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