nif pose_type == 'standing':",
+ "detail": "post_process",
+ "documentation": {}
+ },
+ {
+ "label": "body_pose",
+ "kind": 5,
+ "importPath": "post_process",
+ "description": "post_process",
+ "peekOfCode": "body_pose = body_pose_axis.reshape(bs, 63)\nfor i in [1, 2, 4, 5, 7, 8, 10, 11]:\n body_pose[:, (i - 1) * 3 + 0] = ref_pose[(i) * 3 + 0]\n body_pose[:, (i - 1) * 3 + 1] = ref_pose[(i) * 3 + 1]\n body_pose[:, (i - 1) * 3 + 2] = ref_pose[(i) * 3 + 2]",
+ "detail": "post_process",
+ "documentation": {}
+ },
+ {
+ "label": "save_one_results",
+ "kind": 2,
+ "importPath": "render_pkl_release",
+ "description": "render_pkl_release",
+ "peekOfCode": "def save_one_results(\n vertices,\n faces,\n img_size, #(height,width)\n center, #(cx,cy)\n focal_length, #(focalx,focaly)\n camera_pose, #K:(4,4)\n meta_data={},\n color_type='sky',\n input_renderer=None,",
+ "detail": "render_pkl_release",
+ "documentation": {}
+ },
+ {
+ "label": "render_pkl_api",
+ "kind": 2,
+ "importPath": "render_pkl_release",
+ "description": "render_pkl_release",
+ "peekOfCode": "def render_pkl_api(\n pkl_file_path, # pkl path\n out_images_path, # image path folder\n output_video_path=None, # endwith .mp4\n smplx_model_path='../models/smplx/SMPLX_NEUTRAL_2020_org.npz', #smplx neutral 2020 npz file\n **kwargs):\n dtype = torch.float32\n smplx_model_path = os.path.abspath(\n os.path.join(os.path.dirname(__file__), smplx_model_path))\n all_var = mmcv.load(pkl_file_path)",
+ "detail": "render_pkl_release",
+ "documentation": {}
+ },
+ {
+ "label": "DEFAULT_SMPLX_CONFIG",
+ "kind": 5,
+ "importPath": "render_pkl_release",
+ "description": "render_pkl_release",
+ "peekOfCode": "DEFAULT_SMPLX_CONFIG = dict(\n create_global_orient=True,\n create_body_pose=True,\n create_betas=True,\n create_left_hand_pose=True,\n create_right_hand_pose=True,\n create_expression=True,\n create_jaw_pose=True,\n create_leye_pose=True,\n create_reye_pose=True,",
+ "detail": "render_pkl_release",
+ "documentation": {}
+ },
+ {
+ "label": "colors_dict",
+ "kind": 5,
+ "importPath": "render_pkl_release",
+ "description": "render_pkl_release",
+ "peekOfCode": "colors_dict = {\n 'red': np.array([0.5, 0.2, 0.2]),\n 'pink': np.array([0.7, 0.5, 0.5]),\n 'neutral': np.array([0.7, 0.7, 0.6]),\n # 'purple': np.array([0.5, 0.5, 0.7]),\n 'purple': np.array([0.55, 0.4, 0.9]),\n 'green': np.array([0.5, 0.55, 0.3]),\n 'sky': np.array([0.3, 0.5, 0.55]),\n 'white': np.array([1.0, 0.98, 0.94]),\n}",
+ "detail": "render_pkl_release",
+ "documentation": {}
+ },
+ {
+ "label": "SHOW_stage1",
+ "kind": 2,
+ "importPath": "stage1_main",
+ "description": "stage1_main",
+ "peekOfCode": "def SHOW_stage1(*args, **kwargs):\n machine_info = SHOW.get_machine_info()\n import pprint\n pprint.pprint(f'machine_info: {machine_info}')\n smplifyx_cfg = SHOW.utils.from_rela_path(\n __file__, './configs/mmcv_smplifyx_config.py')\n smplifyx_cfg.merge_from_dict(kwargs)\n def update_betas_name_cfg():\n smplifyx_cfg.save_betas_name = smplifyx_cfg.betas_ver_temp.format(\n smplifyx_cfg.speaker_name)",
+ "detail": "stage1_main",
+ "documentation": {}
+ },
+ {
+ "label": "SHOW_stage2",
+ "kind": 2,
+ "importPath": "stage2_main",
+ "description": "stage2_main",
+ "peekOfCode": "def SHOW_stage2(*args, **kwargs):\n machine_info = SHOW.get_machine_info()\n import pprint\n pprint.pprint(f'machine_info: {machine_info}')\n loggers = kwargs.get('loggers', None)\n tracker_cfg = SHOW.from_rela_path(__file__,\n './configs/mmcv_tracker_config.py')\n tracker_cfg.update(**kwargs)\n tracker_cfg.merge_from_dict(condor_cfg)\n if tracker_cfg.get('over_write_cfg', None):",
+ "detail": "stage2_main",
+ "documentation": {}
+ }
+]
\ No newline at end of file