Update to 2026-09-17 pipeline snapshot; add weights, L20 assets and recording via Git LFS

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>
2026-09-17 11:27:46 +08:00
parent 7e4ef6f98b
commit ae28d55f81
761 changed files with 55598 additions and 59 deletions
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+# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+
+import os,sys
+from dataclasses import dataclass, field
+from typing import List, Optional, Tuple,Union
+import numpy as np
+import omegaconf
+import torch
+
+
+@dataclass
+class TrainingConfig(omegaconf.dictconfig.DictConfig):
+    input_resize: tuple = (160, 160)
+    normalize_xyz:Optional[bool] = True
+    use_mask:Optional[bool] = False
+    crop_ratio:Optional[float] = None
+    split_objects_across_gpus: bool = True
+    max_num_key: Optional[int] = None
+    use_normal:bool = False
+    n_view:int = 1
+    zfar:float = np.inf
+    c_in:int = 6
+    train_num_pair:Optional[int] = None
+    make_pair_online:Optional[bool] = False
+    render_backend:Optional[str] = 'nvdiffrast'
+
+    # Run management
+    run_id: Optional[str] = None
+    exp_name:Optional[str] = None
+    resume_run_id: Optional[str] = None
+    save_dir: Optional[str] = None
+    batch_size: int = 64
+    epoch_size: int = 115200
+    val_size: int = 1280
+    n_epochs: int = 25
+    save_epoch_interval: int = 100
+    n_dataloader_workers: int = 20
+    n_rendering_workers: int = 1
+    gradient_max_norm:float = np.inf
+    max_step_per_epoch: Optional[int] = 25000
+
+    # Network
+    use_BN:bool = True
+    loss_type:Optional[str] = 'pairwise_valid'
+
+    # Optimizer
+    optimizer: str = "adam"
+    weight_decay: float = 0.0
+    clip_grad_norm: float = np.inf
+    lr: float = 0.0001
+    warmup_step: int = -1   # -1 means disable
+    n_epochs_warmup: int = 1
+
+    # Visualization
+    vis_interval: Optional[int] = 1000
+
+    debug: Optional[bool] = None
+
+
+
+@dataclass
+class TrainRefinerConfig:
+    # Datasets
+    input_resize: tuple = (160, 160)  #(W,H)
+    crop_ratio:Optional[float] = None
+    max_num_key: Optional[int] = None
+    use_normal:bool = False
+    use_mask:Optional[bool] = False
+    normal_uint8:bool = False
+    normalize_xyz:Optional[bool] = True
+    trans_normalizer:Optional[list] = None
+    rot_normalizer:Optional[float] = None
+    c_in:int = 6
+    n_view:int = 1
+    zfar:float = np.inf
+    trans_rep:str = 'tracknet'  # tracknet/deepim
+    rot_rep:Optional[str] = 'axis_angle'  # 6d/axis_angle
+    save_dir: Optional[str] = None
+
+    # Run management
+    run_id: Optional[str] = None
+    exp_name:Optional[str] = None
+    batch_size: int = 64
+    use_BN:bool = True
+    optimizer: str = "adam"
+    weight_decay: float = 0.0
+    clip_grad_norm: float = np.inf
+    lr: float = 0.0001
+    warmup_step: int = -1
+    loss_type:str = 'l2'   # l1/l2/add
+
+    vis_interval: Optional[int] = 1000
+    debug: Optional[bool] = None
+
+