liyang
ae28d55f81
Source: RGB-D -> Dyn-HaMR -> L20 retargeting -> FoundationPose -> reference repair -> SPIDER, documented in docs/PIPELINE_LATEST.md and docs/SETUP_AND_WEIGHTS.md. Adds FoundationPose and nvdiffrast upstream snapshots, requirements/pipeline_venv.txt and the FoundationPose weight manifest/downloader. Assets (Git LFS): weights/ (WiLoR detector, HandFlow denoiser, UniDepth-L), FoundationPose checkpoints, HaMeR checkpoint, Dyn-HaMR HMP model and BMC constraints, L20 URDF/meshes, the 20260915_171525 D405 recording and the two box CADs. MANO models are not redistributed (third_party/hamer/_DATA/data/mano/README.txt). Environments, caches and run outputs excluded. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
103 lines
2.9 KiB
Python
103 lines
2.9 KiB
Python
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
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#
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# NVIDIA CORPORATION and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION is strictly prohibited.
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import os,sys
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from dataclasses import dataclass, field
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from typing import List, Optional, Tuple,Union
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import numpy as np
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import omegaconf
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import torch
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@dataclass
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class TrainingConfig(omegaconf.dictconfig.DictConfig):
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input_resize: tuple = (160, 160)
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normalize_xyz:Optional[bool] = True
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use_mask:Optional[bool] = False
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crop_ratio:Optional[float] = None
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split_objects_across_gpus: bool = True
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max_num_key: Optional[int] = None
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use_normal:bool = False
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n_view:int = 1
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zfar:float = np.inf
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c_in:int = 6
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train_num_pair:Optional[int] = None
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make_pair_online:Optional[bool] = False
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render_backend:Optional[str] = 'nvdiffrast'
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# Run management
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run_id: Optional[str] = None
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exp_name:Optional[str] = None
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resume_run_id: Optional[str] = None
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save_dir: Optional[str] = None
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batch_size: int = 64
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epoch_size: int = 115200
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val_size: int = 1280
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n_epochs: int = 25
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save_epoch_interval: int = 100
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n_dataloader_workers: int = 20
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n_rendering_workers: int = 1
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gradient_max_norm:float = np.inf
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max_step_per_epoch: Optional[int] = 25000
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# Network
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use_BN:bool = True
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loss_type:Optional[str] = 'pairwise_valid'
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# Optimizer
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optimizer: str = "adam"
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weight_decay: float = 0.0
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clip_grad_norm: float = np.inf
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lr: float = 0.0001
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warmup_step: int = -1 # -1 means disable
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n_epochs_warmup: int = 1
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# Visualization
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vis_interval: Optional[int] = 1000
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debug: Optional[bool] = None
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@dataclass
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class TrainRefinerConfig:
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# Datasets
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input_resize: tuple = (160, 160) #(W,H)
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crop_ratio:Optional[float] = None
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max_num_key: Optional[int] = None
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use_normal:bool = False
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use_mask:Optional[bool] = False
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normal_uint8:bool = False
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normalize_xyz:Optional[bool] = True
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trans_normalizer:Optional[list] = None
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rot_normalizer:Optional[float] = None
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c_in:int = 6
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n_view:int = 1
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zfar:float = np.inf
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trans_rep:str = 'tracknet' # tracknet/deepim
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rot_rep:Optional[str] = 'axis_angle' # 6d/axis_angle
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save_dir: Optional[str] = None
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# Run management
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run_id: Optional[str] = None
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exp_name:Optional[str] = None
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batch_size: int = 64
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use_BN:bool = True
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optimizer: str = "adam"
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weight_decay: float = 0.0
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clip_grad_norm: float = np.inf
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lr: float = 0.0001
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warmup_step: int = -1
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loss_type:str = 'l2' # l1/l2/add
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vis_interval: Optional[int] = 1000
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debug: Optional[bool] = None
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