lib/pixielib/utils/config.py

"""
Default config for PIXIE
"""
import argparse
import os

import yaml
from yacs.config import CfgNode as CN
from huggingface_hub import hf_hub_download

cfg = CN()

abs_pixie_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
cfg.pixie_dir = abs_pixie_dir
cfg.device = "cuda"
cfg.device_id = "0"
cfg.pretrained_modelpath = hf_hub_download(
    repo_id="Yuliang/PIXIE", filename="pixie_model.tar", use_auth_token=os.environ["ICON"]
)
# smplx parameter settings
cfg.params = CN()
cfg.params.body_list = ["body_cam", "global_pose", "partbody_pose", "neck_pose"]
cfg.params.head_list = ["head_cam", "tex", "light"]
cfg.params.head_share_list = ["shape", "exp", "head_pose", "jaw_pose"]
cfg.params.hand_list = ["hand_cam"]
cfg.params.hand_share_list = [
    "right_wrist_pose",
    "right_hand_pose",
]    # only for right hand

# ---------------------------------------------------------------------------- #
# Options for Body model
# ---------------------------------------------------------------------------- #
cfg.model = CN()
cfg.model.topology_path = hf_hub_download(
    repo_id="Yuliang/PIXIE",
    use_auth_token=os.environ["ICON"],
    filename="SMPL_X_template_FLAME_uv.obj"
)
cfg.model.topology_smplxtex_path = hf_hub_download(
    repo_id="Yuliang/PIXIE", use_auth_token=os.environ["ICON"], filename="smplx_tex.obj"
)
cfg.model.topology_smplx_hand_path = hf_hub_download(
    repo_id="Yuliang/PIXIE", use_auth_token=os.environ["ICON"], filename="smplx_hand.obj"
)
cfg.model.smplx_model_path = hf_hub_download(
    repo_id="Yuliang/PIXIE", use_auth_token=os.environ["ICON"], filename="SMPLX_NEUTRAL_2020.npz"
)
cfg.model.face_mask_path = hf_hub_download(
    repo_id="Yuliang/PIXIE", use_auth_token=os.environ["ICON"], filename="uv_face_mask.png"
)
cfg.model.face_eye_mask_path = hf_hub_download(
    repo_id="Yuliang/PIXIE", use_auth_token=os.environ["ICON"], filename="uv_face_eye_mask.png"
)
cfg.model.extra_joint_path = hf_hub_download(
    repo_id="Yuliang/PIXIE", use_auth_token=os.environ["ICON"], filename="smplx_extra_joints.yaml"
)
cfg.model.j14_regressor_path = hf_hub_download(
    repo_id="Yuliang/PIXIE", use_auth_token=os.environ["ICON"], filename="SMPLX_to_J14.pkl"
)
cfg.model.flame2smplx_cached_path = hf_hub_download(
    repo_id="Yuliang/PIXIE", use_auth_token=os.environ["ICON"], filename="flame2smplx_tex_1024.npy"
)
cfg.model.smplx_tex_path = hf_hub_download(
    repo_id="Yuliang/PIXIE", use_auth_token=os.environ["ICON"], filename="smplx_tex.png"
)
cfg.model.mano_ids_path = hf_hub_download(
    repo_id="Yuliang/PIXIE",
    use_auth_token=os.environ["ICON"],
    filename="MANO_SMPLX_vertex_ids.pkl"
)
cfg.model.flame_ids_path = hf_hub_download(
    repo_id="Yuliang/PIXIE",
    use_auth_token=os.environ["ICON"],
    filename="SMPL-X__FLAME_vertex_ids.npy"
)
cfg.model.uv_size = 256
cfg.model.n_shape = 200
cfg.model.n_tex = 50
cfg.model.n_exp = 50
cfg.model.n_body_cam = 3
cfg.model.n_head_cam = 3
cfg.model.n_hand_cam = 3
cfg.model.tex_type = "BFM"    # BFM, FLAME, albedoMM
cfg.model.uvtex_type = "SMPLX"    # FLAME or SMPLX
cfg.model.use_tex = False    # whether to use flame texture model
cfg.model.flame_tex_path = ""

# pose
cfg.model.n_global_pose = 3 * 2
cfg.model.n_head_pose = 3 * 2
cfg.model.n_neck_pose = 3 * 2
cfg.model.n_jaw_pose = 3    # euler angle
cfg.model.n_body_pose = 21 * 3 * 2
cfg.model.n_partbody_pose = (21 - 4) * 3 * 2
cfg.model.n_left_hand_pose = 15 * 3 * 2
cfg.model.n_right_hand_pose = 15 * 3 * 2
cfg.model.n_left_wrist_pose = 1 * 3 * 2
cfg.model.n_right_wrist_pose = 1 * 3 * 2
cfg.model.n_light = 27
cfg.model.check_pose = True

# ---------------------------------------------------------------------------- #
# Options for Dataset
# ---------------------------------------------------------------------------- #
cfg.dataset = CN()
cfg.dataset.source = ["body", "head", "hand"]

# head/face dataset
cfg.dataset.head = CN()
cfg.dataset.head.batch_size = 24
cfg.dataset.head.num_workers = 2
cfg.dataset.head.from_body = True
cfg.dataset.head.image_size = 224
cfg.dataset.head.image_hd_size = 224
cfg.dataset.head.scale_min = 1.8
cfg.dataset.head.scale_max = 2.2
cfg.dataset.head.trans_scale = 0.3
# body datset
cfg.dataset.body = CN()
cfg.dataset.body.batch_size = 24
cfg.dataset.body.num_workers = 2
cfg.dataset.body.image_size = 224
cfg.dataset.body.image_hd_size = 1024
cfg.dataset.body.use_hd = True
# hand datset
cfg.dataset.hand = CN()
cfg.dataset.hand.batch_size = 24
cfg.dataset.hand.num_workers = 2
cfg.dataset.hand.image_size = 224
cfg.dataset.hand.image_hd_size = 512
cfg.dataset.hand.scale_min = 2.2
cfg.dataset.hand.scale_max = 2.6
cfg.dataset.hand.trans_scale = 0.4

# ---------------------------------------------------------------------------- #
# Options for Network
# ---------------------------------------------------------------------------- #
cfg.network = CN()
cfg.network.encoder = CN()
cfg.network.encoder.body = CN()
cfg.network.encoder.body.type = "hrnet"
cfg.network.encoder.head = CN()
cfg.network.encoder.head.type = "resnet50"
cfg.network.encoder.hand = CN()
cfg.network.encoder.hand.type = "resnet50"

cfg.network.regressor = CN()
cfg.network.regressor.head_share = CN()
cfg.network.regressor.head_share.type = "mlp"
cfg.network.regressor.head_share.channels = [1024, 1024]
cfg.network.regressor.hand_share = CN()
cfg.network.regressor.hand_share.type = "mlp"
cfg.network.regressor.hand_share.channels = [1024, 1024]
cfg.network.regressor.body = CN()
cfg.network.regressor.body.type = "mlp"
cfg.network.regressor.body.channels = [1024]
cfg.network.regressor.head = CN()
cfg.network.regressor.head.type = "mlp"
cfg.network.regressor.head.channels = [1024]
cfg.network.regressor.hand = CN()
cfg.network.regressor.hand.type = "mlp"
cfg.network.regressor.hand.channels = [1024]

cfg.network.extractor = CN()
cfg.network.extractor.head_share = CN()
cfg.network.extractor.head_share.type = "mlp"
cfg.network.extractor.head_share.channels = []
cfg.network.extractor.left_hand_share = CN()
cfg.network.extractor.left_hand_share.type = "mlp"
cfg.network.extractor.left_hand_share.channels = []
cfg.network.extractor.right_hand_share = CN()
cfg.network.extractor.right_hand_share.type = "mlp"
cfg.network.extractor.right_hand_share.channels = []

cfg.network.moderator = CN()
cfg.network.moderator.head_share = CN()
cfg.network.moderator.head_share.detach_inputs = False
cfg.network.moderator.head_share.detach_feature = False
cfg.network.moderator.head_share.type = "temp-softmax"
cfg.network.moderator.head_share.channels = [1024, 1024]
cfg.network.moderator.head_share.reduction = 4
cfg.network.moderator.head_share.scale_type = "scalars"
cfg.network.moderator.head_share.scale_init = 1.0
cfg.network.moderator.hand_share = CN()
cfg.network.moderator.hand_share.detach_inputs = False
cfg.network.moderator.hand_share.detach_feature = False
cfg.network.moderator.hand_share.type = "temp-softmax"
cfg.network.moderator.hand_share.channels = [1024, 1024]
cfg.network.moderator.hand_share.reduction = 4
cfg.network.moderator.hand_share.scale_type = "scalars"
cfg.network.moderator.hand_share.scale_init = 0.0


def get_cfg_defaults():
    """Get a yacs CfgNode object with default values for my_project."""
    # Return a clone so that the defaults will not be altered
    # This is for the "local variable" use pattern
    return cfg.clone()


def update_cfg(cfg, cfg_file):
    # cfg.merge_from_file(cfg_file, allow_unsafe=True)
    cfg.merge_from_file(cfg_file)
    return cfg.clone()


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--cfg", type=str, help="cfg file path")

    args = parser.parse_args()
    cfg = get_cfg_defaults()
    if args.cfg is not None:
        cfg_file = args.cfg
        cfg = update_cfg(cfg, args.cfg)
        cfg.cfg_file = cfg_file
    return cfg