EasyMocap/config/1v1p/hand_detect_finetune.yml

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smooth: &smooth_keypoints
weight: 50.
module: myeasymocap.operations.loss.Smooth
key_from_output: [keypoints, Th]
key_from_infos: [] # TODO: 根据2D的置信度来计算smooth权重
args:
keys: [Th, keypoints]
smooth_type: [Linear, Linear] # 这个depth似乎需要相机参数进行转换
norm: [l2, l2]
order: [2, 2]
weights: [1000., 1000.]
window_weight: [0.5, 0.3, 0.1, 0.1]
module: myeasymocap.stages.basestage.MultiStage
args:
output: output/sv1p_hand
at_step:
detect_by_mediapipe:
module: myeasymocap.backbone.mediapipe.hand.MediaPipe
key_from_data: [images, imgnames]
args:
ckpt: models/mediapipe/hand_landmarker.task
hand2d:
module: myeasymocap.backbone.hand2d.hand2d.MyHand2D
key_from_data: [images, imgnames]
key_from_previous: [bbox]
args:
# ckpt: /nas/public/EasyMocapModels/hrnetv2_w18_coco_wholebody_hand_256x256-1c028db7_20210908.pth
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# ckpt: /nas/public/EasyMocapModels/hand/resnet_kp2d_clean.pt
ckpt: models/hand_resnet_kp2d_clean.pt
url: 1LTK7e9oAS6B3drmQyXwTZild6k87fEZa
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mode: resnet
vis2d:
module: myeasymocap.io.vis.Vis2D
skip: False
key_from_data: [images]
key_from_previous: [keypoints, bbox]
args:
name: vis_keypoints2d
scale: 0.5
infer_mano: #
module: myeasymocap.backbone.hmr.hmr.MyHMR
key_from_data: [images, imgnames]
key_from_previous: [bbox]
key_keep: [meta, cameras, imgnames] # 将这些参数都保留到最后的输出中
args:
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ckpt: models/hand_manol_pca45_noflat.ckpt
url: '1KTi_oJ_udLRK3WZ3xyHzBUd6vKAApfT8'
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# TODO: add visualize for Init MANO
at_final:
load_hand_model: # 载入身体模型
module: myeasymocap.io.model.MANOLoader
args:
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cfg_path: config/model/manol.yml
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model_path: models/manov1.2/MANO_LEFT.pkl #models/handmesh/data/MANO_RIGHT.pkl # load mano model
regressor_path: models/manov1.2/J_regressor_mano_LEFT.txt #models/handmesh/data/J_regressor_mano_RIGHT.txt
num_pca_comps: 45
use_pca: True
use_flat_mean: False
# 这个模块返回两个内容body_model, model; 其中的body_model是用来进行可视化的
mean_param: # 初始化姿态这里将poses和shapes都进行平均
module: myeasymocap.operations.init.MeanShapes
key_from_data: [params]
args:
keys: ['shapes']
init_T: # 初始化每一帧的位置
module: myeasymocap.operations.optimizer.Optimizer
key_from_data: [keypoints, cameras, params]
key_from_previous: [model]
args:
optimizer_args: {optim_type: lbfgs}
optimize_keys: [Th]
loss:
repro:
weight: 100.
module: myeasymocap.operations.loss.Keypoints2D
key_from_output: [keypoints]
key_from_infos: [keypoints, cameras]
args:
norm: l2
smooth: *smooth_keypoints
init_R: # 初始化每一帧的旋转
module: myeasymocap.operations.optimizer.Optimizer
key_from_data: [keypoints, cameras]
key_from_previous: [model, params]
args:
optimizer_args: {optim_type: lbfgs}
optimize_keys: [Rh]
loss:
repro:
weight: 100.
module: myeasymocap.operations.loss.Keypoints2D
key_from_output: [keypoints]
key_from_infos: [keypoints, cameras]
args:
norm: l2
smooth: *smooth_keypoints
refine_poses: # 优化poses
repeat: 2
module: myeasymocap.operations.optimizer.Optimizer
key_from_data: [keypoints, cameras]
key_from_previous: [model, params]
args:
optimizer_args: {optim_type: lbfgs}
optimize_keys: [poses, shapes, Rh, Th]
loss:
repro:
weight: 100.
module: myeasymocap.operations.loss.Keypoints2D
key_from_output: [keypoints]
key_from_infos: [keypoints, cameras]
args:
norm: l1
reg:
weight: 0.001
module: myeasymocap.operations.loss.RegLoss
key_from_output: [poses]
key_from_infos: []
args:
key: poses
norm: l2
smooth: *smooth_keypoints
write:
module: myeasymocap.io.write.WriteSMPL
key_from_data: [meta]
key_from_previous: [params, model]
args:
name: smpl
render:
module: myeasymocap.io.vis3d.Render_multiview
key_from_data: [cameras, imgnames]
key_from_previous: [hand_model, params]
args:
model_name: hand_model
backend: pyrender
view_list: [0]
scale: 0.5
make_video:
module: myeasymocap.io.video.MakeVideo
args:
fps: 50
keep_image: False