147 lines
5.1 KiB
YAML
147 lines
5.1 KiB
YAML
module: myeasymocap.stages.basestage.MultiStage
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args:
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output: output/sv1p # 指定输出路径
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at_step:
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detect:
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module: myeasymocap.backbone.yolo.yolo.YoloWithTrack
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key_from_data: [images, imgnames]
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args:
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model: yolov5m
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name: person
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keypoints2d:
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module: myeasymocap.backbone.hrnet.myhrnet.MyHRNet
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key_from_data: [images, imgnames]
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key_from_previous: [bbox]
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key_keep: []
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args:
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ckpt: data/models/pose_hrnet_w48_384x288.pth
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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: # 这个模块给定图片和检测的框，直接返回crop系下的人体姿态
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module: myeasymocap.backbone.pare.pare.MyPARE
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key_from_data: [images, imgnames] # 从数据集中读入的bbox、图片、图片名，图片名用于保存结果
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key_from_previous: [bbox]
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key_keep: [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: 3dpw # 指定使用3dpw的预训练模型
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at_final:
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load_body_model: # 载入SMPL模型
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module: myeasymocap.io.model.SMPLLoader
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args:
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model_path: models/pare/data/body_models/smpl/SMPL_NEUTRAL.pkl
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regressor_path: models/J_regressor_body25.npy
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init_translation: # 给定crop系下的姿态、2D关键点、相机参数，返回世界系下的人体姿态
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module: myeasymocap.operations.init.InitTranslation
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key_from_data: [keypoints, cameras, params] # 读入关键点、相机参数、SMPL参数
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key_from_previous: [body_model] # 读入SMPL模型进行关键点计算
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args:
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solve_T: True
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solve_R: False
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smooth: # 对初始化的结果进行平滑
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module: myeasymocap.operations.smooth.SmoothPoses
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key_from_data: [params]
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args:
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window_size: 2
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mean_param: # Mean shapes
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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_RT:
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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: [Th, 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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index_est: [2, 5, 9, 12]
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index_gt: [2, 5, 9, 12]
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smooth:
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weight: 1.
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module: myeasymocap.operations.loss.Smooth
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key_from_output: [Rh, Th]
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key_from_infos: [cameras] # 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, Th]
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smooth_type: [Linear, Depth] # 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: [100., 1000.]
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window_weight: [0.5, 0.3, 0.1, 0.1]
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refine_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, 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: gm
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norm_info: 0.02
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smooth:
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weight: 1.
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module: myeasymocap.operations.loss.Smooth
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key_from_output: [poses, Rh, Th, keypoints]
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key_from_infos: [cameras] # 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, Th, poses, keypoints]
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smooth_type: [Linear, Depth, Linear, Linear] # 这个depth似乎需要相机参数进行转换
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norm: [l2, l2, l2, l2]
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order: [2, 2, 2, 2]
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weights: [100., 1000., 50., 100.]
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window_weight: [0.5, 0.3, 0.1, 0.1]
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init:
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weight: 1.
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module: myeasymocap.operations.loss.Init
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key_from_output: [poses]
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key_from_infos: [init_poses]
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args:
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keys: [poses]
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norm: l2
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weights: [1.]
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prior:
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weight: 0.1
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module: easymocap.multistage.gmm.GMMPrior
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key_from_output: [poses]
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key_from_infos: []
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args:
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start: 0
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end: 69
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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
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key_from_data: [cameras, imgnames]
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key_from_previous: [params, body_model]
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args:
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backend: pyrender
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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: 30
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keep_image: False |