Repository: NVlabs/GraspGen
Branch: main
Commit: a56d518f3b76
Files: 163
Total size: 14.3 MB
Directory structure:
gitextract_rq0avka1/
├── .gitattributes
├── .gitignore
├── .gitmodules
├── LICENSE
├── LICENSE_ASSETS.md
├── README.md
├── assets/
│ ├── franka/
│ │ ├── franka_panda.urdf
│ │ └── meshes/
│ │ ├── collision/
│ │ │ ├── finger.obj
│ │ │ ├── hand.obj
│ │ │ ├── link0.obj
│ │ │ ├── link1.obj
│ │ │ ├── link2.obj
│ │ │ ├── link3.obj
│ │ │ ├── link4.obj
│ │ │ ├── link5.obj
│ │ │ ├── link6.obj
│ │ │ └── link7.obj
│ │ └── visual/
│ │ ├── finger.dae
│ │ ├── hand.dae
│ │ ├── link0.dae
│ │ ├── link1.dae
│ │ ├── link2.dae
│ │ ├── link3.dae
│ │ ├── link4.dae
│ │ ├── link5.dae
│ │ ├── link6.dae
│ │ └── link7.dae
│ ├── objects/
│ │ ├── banana.obj
│ │ ├── box.obj
│ │ └── example_object.pcd
│ ├── panda_gripper/
│ │ ├── finger.stl
│ │ └── hand.stl
│ ├── robotiq/
│ │ └── robotiq_140_collision.obj
│ └── suction/
│ └── suction_cup.obj
├── client-server/
│ ├── README.md
│ ├── graspgen_client.py
│ └── graspgen_server.py
├── config/
│ └── grippers/
│ ├── franka_panda.py
│ ├── franka_panda.yaml
│ ├── robotiq_2f_140.py
│ ├── robotiq_2f_140.yaml
│ ├── robotiq_2f_85.yaml
│ ├── single_suction_cup_30mm.py
│ └── single_suction_cup_30mm.yaml
├── docker/
│ ├── build.sh
│ ├── compose.serve.yml
│ ├── graspgen_cuda121.dockerfile
│ ├── graspgen_cuda128.dockerfile
│ ├── run.sh
│ ├── run_meshcat.sh
│ ├── run_server.sh
│ └── serve.dockerfile
├── docs/
│ ├── GRASP_DATASET_FORMAT.md
│ └── GRIPPER_DESCRIPTION.md
├── grasp_gen/
│ ├── __init__.py
│ ├── dataset/
│ │ ├── dataset.py
│ │ ├── dataset_utils.py
│ │ ├── eval_utils.py
│ │ ├── exceptions.py
│ │ ├── image_utils.py
│ │ ├── renderer.py
│ │ ├── suction.py
│ │ ├── visualize_utils.py
│ │ └── webdataset_utils.py
│ ├── grasp_server.py
│ ├── metrics.py
│ ├── models/
│ │ ├── action_decoder.py
│ │ ├── contact_decoder.py
│ │ ├── criterion.py
│ │ ├── discriminator.py
│ │ ├── generator.py
│ │ ├── grasp_gen.py
│ │ ├── m2t2.py
│ │ ├── matcher.py
│ │ ├── model_utils.py
│ │ ├── pointnet/
│ │ │ ├── pointnet2.py
│ │ │ ├── pointnet2_modules.py
│ │ │ └── pointnet2_utils.py
│ │ ├── ptv3/
│ │ │ ├── ptv3.py
│ │ │ └── serialization/
│ │ │ ├── __init__.py
│ │ │ ├── default.py
│ │ │ ├── hilbert.py
│ │ │ └── z_order.py
│ │ └── vit.py
│ ├── robot.py
│ ├── serving/
│ │ ├── __init__.py
│ │ ├── zmq_client.py
│ │ └── zmq_server.py
│ └── utils/
│ ├── logging_config.py
│ ├── math_utils.py
│ ├── meshcat_utils.py
│ ├── plot_utils.py
│ ├── point_cloud_utils.py
│ ├── rotation_conversions.py
│ ├── so3.py
│ ├── train_utils.py
│ └── viser_utils.py
├── install_pointnet.sh
├── install_uv_pointnet.sh
├── mcp/
│ ├── .python-version
│ ├── README.md
│ ├── pyproject.toml
│ └── src/
│ └── mcp_server_graspgen/
│ ├── __init__.py
│ ├── __main__.py
│ └── server.py
├── pointnet2_ops/
│ ├── MANIFEST.in
│ ├── pointnet2_ops/
│ │ ├── __init__.py
│ │ ├── _ext-src/
│ │ │ ├── include/
│ │ │ │ ├── ball_query.h
│ │ │ │ ├── cuda_utils.h
│ │ │ │ ├── group_points.h
│ │ │ │ ├── interpolate.h
│ │ │ │ ├── sampling.h
│ │ │ │ └── utils.h
│ │ │ └── src/
│ │ │ ├── ball_query.cpp
│ │ │ ├── ball_query_gpu.cu
│ │ │ ├── bindings.cpp
│ │ │ ├── group_points.cpp
│ │ │ ├── group_points_gpu.cu
│ │ │ ├── interpolate.cpp
│ │ │ ├── interpolate_gpu.cu
│ │ │ ├── sampling.cpp
│ │ │ └── sampling_gpu.cu
│ │ ├── _version.py
│ │ ├── pointnet2_modules.py
│ │ └── pointnet2_utils.py
│ └── setup.py
├── pyproject.toml
├── requirements.txt
├── runs/
│ ├── train_graspgen_franka_panda_dis.sh
│ ├── train_graspgen_franka_panda_gen.sh
│ ├── train_graspgen_robotiq_2f_140_dis.sh
│ └── train_graspgen_robotiq_2f_140_gen.sh
├── scripts/
│ ├── config.yaml
│ ├── convert_obj_to_usd.py
│ ├── create_grasp_sim_usd.py
│ ├── demo_collision_free_grasps.py
│ ├── demo_object_mesh.py
│ ├── demo_object_pc.py
│ ├── demo_scene_pc.py
│ ├── download_objects.py
│ ├── inference_graspgen.py
│ ├── inference_m2t2.py
│ ├── run_grasp_sim_omniverse.py
│ ├── save_grasps_to_usd.py
│ ├── train_graspgen.py
│ └── train_m2t2.py
├── setup.py
├── tests/
│ ├── conftest.py
│ ├── test_dataset_format.py
│ ├── test_grasp_server.py
│ ├── test_inference_installation.py
│ ├── test_inference_perf.py
│ ├── test_math_utils.py
│ ├── test_point_cloud_utils.py
│ ├── test_robot.py
│ ├── test_rotation_conversions.py
│ ├── test_serving.py
│ └── test_usd_grasp_pipeline.py
└── tutorials/
├── TUTORIAL.md
├── generate_dataset_suction_single_object.py
├── generate_model_inference_config.py
├── tutorial_train_dis.sh
└── tutorial_train_gen.sh
================================================
FILE CONTENTS
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FILE: .gitattributes
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*.pth filter=lfs diff=lfs merge=lfs -text
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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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Licenses for external assets are listed here:
franka_panda uRDF
----------------
The Franka urdf files in assets/franka/ has minor modifications
from those in the "franka_description" component of the franka_ros package.
Site: https://github.com/frankaemika/franka_ros/
License: https://github.com/frankaemika/franka_ros/blob/0.7.0/NOTICE
https://github.com/frankaemika/franka_ros/blob/0.7.0/LICENSE
Copyright 2017 Franka Emika GmbH
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
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================================================
FILE: README.md
================================================
GraspGen: A Diffusion-based Framework for 6-DOF Grasping
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================================================
FILE: assets/objects/box.obj
================================================
# https://github.com/mikedh/trimesh
v -0.05000000 -0.05000000 -0.05000000
v -0.05000000 -0.05000000 0.05000000
v -0.05000000 0.05000000 -0.05000000
v -0.05000000 0.05000000 0.05000000
v 0.05000000 -0.05000000 -0.05000000
v 0.05000000 -0.05000000 0.05000000
v 0.05000000 0.05000000 -0.05000000
v 0.05000000 0.05000000 0.05000000
f 2 4 1
f 5 2 1
f 1 4 3
f 3 5 1
f 2 8 4
f 6 2 5
f 6 8 2
f 4 8 3
f 7 5 3
f 3 8 7
f 7 6 5
f 8 6 7
================================================
FILE: assets/objects/example_object.pcd
================================================
# .PCD v0.7 - Point Cloud Data file format
VERSION 0.7
FIELDS x y z
SIZE 4 4 4
TYPE F F F
COUNT 1 1 1
WIDTH 7674
HEIGHT 1
VIEWPOINT 0 0 0 1 0 0 0
POINTS 7674
DATA ascii
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================================================
FILE: assets/robotiq/robotiq_140_collision.obj
================================================
# https://github.com/mikedh/trimesh
o robotiq_arg2f_base_link.stl
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o robotiq_arg2f_140_inner_finger.stl_1
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o geometry_1
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o robotiq_arg2f_140_inner_knuckle.stl_1
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FILE: assets/suction/suction_cup.obj
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================================================
FILE: client-server/README.md
================================================
# GraspGen Standalone Server
GraspGen can be run as a standalone ZMQ server so that any application — on the same machine or across the network — can request 6-DOF grasp predictions without importing the model code or needing a GPU.
```
┌──────────────────────┐ ZMQ (tcp) ┌──────────────────────┐
│ Client (any lang) │ ──── point cloud / mesh ──────▶ │ GraspGen Server │
│ - Python / C++ / … │ ◀── grasps + scores ─── │ - GPU, model loaded │
│ - No CUDA needed │ │ - Runs in Docker │
└──────────────────────┘ └──────────────────────┘
```
The server loads a gripper model (Franka Panda, Robotiq 2F-140, or Single Suction Cup 30mm) and listens on a ZMQ REP socket. Clients send point clouds (as numpy arrays serialized with msgpack) and receive back 6-DOF grasp poses and confidence scores.
## With Docker (recommended)
Terminal window 1 — **start the server**:
```bash
# Build the base Docker image (one-time):
bash docker/build.sh
# Start the server (default: Robotiq 2F-140 on port 5556):
MODELS_DIR=/path/to/GraspGenModels docker compose -f docker/compose.serve.yml up --build
# Or with a custom gripper and port:
MODELS_DIR=/path/to/GraspGenModels \
SERVER_ARGS="--gripper_config /models/checkpoints/graspgen_franka_panda.yml --port 5557" \
docker compose -f docker/compose.serve.yml up --build
```
You can customize the loaded gripper by providing `SERVER_ARGS` (see `client-server/graspgen_server.py --help`). Available gripper configs in the checkpoints directory:
- `graspgen_robotiq_2f_140.yml` (default)
- `graspgen_franka_panda.yml`
- `graspgen_single_suction_cup_30mm.yml`
Terminal window 2 — **run the client** (lightweight uv environment, no CUDA needed):
```bash
# Create a client environment (one-time):
uv venv --python 3.10 client-server/.venv
source client-server/.venv/bin/activate
uv pip install pyzmq msgpack msgpack-numpy numpy trimesh
uv pip install -e . --no-deps
# Run the client with a mesh file:
python client-server/graspgen_client.py \
--mesh_file /path/to/GraspGenModels/sample_data/meshes/box.obj \
--mesh_scale 1.0 \
--host localhost --port 5556
# Or with a point cloud file (.pcd / .ply / .xyz / .npy):
python client-server/graspgen_client.py \
--pcd_file assets/objects/example_object.pcd \
--host localhost --port 5556
```
## Without Docker
Terminal window 1 — **start the server**:
```bash
# Activate your GraspGen environment (must have CUDA + all GraspGen dependencies):
conda activate GraspGen # or source .venv/bin/activate
# Install serving dependencies:
pip install pyzmq msgpack msgpack-numpy
# Start the server:
python client-server/graspgen_server.py \
--gripper_config /path/to/GraspGenModels/checkpoints/graspgen_robotiq_2f_140.yml \
--port 5556
```
Terminal window 2 — **run the client**:
```bash
# Create a client environment (one-time):
uv venv --python 3.10 client-server/.venv
source client-server/.venv/bin/activate
uv pip install pyzmq msgpack msgpack-numpy numpy trimesh
uv pip install -e . --no-deps
# Run the client with a mesh file:
python client-server/graspgen_client.py \
--mesh_file /path/to/GraspGenModels/sample_data/meshes/box.obj \
--mesh_scale 1.0 \
--host localhost --port 5556
# Or with a point cloud file:
python client-server/graspgen_client.py \
--pcd_file assets/objects/example_object.pcd \
--host localhost --port 5556
```
## Python Client API
The client only requires `pyzmq`, `msgpack`, `msgpack-numpy`, and `numpy` — no PyTorch or CUDA.
```python
from grasp_gen.serving.zmq_client import GraspGenClient
client = GraspGenClient(host="localhost", port=5556)
# Get server info
print(client.server_metadata)
# {'gripper_name': 'robotiq_2f_140', 'model_name': 'diffusion-discriminator', ...}
# Run inference
grasps, confidences = client.infer(
point_cloud, # (N, 3) numpy float32 array
num_grasps=200, # diffusion samples
topk_num_grasps=100, # return top-k by confidence
)
# grasps: (M, 4, 4) float32 — 6-DOF grasp poses
# confidences: (M,) float32 — grasp quality scores [0, 1]
client.close()
```
## Protocol Reference
The server uses **msgpack** serialization over a **ZMQ REP** socket.
| Request | Fields | Response |
|---------|--------|----------|
| `{"action": "health"}` | — | `{"status": "ok"}` |
| `{"action": "metadata"}` | — | `{"gripper_name": ..., "model_name": ..., ...}` |
| `{"action": "infer", "point_cloud": ndarray, ...}` | `grasp_threshold`, `num_grasps`, `topk_num_grasps`, `min_grasps`, `max_tries`, `remove_outliers` | `{"grasps": ndarray, "confidences": ndarray, "num_grasps": int, "timing": {...}}` |
This makes it straightforward to write clients in any language with ZMQ and msgpack bindings (C++, Rust, etc.).
================================================
FILE: client-server/graspgen_client.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2025, 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.
"""Client that loads a mesh or point cloud, sends it to the GraspGen ZMQ
server, and prints (and optionally visualizes) the returned grasps.
Usage:
# From a mesh file:
python client-server/graspgen_client.py \
--mesh_file /path/to/box.obj --mesh_scale 1.0 \
--host localhost --port 5556
# From a PCD file:
python client-server/graspgen_client.py \
--pcd_file assets/objects/example_object.pcd \
--host localhost --port 5556
# With visualization:
python client-server/graspgen_client.py \
--mesh_file /path/to/box.obj --mesh_scale 1.0 \
--host localhost --port 5556 --visualize
"""
import argparse
import logging
import sys
import time
import numpy as np
import trimesh
from grasp_gen.serving.zmq_client import GraspGenClient
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser(
description="Send a mesh or point cloud to the GraspGen ZMQ server and print grasp results",
)
input_group = parser.add_mutually_exclusive_group(required=True)
input_group.add_argument(
"--mesh_file", type=str, help="Path to a mesh file (.obj / .stl)"
)
input_group.add_argument(
"--pcd_file", type=str, help="Path to a point cloud file (.pcd / .ply / .xyz / .npy)"
)
parser.add_argument(
"--mesh_scale", type=float, default=1.0, help="Scale factor for the mesh (only used with --mesh_file)"
)
parser.add_argument(
"--num_sample_points",
type=int,
default=2000,
help="Number of points to sample from the mesh surface (only used with --mesh_file)",
)
parser.add_argument(
"--num_grasps", type=int, default=200, help="Number of grasps to request"
)
parser.add_argument(
"--grasp_threshold",
type=float,
default=-1.0,
help="Confidence threshold (-1.0 = use top-k instead)",
)
parser.add_argument(
"--topk_num_grasps",
type=int,
default=100,
help="Return only top-k grasps",
)
parser.add_argument("--host", type=str, default="localhost", help="Server host")
parser.add_argument("--port", type=int, default=5556, help="Server port")
parser.add_argument(
"--visualize",
action="store_true",
help="Visualize point cloud and grasps in viser (http://localhost:8080)",
)
parser.add_argument(
"--viser_port",
type=int,
default=8080,
help="Port for the viser visualization server (default: 8080)",
)
return parser.parse_args()
def load_point_cloud_from_mesh(mesh_file: str, scale: float, num_points: int) -> np.ndarray:
"""Load a mesh, scale it, sample surface points, and center them."""
mesh = trimesh.load(mesh_file)
mesh.apply_scale(scale)
xyz, _ = trimesh.sample.sample_surface(mesh, num_points)
xyz = np.array(xyz, dtype=np.float32)
xyz -= xyz.mean(axis=0)
return xyz
def load_point_cloud_from_file(pcd_file: str) -> np.ndarray:
"""Load a point cloud from .pcd, .ply, .xyz, or .npy file and center it."""
ext = pcd_file.rsplit(".", 1)[-1].lower()
if ext == "npy":
xyz = np.load(pcd_file).astype(np.float32)
elif ext == "xyz":
xyz = np.loadtxt(pcd_file, dtype=np.float32)
elif ext == "pcd":
xyz = _read_pcd_ascii(pcd_file)
elif ext == "ply":
cloud = trimesh.load(pcd_file)
xyz = np.array(cloud.vertices, dtype=np.float32)
else:
raise ValueError(f"Unsupported point cloud format: .{ext}")
if xyz.ndim != 2 or xyz.shape[1] < 3:
raise ValueError(f"Expected (N, 3+) array, got shape {xyz.shape}")
xyz = xyz[:, :3]
xyz -= xyz.mean(axis=0)
return xyz
def _read_pcd_ascii(path: str) -> np.ndarray:
"""Minimal ASCII PCD reader (FIELDS x y z)."""
points = []
in_data = False
with open(path, "r") as f:
for line in f:
if in_data:
vals = line.strip().split()
if len(vals) >= 3:
points.append([float(vals[0]), float(vals[1]), float(vals[2])])
elif line.strip().startswith("DATA"):
in_data = True
return np.array(points, dtype=np.float32)
def main():
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
)
args = parse_args()
if args.mesh_file:
input_source = args.mesh_file
logger.info("Loading mesh: %s (scale=%.2f)", args.mesh_file, args.mesh_scale)
point_cloud = load_point_cloud_from_mesh(
args.mesh_file, args.mesh_scale, args.num_sample_points
)
logger.info("Sampled %d points from mesh surface", len(point_cloud))
else:
input_source = args.pcd_file
logger.info("Loading point cloud: %s", args.pcd_file)
point_cloud = load_point_cloud_from_file(args.pcd_file)
logger.info("Loaded %d points from file", len(point_cloud))
logger.info("Connecting to GraspGen server at %s:%d ...", args.host, args.port)
with GraspGenClient(host=args.host, port=args.port) as client:
metadata = client.server_metadata
logger.info("Server metadata: %s", metadata)
logger.info("Sending inference request ...")
t0 = time.monotonic()
grasps, confidences = client.infer(
point_cloud,
num_grasps=args.num_grasps,
grasp_threshold=args.grasp_threshold,
topk_num_grasps=args.topk_num_grasps,
)
elapsed_ms = (time.monotonic() - t0) * 1000
print(f"\n{'='*60}")
print(f" GraspGen ZMQ Client Results")
print(f"{'='*60}")
print(f" Input : {input_source}")
print(f" Points sent : {len(point_cloud)}")
print(f" Grasps returned : {len(grasps)}")
if len(grasps) > 0:
print(f" Confidence range: {confidences.min():.4f} - {confidences.max():.4f}")
print(f" Best grasp pose :")
print(f" {grasps[0]}")
print(f" Round-trip time : {elapsed_ms:.1f} ms")
print(f"{'='*60}\n")
if args.visualize and len(grasps) > 0:
visualize_results(
point_cloud,
grasps,
confidences,
gripper_name=metadata.get("gripper_name", "franka_panda"),
viser_port=args.viser_port,
)
return 0 if len(grasps) > 0 else 1
def visualize_results(
point_cloud: np.ndarray,
grasps: np.ndarray,
confidences: np.ndarray,
gripper_name: str,
viser_port: int,
):
"""Visualize the point cloud and grasps using the GraspGen viser utilities."""
from grasp_gen.utils.viser_utils import (
create_visualizer,
get_color_from_score,
visualize_grasp,
visualize_pointcloud,
)
vis = create_visualizer(port=viser_port)
pc_color = np.ones((len(point_cloud), 3), dtype=np.uint8) * 200
visualize_pointcloud(vis, "point_cloud", point_cloud, pc_color, size=0.003)
scores = get_color_from_score(confidences, use_255_scale=True)
for i, grasp in enumerate(grasps):
grasp = grasp.copy()
grasp[3, 3] = 1.0
visualize_grasp(
vis,
f"grasps/{i:03d}",
grasp,
color=scores[i],
gripper_name=gripper_name,
linewidth=0.6,
)
print(f"\nViser visualization running at http://localhost:{viser_port}")
print("Press Ctrl+C to exit.")
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
pass
if __name__ == "__main__":
sys.exit(main())
================================================
FILE: client-server/graspgen_server.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2025, 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.
"""Start a GraspGen ZMQ inference server.
Usage:
# Serve with a specific gripper checkpoint config:
python client-server/graspgen_server.py --gripper_config /models/checkpoints/graspgen_franka_panda.yml
# Custom port:
python client-server/graspgen_server.py --gripper_config /models/checkpoints/graspgen_robotiq_2f_140.yml --port 5557
# Bind to localhost only:
python client-server/graspgen_server.py --gripper_config /models/checkpoints/graspgen_franka_panda.yml --host 127.0.0.1
"""
import argparse
import logging
def parse_args():
parser = argparse.ArgumentParser(
description="Start a GraspGen ZMQ inference server",
formatter_class=argparse.RawDescriptionHelpFormatter,
)
parser.add_argument(
"--gripper_config",
type=str,
required=True,
help="Path to gripper configuration YAML file (e.g. checkpoints/graspgen_franka_panda.yml)",
)
parser.add_argument(
"--host",
type=str,
default="0.0.0.0",
help="Address to bind the ZMQ socket (default: 0.0.0.0)",
)
parser.add_argument(
"--port",
type=int,
default=5556,
help="Port to bind the ZMQ socket (default: 5556)",
)
return parser.parse_args()
def main():
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
)
args = parse_args()
from grasp_gen.serving.zmq_server import GraspGenZMQServer
server = GraspGenZMQServer(
gripper_config=args.gripper_config,
host=args.host,
port=args.port,
)
server.serve_forever()
if __name__ == "__main__":
main()
================================================
FILE: config/grippers/franka_panda.py
================================================
import torch
import numpy as np
import trimesh
import os
from grasp_gen.robot import load_control_points_core, load_default_gripper_config
from pathlib import Path
import trimesh.transformations as tra
class GripperModel():
def __init__(self, data_root_dir=None):
if data_root_dir is None:
data_root_dir = f'{Path(__file__).parent.parent.parent}/assets/panda_gripper'
fn_base = data_root_dir + "/hand.stl"
fn_finger = data_root_dir + "/finger.stl"
self.base = trimesh.load(fn_base)
self.finger_l = trimesh.load(fn_finger)
self.finger_r = self.finger_l.copy()
# transform fingers relative to the base
self.finger_l.apply_transform(tra.euler_matrix(0, 0, np.pi))
self.finger_l.apply_translation([0.04, 0, 0.0584])
self.finger_r.apply_translation([-0.04, 0, 0.0584])
self.fingers = trimesh.util.concatenate([self.finger_l, self.finger_r])
self.mesh = trimesh.util.concatenate([self.fingers, self.base])
def set_offset(self, offset):
self.finger_l.apply_translation([offset / 2, 0, 0.0584])
self.finger_r.apply_translation([-offset / 2, 0, 0.0584])
def get_gripper_collision_mesh(self):
return self.mesh
def get_gripper_visual_mesh(self):
return self.mesh
def get_gripper_offset_bins():
# For M2T2-only
offset_bins = [
0, 0.00794435329, 0.0158887021, 0.0238330509,
0.0317773996, 0.0397217484, 0.0476660972,
0.055610446, 0.0635547948, 0.0714991435, 0.08
]
offset_bin_weights = [
0.16652107, 0.21488856, 0.37031708, 0.55618503, 0.75124664,
0.93943357, 1.07824539, 1.19423112, 1.55731375, 3.17161779
]
return offset_bins, offset_bin_weights
def load_control_points() -> torch.Tensor:
"""
Load the control points for the gripper, used for training.
Returns a tensor of shape (4, N) where N is the number of control points.
"""
gripper_config = load_default_gripper_config(Path(__file__).stem)
control_points = load_control_points_core(gripper_config)
control_points = np.vstack([control_points, np.zeros(3)])
control_points = np.hstack([control_points, np.ones([len(control_points), 1])])
control_points = torch.from_numpy(control_points).float()
return control_points.T
def load_control_points_for_visualization():
gripper_config = load_default_gripper_config(Path(__file__).stem)
control_points = load_control_points_core(gripper_config)
mid_point = (control_points[0] + control_points[1]) / 2
control_points = [
control_points[-2], control_points[0], mid_point,
[0, 0, 0], mid_point, control_points[1], control_points[-1]
]
return [control_points, ]
================================================
FILE: config/grippers/franka_panda.yaml
================================================
_target_: graspsampling.hands.PandaGripper
contact_points:
- link_name: panda_leftfinger
- link_name: panda_rightfinger
physics_parameters:
file_name: "urdf/franka_description/robots/franka_panda_gripper_spherical_dof_acronym.urdf"
default_config: [0, 0, 0, 0, 0, 0, 0.04, 0.04]
options:
flip_visual_attachments: True
dof_properties:
driveMode: [1, 1, 1, 1, 1, 1, 2, 2]
stiffness: [1e3, 1e3, 1e3, 1e3, 1e3, 1e3, 0, 0]
damping: [50, 50, 50, 50, 50, 50, 800, 800]
effort: 1400.0
policy:
closing_velocity: []
gripper_type: [panda_hand]
shake_rot_axis: [0, 1, 0]
width: 0.10537486
depth: 0.10527314
transform_offset_from_asset_to_graspgen_convention: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]] # expressed as translation and quaternion[[xyz],[xyzw]]
symmetric_antipodal: True
================================================
FILE: config/grippers/robotiq_2f_140.py
================================================
import torch
import numpy as np
import trimesh
import os
from grasp_gen.robot import load_control_points_core, load_default_gripper_config
from pathlib import Path
class GripperModel(object):
def __init__(self, data_root_dir=None):
if data_root_dir is None:
data_root_dir = f'{Path(__file__).parent.parent.parent}/assets/robotiq'
fn_base = data_root_dir + "/robotiq_140_collision.obj"
self.mesh = trimesh.load(fn_base)
def get_gripper_collision_mesh(self):
return self.mesh
def get_gripper_visual_mesh(self):
return self.mesh
def get_gripper_offset_bins():
offset_bins = [
0.0, 0.01360345836, 0.02720691672,
0.04081037508, 0.05441383344, 0.0680172918,
0.08162075016, 0.09522420852, 0.10882766688,
0.12243112524000001, 0.1360345836
]
offset_bin_weights = [
0.16652107, 0.21488856, 0.37031708, 0.55618503, 0.75124664,
0.93943357, 1.07824539, 1.19423112, 1.55731375, 3.17161779
]
return offset_bins, offset_bin_weights
def load_control_points() -> torch.Tensor:
"""
Load the control points for the gripper, used for training.
Returns a tensor of shape (4, N) where N is the number of control points.
"""
gripper_config = load_default_gripper_config(Path(__file__).stem)
control_points = load_control_points_core(gripper_config)
control_points = np.vstack([control_points, np.zeros(3)])
control_points = np.hstack([control_points, np.ones([len(control_points), 1])])
control_points = torch.from_numpy(control_points).float()
return control_points.T
def load_control_points_for_visualization():
gripper_config = load_default_gripper_config(Path(__file__).stem)
control_points = load_control_points_core(gripper_config)
mid_point = (control_points[0] + control_points[1]) / 2
control_points = [
control_points[-2], control_points[0], mid_point,
[0, 0, 0], mid_point, control_points[1], control_points[-1]
]
return [control_points, ]
================================================
FILE: config/grippers/robotiq_2f_140.yaml
================================================
_target_: graspsampling.hands.URDFHand
file_name: /home/clemens/code/graspsampling-py/data/hands/robotiq_2f_140/urdf/robotiq_arg2f_140_model.urdf
grasp_frame: base_frame
maximum_aperture: 0.12
approach_direction: [0., 0., 1.]
open_configurations:
- [0.]
close_configurations:
- [0.7]
width: 0.13603458
depth: 0.1950
closing_regions:
- extents: [0.12, 0.02, 0.06]
translation: [0.0, 0.0, 0.195]
rotation_wxyz: [1., 0., 0., 0.]
contact_points:
- link_name: right_inner_finger_pad_geometry_9
location: [-6.80172918e-02, 0, 0.195]
normal: [1., 0., 0.]
- link_name: left_inner_finger_pad_geometry_4
location: [6.80172918e-02, 0, 0.195]
normal: [-1., 0., 0.]
transform_offset_from_asset_to_graspgen_convention: [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0]] # expressed as translation and quaternion[[xyz],[xyzw]]
symmetric_antipodal: True
================================================
FILE: config/grippers/robotiq_2f_85.yaml
================================================
_target_: graspsampling.hands.URDFHand
file_name: /home/clemens/code/graspsampling-py/data/hands/robotiq_2f_85/urdf/robotiq_arg2f_85_model.urdf
grasp_frame: base_frame
maximum_aperture: 0.12
approach_direction: [0., 0., 1.]
open_configurations:
- [0.]
close_configurations:
- [0.8]
closing_regions:
- extents: [0.08, 0.02, 0.035]
translation: [0.0, 0.0, 0.13]
rotation_wxyz: [1., 0., 0., 0.]
contact_points:
- link_name: left_inner_finger_pad_geometry_9
location: [4.61807407e-02, -3.67749690e-05, 1.30324000e-01]
normal: [-1., 0., 0.]
- link_name: right_inner_finger_pad_geometry_14
location: [-4.61807407e-02, 3.67749690e-05, 1.30324000e-01]
normal: [1., 0., 0.]
================================================
FILE: config/grippers/single_suction_cup_30mm.py
================================================
import torch
import numpy as np
import trimesh
import os
from grasp_gen.robot import load_control_points_core, load_default_gripper_config, load_visualize_control_points_multi_suction, parse_offset_transform_from_yaml
from pathlib import Path
import trimesh.transformations as tra
class GripperModel(object):
def __init__(self, data_root_dir=None, simplified=True):
if data_root_dir is None:
data_root_dir = f'{Path(__file__).parent.parent.parent}/assets/suction'
fn_base = data_root_dir + "/suction_cup.obj"
self.mesh = trimesh.load(fn_base).dump(concatenate=True)
self.simplified = simplified
def get_gripper_collision_mesh(self):
if self.simplified:
return self.mesh.bounding_cylinder
else:
return self.mesh
def get_gripper_visual_mesh(self):
return self.mesh
def load_visualize_control_points_suction():
pts = [
[0.0, 0, 0],
]
return load_visualize_control_points_multi_suction(pts)
def load_control_points() -> torch.Tensor:
"""
Load the control points for the gripper, used for training.
Returns a tensor of shape (4, N) where N is the number of control points.
"""
gripper_config = load_default_gripper_config(Path(__file__).stem)
control_points = load_control_points_core(gripper_config)
control_points = np.hstack([control_points, np.ones([len(control_points), 1])])
control_points = torch.from_numpy(control_points).float()
return control_points.T
def load_control_points_for_visualization():
gripper_config = load_default_gripper_config(Path(__file__).stem)
# TODO: Move this to gripper python script
control_points = load_visualize_control_points_suction()
# NOTE - This is the transform offset from converting a gripper from its original axis to the graspgen convention
if "transform_offset_from_asset_to_graspgen_convention" in gripper_config:
offset_transform = parse_offset_transform_from_yaml(gripper_config['transform_offset_from_asset_to_graspgen_convention'])
else:
offset_transform = np.eye(4)
ctrl_pts = [tra.transform_points(cpt, offset_transform) for cpt in control_points]
depth = gripper_config['depth']
line_pts = np.array([[0,0,0], [0,0,depth]])
line_pts = np.expand_dims(line_pts, 0)
line_pts = [tra.transform_points(cpt, offset_transform) for cpt in line_pts]
line_pts = line_pts[0]
ctrl_pts.append(line_pts)
return ctrl_pts
def get_transform_from_base_link_to_tool_tcp():
"""
This is because the gripper base_link is coincident with the tool TCP.
"""
return tra.translation_matrix([0, 0, 0.0])
================================================
FILE: config/grippers/single_suction_cup_30mm.yaml
================================================
poses:
- [[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]
num_sides: 15
radius: 0.015
height: 0.03
friction_mu: 0.5
material_kappa: 0.005
suction_force: 250.0
interpolated_perimeter_vertices: 0
spring_max_length_change: 0.1
standoff_distance: 0.001
num_suction_points_for_hole_check: 50
collision_mesh_fname: "assets/suction/suction_cup.obj"
depth: 0.069
control_points:
- [0, 0, 0]
- [0, 0, 0.050]
- [0, 0, 0.075]
================================================
FILE: docker/build.sh
================================================
VER=1.0
docker build -f docker/graspgen_cuda121.dockerfile --progress=plain . --network=host -t graspgen:$VER -t graspgen:latest
================================================
FILE: docker/compose.serve.yml
================================================
# GraspGen ZMQ Inference Server (Docker Compose)
#
# Usage:
# # From the GraspGen repo root:
#
# # 1. Build the base image (one time):
# bash docker/build.sh
#
# # 2. Start the server (default: Robotiq 2F-140 on port 5556):
# MODELS_DIR=/path/to/GraspGenModels docker compose -f docker/compose.serve.yml up --build
#
# # 3. Custom gripper config:
# MODELS_DIR=/path/to/GraspGenModels \
# SERVER_ARGS="--gripper_config /models/checkpoints/graspgen_franka_panda.yml --port 5557" \
# docker compose -f docker/compose.serve.yml up --build
#
# The server binds on 0.0.0.0: with network_mode: host,
# so clients can reach it at tcp://:.
services:
graspgen_server:
image: graspgen-server
build:
context: ..
dockerfile: docker/serve.dockerfile
init: true
tty: true
network_mode: host
volumes:
- ${MODELS_DIR:?Set MODELS_DIR to your GraspGenModels path}:/models
environment:
- NVIDIA_DISABLE_REQUIRE=1
- NVIDIA_DRIVER_CAPABILITIES=all
- SERVER_ARGS
command: >
${SERVER_ARGS:---gripper_config /models/checkpoints/graspgen_robotiq_2f_140.yml --port 5556}
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
shm_size: "8g"
================================================
FILE: docker/graspgen_cuda121.dockerfile
================================================
# Build base image
FROM nvcr.io/nvidia/pytorch:23.07-py3 AS base
# tmux is for debugging, osmesa is for rendering. Put all apt-get installs in this line
RUN apt update && apt-get install -y tmux libosmesa6-dev
RUN pip install --upgrade pip
# Install dependencies
RUN pip install h5py hydra-core matplotlib meshcat scikit-learn scipy tensorboard trimesh==4.5.3
# Install scene_synthesizer
RUN pip install scene-synthesizer[recommend]
RUN pip install torch==2.1.0 torchvision
# Install torch_cluster
RUN pip install torch-cluster -f https://data.pyg.org/whl/torch-2.1.0+cu121.html
RUN pip install imageio pickle5 opencv-python python-fcl
# Install pyrender
RUN pip install pyrender==0.1.45 pyglet==2.1.6 && pip install PyOpenGL==3.1.5
# Install pointnet2 modules
COPY pointnet2_ops pointnet2_ops
RUN pip install ./pointnet2_ops
# Diffusion dependencies
RUN pip install diffusers==0.11.1 timm==1.0.15
RUN pip install huggingface-hub==0.25.2
# PointTransformerV3 dependencies
RUN pip install addict yapf==0.40.1 tensorboardx sharedarray torch-geometric
RUN pip install torch-scatter -f https://data.pyg.org/whl/torch-2.1.2+cu121.html
RUN pip install spconv-cu120
# For the analytic model
RUN pip install qpsolvers[clarabel]
# This was not installed before
RUN pip install yourdfpy==0.0.56
# Not sure why this is needed again
RUN pip install trimesh==4.5.3 timm==1.0.15
# For dataset management and manifold
RUN pip install webdataset
RUN curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | bash
RUN apt-get install git-lfs
RUN pip install objaverse==0.1.7
RUN mkdir -p /install/
RUN cd /install/ && git clone --recursive -j8 https://github.com/hjwdzh/Manifold.git
RUN mkdir -p /install/Manifold/build
RUN cd /install/Manifold/build
RUN cd /install/Manifold/build && cmake .. -DCMAKE_BUILD_TYPE=Release
RUN cd /install/Manifold/build && make
ENV PATH="${PATH}:/install/Manifold/build/"
WORKDIR /code/
================================================
FILE: docker/graspgen_cuda128.dockerfile
================================================
# Build base image
FROM nvcr.io/nvidia/pytorch:25.03-py3 AS base
# tmux is for debugging, osmesa is for rendering. Put all apt-get installs in this line
RUN apt update && apt-get install -y tmux libosmesa6-dev
RUN pip install --upgrade pip
# Install dependencies
RUN pip install h5py hydra-core matplotlib meshcat scikit-learn scipy tensorboard trimesh==4.5.3
# Install scene_synthesizer
RUN pip install scene-synthesizer[recommend]
RUN pip install torch==2.1.0 torchvision
# Install torch_cluster
RUN pip install torch-cluster -f https://data.pyg.org/whl/torch-2.7.0+cu128.html
RUN pip install imageio opencv-python python-fcl
# Install pyrender
RUN pip install pyrender==0.1.45 pyglet==2.1.6 && pip install PyOpenGL==3.1.5
# Install pointnet2 modules
COPY pointnet2_ops pointnet2_ops
RUN pip install ./pointnet2_ops
# Diffusion dependencies
RUN pip install diffusers==0.11.1 timm==1.0.15
RUN pip install huggingface-hub==0.25.2
# PointTransformerV3 dependencies
RUN pip install addict yapf==0.40.1 tensorboardx sharedarray torch-geometric
RUN pip install torch-scatter -f https://data.pyg.org/whl/torch-2.1.2+cu121.html
# RUN pip install spconv-cu128 # This is not available yet: https://github.com/Pointcept/PointTransformerV3/issues/159
# For the analytic model
RUN pip install qpsolvers[clarabel]
# This was not installed before
RUN pip install yourdfpy==0.0.56
# Not sure why this is needed again
RUN pip install trimesh==4.5.3 timm==1.0.15
# For dataset management and manifold
RUN pip install webdataset
RUN curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | bash
RUN apt-get install git-lfs
RUN pip install objaverse==0.1.7
RUN mkdir -p /install/
RUN cd /install/ && git clone --recursive -j8 https://github.com/hjwdzh/Manifold.git
RUN mkdir -p /install/Manifold/build
RUN cd /install/Manifold/build
RUN cd /install/Manifold/build && cmake .. -DCMAKE_BUILD_TYPE=Release
RUN cd /install/Manifold/build && make
ENV PATH="${PATH}:/install/Manifold/build/"
WORKDIR /code/
================================================
FILE: docker/run.sh
================================================
#!/bin/bash
# Function to display usage information
show_usage() {
echo "Usage: $0 [--models ] [--grasp_dataset --object_dataset --results ]"
echo ""
echo "Arguments:"
echo " grasp_gen_code_dir (required) Path to the GraspGen code repository (relative or absolute)"
echo " --models (optional) Path to the models directory (for inference, relative or absolute)"
echo " --grasp_dataset (optional) Path to the grasp dataset directory (for training, relative or absolute)"
echo " --object_dataset (optional) Path to the object dataset directory (for training, relative or absolute)"
echo " --results (optional) Path to the results directory (for training outputs, relative or absolute)"
echo ""
echo "Examples:"
echo " # For inference demo (with models):"
echo " $0 /path/to/graspgen/code --models /path/to/models"
echo " $0 ./graspgen --models ./models"
echo ""
echo " # For training (with grasp dataset, object dataset, and results):"
echo " $0 /path/to/graspgen/code --grasp_dataset /path/to/grasp_dataset --object_dataset /path/to/object_dataset --results /path/to/results"
echo " $0 ./graspgen --grasp_dataset ./datasets/grasp --object_dataset ./datasets/objects --results ./output"
echo ""
echo "Note: grasp_gen_code_dir is always mounted at /code"
echo " models_dir (if provided) is mounted at /models"
echo " grasp_dataset_dir (if provided) is mounted at /grasp_dataset"
echo " object_dataset_dir (if provided) is mounted at /object_dataset"
echo " results_dir (if provided) is mounted at /results"
echo " All paths are converted to absolute paths for Docker volume mounting"
exit 1
}
# Function to convert path to absolute path
make_absolute_path() {
local path="$1"
if [[ "$path" = /* ]]; then
# Already absolute path
echo "$path"
else
# Relative path, convert to absolute
echo "$(realpath "$path")"
fi
}
# Check if at least the required argument is provided
if [ $# -lt 1 ]; then
show_usage
fi
GRASP_GEN_CODE_DIR="$(make_absolute_path "$1")"
MODELS_DIR=""
GRASP_DATASET_DIR=""
OBJECT_DATASET_DIR=""
RESULTS_DIR=""
# Parse optional arguments
shift # Remove the first argument (grasp_gen_code_dir)
while [[ $# -gt 0 ]]; do
case $1 in
--models)
MODELS_DIR="$(make_absolute_path "$2")"
shift 2
;;
--grasp_dataset)
GRASP_DATASET_DIR="$(make_absolute_path "$2")"
shift 2
;;
--object_dataset)
OBJECT_DATASET_DIR="$(make_absolute_path "$2")"
shift 2
;;
--results)
RESULTS_DIR="$(make_absolute_path "$2")"
shift 2
;;
-h|--help)
show_usage
;;
*)
echo "Unknown option: $1"
show_usage
;;
esac
done
# Validate that the required directory exists
if [ ! -d "$GRASP_GEN_CODE_DIR" ]; then
echo "Error: grasp_gen_code_dir '$GRASP_GEN_CODE_DIR' does not exist or is not a directory"
exit 1
fi
# Build volume mount arguments
VOLUME_MOUNTS="-v ${GRASP_GEN_CODE_DIR}:/code"
# Add models directory if provided
if [ -n "$MODELS_DIR" ]; then
if [ ! -d "$MODELS_DIR" ]; then
echo "Error: models_dir '$MODELS_DIR' does not exist or is not a directory"
exit 1
fi
VOLUME_MOUNTS="$VOLUME_MOUNTS -v ${MODELS_DIR}:/models"
fi
# Add grasp dataset directory if provided
if [ -n "$GRASP_DATASET_DIR" ]; then
if [ ! -d "$GRASP_DATASET_DIR" ]; then
echo "Error: grasp_dataset_dir '$GRASP_DATASET_DIR' does not exist or is not a directory"
exit 1
fi
VOLUME_MOUNTS="$VOLUME_MOUNTS -v ${GRASP_DATASET_DIR}:/grasp_dataset"
fi
# Add object dataset directory if provided
if [ -n "$OBJECT_DATASET_DIR" ]; then
if [ ! -d "$OBJECT_DATASET_DIR" ]; then
echo "Error: object_dataset_dir '$OBJECT_DATASET_DIR' does not exist or is not a directory"
exit 1
fi
VOLUME_MOUNTS="$VOLUME_MOUNTS -v ${OBJECT_DATASET_DIR}:/object_dataset"
fi
# Add results directory if provided
if [ -n "$RESULTS_DIR" ]; then
if [ ! -d "$RESULTS_DIR" ]; then
echo "Creating results directory: $RESULTS_DIR"
mkdir -p "$RESULTS_DIR"
fi
VOLUME_MOUNTS="$VOLUME_MOUNTS -v ${RESULTS_DIR}:/results"
fi
echo "Starting Docker container with:"
echo " Code directory: $GRASP_GEN_CODE_DIR -> /code"
if [ -n "$MODELS_DIR" ]; then
echo " Models directory: $MODELS_DIR -> /models (for inference)"
fi
if [ -n "$GRASP_DATASET_DIR" ]; then
echo " Grasp dataset directory: $GRASP_DATASET_DIR -> /grasp_dataset (for training)"
fi
if [ -n "$OBJECT_DATASET_DIR" ]; then
echo " Object dataset directory: $OBJECT_DATASET_DIR -> /object_dataset (for training)"
fi
if [ -n "$RESULTS_DIR" ]; then
echo " Results directory: $RESULTS_DIR -> /results (for training outputs)"
fi
echo ""
xhost +local:root
docker run \
--privileged \
-e NVIDIA_DISABLE_REQUIRE=1 \
-e NVIDIA_DRIVER_CAPABILITIES=all \
--device /dev/dri \
-it \
-e DISPLAY \
$VOLUME_MOUNTS \
--gpus all \
--net host \
--shm-size 40G \
graspgen:latest \
/bin/bash \
-c "cd /code/ && pip install -e . && bash" \
xhost -local:root
================================================
FILE: docker/run_meshcat.sh
================================================
#!/bin/bash
docker run \
--gpus all \
-e NVIDIA_DRIVER_CAPABILITIES=compute,graphics,utility \
--runtime nvidia \
-e NVIDIA_DISABLE_REQUIRE=1 \
graspgen:latest \
meshcat-server
================================================
FILE: docker/run_server.sh
================================================
#!/bin/bash
# Start the GraspGen ZMQ inference server in Docker.
#
# Usage:
# bash docker/run_server.sh --models [--gripper_config CONFIG] [--port PORT]
#
# Examples:
# # Default (Robotiq 2F-140 on port 5556):
# bash docker/run_server.sh $(pwd) --models /path/to/GraspGenModels
#
# # Franka Panda on port 5557:
# bash docker/run_server.sh $(pwd) --models /path/to/GraspGenModels \
# --gripper_config /models/checkpoints/graspgen_franka_panda.yml --port 5557
set -e
show_usage() {
echo "Usage: $0 --models [--gripper_config CONFIG] [--port PORT]"
echo ""
echo "Arguments:"
echo " graspgen_code_dir Path to the GraspGen code repository"
echo " --models Path to GraspGenModels directory"
echo " --gripper_config Gripper config inside the container (default: /models/checkpoints/graspgen_robotiq_2f_140.yml)"
echo " --port ZMQ port (default: 5556)"
exit 1
}
make_absolute_path() {
local path="$1"
if [[ "$path" = /* ]]; then echo "$path"; else echo "$(realpath "$path")"; fi
}
if [ $# -lt 3 ]; then show_usage; fi
GRASPGEN_CODE_DIR="$(make_absolute_path "$1")"
shift
MODELS_DIR=""
GRIPPER_CONFIG="/models/checkpoints/graspgen_robotiq_2f_140.yml"
PORT=5556
while [[ $# -gt 0 ]]; do
case $1 in
--models) MODELS_DIR="$(make_absolute_path "$2")"; shift 2 ;;
--gripper_config) GRIPPER_CONFIG="$2"; shift 2 ;;
--port) PORT="$2"; shift 2 ;;
-h|--help) show_usage ;;
*) echo "Unknown option: $1"; show_usage ;;
esac
done
if [ -z "$MODELS_DIR" ]; then echo "Error: --models is required"; show_usage; fi
if [ ! -d "$GRASPGEN_CODE_DIR" ]; then echo "Error: $GRASPGEN_CODE_DIR not found"; exit 1; fi
if [ ! -d "$MODELS_DIR" ]; then echo "Error: $MODELS_DIR not found"; exit 1; fi
echo "Starting GraspGen ZMQ server in Docker ..."
echo " Code : $GRASPGEN_CODE_DIR -> /code"
echo " Models : $MODELS_DIR -> /models"
echo " Config : $GRIPPER_CONFIG"
echo " Port : $PORT"
docker run \
--rm \
--gpus all \
--net host \
--shm-size 8G \
-e NVIDIA_DISABLE_REQUIRE=1 \
-e NVIDIA_DRIVER_CAPABILITIES=all \
-v "${GRASPGEN_CODE_DIR}:/code" \
-v "${MODELS_DIR}:/models" \
graspgen:latest \
/bin/bash -c "cd /code && pip install -q -e . && pip install -q pyzmq msgpack msgpack-numpy && python tools/graspgen_server.py --gripper_config ${GRIPPER_CONFIG} --port ${PORT}"
================================================
FILE: docker/serve.dockerfile
================================================
# GraspGen ZMQ Inference Server
#
# This Dockerfile builds on top of the graspgen:latest base image
# (built via `bash docker/build.sh`) and adds ZMQ serving dependencies.
#
# Build standalone:
# docker build -f docker/serve.dockerfile -t graspgen-server:latest .
#
# Or via compose:
# docker compose -f docker/compose.serve.yml up --build
FROM graspgen:latest
RUN pip install pyzmq msgpack msgpack-numpy
COPY . /code
WORKDIR /code
RUN pip install -e .
EXPOSE 5556
ENTRYPOINT ["python", "tools/graspgen_server.py"]
CMD ["--gripper_config", "/models/checkpoints/graspgen_robotiq_2f_140.yml", "--port", "5556"]
================================================
FILE: docs/GRASP_DATASET_FORMAT.md
================================================
# GraspGen Gripper Description
version: `v.1.0.0`
GraspGen expects the dataset to be in the following format.
1. **Splits**: The objects are separated into the training and validation/test sets. Each line in the `*.txt` file can be a uuid (in the case of Objaverse) or a relative path to the object mesh (obj/stl) file (relative to the root of the object dataset). If you are using the same object for both training and testing, include them in both lists.
```
path/to/splits/
train.txt
valid.txt
```
2. **Grasp Dataset**: The grasps are specified in separate directory
```
path/to/grasp/data/
*.json
```
Each json file inside grasp dataset has following information:
```
{
"object": {
"file": # relative path to object asset in the object dataset
"scale": # scale for the object mesh at which the grasps were sampled and evaluated
},
"grasps": {
"transforms": # 4x4 homogeous transformation matrix of the base link of gripper
"object_in_gripper": # mask to distinguish successful vs. unsuccessful grasps
}
}
```
The json file can be loaded in python as follows:
```
import json
import numpy as np
grasps_dict = json.load(open("/path/to/json/file", "r"))
object_file = grasps_dict["object"]["file"]
object_scale = grasps_dict["object"]["scale"]
grasps = np.array(grasps_dict["grasps"]["transforms"])
grasp_mask = np.array(grasps_dict["grasps"]["object_in_gripper"])
positive_grasps = grasps[grasp_mask]
negative_grasps = grasps[~grasp_mask]
```
================================================
FILE: docs/GRIPPER_DESCRIPTION.md
================================================
# GraspGen Gripper Description
version: `v.1.0.0`
A gripper is specified by its unique name "gripper_name", which is an argument in the training script as well as the basename for the config files.
Each gripper need to have three assets defined
1) YAML Config `{gripper_name}.yaml`
2) Python Config `{gripper_name}.py`
3) URDF for just the gripper
### YAML Config
`{gripper_name}.yaml` has the following key variables used for data generation and training:
- `width`: Distance between the fingers at its max joint angle in the open configuration
- `depth`: Extents of the gripper along the z-axis, from base link frame to TCP frame
- `transform_offset_from_asset_to_graspgen_convention`: Expressed as a list of translation and quaternion e.g. `[[xyz],[xyzw]]`
Our gripper definition follows the following convention shown below. The approach direction is the positive Z-axis while the gripper finger closing direction is along the X-axis.
If your gripper has a different convention, please use the `transform_offset_from_asset_to_graspgen_convention` variable to bring your gripper to this convention. The original URDF does not need to be modified.
Examples of the frames overlaid on the 3 gripper models we provided in the dataset (Suction, Robotiq 2f-140 and Franka-Panda) are shown below:
### Python Config
The following variables and functions need to be defined in the python script `{gripper_name}.py`:
- class `GripperModel`: Specifies how the collision and visual meshes of the gripper can be loaded from the gripper URDF
- func `load_control_points_for_visualization`: Loads the control points needed for visualization in meshcat
— func `load_control_points`: Loads the control points needed for applying training metrics
Optional:
get_transform_from_base_link_to_tool_tcp:
If this function is not specified, this is assumed to be a fixed positive Z-offset based on the `depth` value.
If you have feedback about this convetion, please add a GitHub issue!
================================================
FILE: grasp_gen/__init__.py
================================================
from grasp_gen.utils.logging_config import setup_logging
# Set up logging when the package is imported
setup_logging()
# Version info
__version__ = "0.1.0"
================================================
FILE: grasp_gen/dataset/dataset.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""
Data loader for training grasp models.
"""
import glob
import json
import logging
import os
import random
import time
from typing import Tuple, Union
import h5py
import numpy as np
try:
import pickle5 as pickle
except:
import pickle
import torch
import trimesh
import trimesh.transformations as tra
from omegaconf import DictConfig
from sklearn.neighbors import KDTree
from torch.utils.data import Dataset
from tqdm import tqdm
from grasp_gen.dataset.dataset_utils import (
GraspGenDatasetCache,
ObjectGraspDataset,
dump_object_list,
filter_grasps_by_point_cloud_visibility,
load_from_json,
load_object_grasp_data,
GraspJsonDatasetReader,
get_rotation_augmentation,
)
from grasp_gen.dataset.eval_utils import check_collision
from grasp_gen.dataset.exceptions import DataLoaderError
# NOTE: render_pc is imported lazily where needed to avoid forcing pyrender import in headless environments
from grasp_gen.dataset.visualize_utils import (
MAPPING_ID2NAME,
MAPPING_NAME2ID,
visualize_discriminator_dataset,
visualize_generator_dataset,
)
from grasp_gen.dataset.webdataset_utils import GraspWebDatasetReader, is_webdataset
from grasp_gen.utils.meshcat_utils import (
create_visualizer,
visualize_pointcloud,
visualize_grasp,
)
from grasp_gen.robot import get_gripper_info
from grasp_gen.utils.logging_config import get_logger
# Configure logging
logger = get_logger(__name__)
logger.setLevel(logging.INFO)
if not logger.handlers:
handler = logging.StreamHandler()
handler.setFormatter(
logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
)
logger.addHandler(handler)
def get_cache_path(cache_dir: str, root_dir: str) -> str:
if not os.path.exists(cache_dir):
os.makedirs(cache_dir, exist_ok=True)
cache_name = os.path.basename(root_dir)
cache_path = os.path.join(cache_dir, cache_name)
os.makedirs(cache_path, exist_ok=True)
return cache_path
def get_cache_prefix(prob_point_cloud: float, load_discriminator_dataset: bool) -> str:
cache_name = get_pc_setting_name(prob_point_cloud)
prefix = "dis" if load_discriminator_dataset else "gen"
cache_name = f"{cache_name}_{prefix}"
return cache_name
def get_pc_setting_name(prob_point_cloud: float) -> str:
name = "mesh" if prob_point_cloud <= 0 else "pc"
if prob_point_cloud < 1.0 and prob_point_cloud > 0:
name = "meshandpc"
return name
def get_denylist_path(
cache_dir: str,
root_dir: str,
prob_point_cloud: float,
load_discriminator_dataset: bool,
) -> str:
cache_path = get_cache_path(cache_dir, root_dir)
prefix = get_cache_prefix(prob_point_cloud, load_discriminator_dataset)
return os.path.join(cache_path, f"denylist_{prefix}.json")
def is_valid_cache_dir(cfg: DictConfig) -> bool:
dataset_cache_dir = get_cache_path(cfg.cache_dir, cfg.root_dir)
if not os.path.exists(dataset_cache_dir):
return False
json_files = glob.glob(os.path.join(dataset_cache_dir, "denylist_*.json"))
if len(json_files) == 0:
logger.info(
f"[CACHE VALIDATION] No denylist found in {dataset_cache_dir}, deleting cache dir. It may have been improperly created before."
)
os.system(f"rm -rf {dataset_cache_dir}") # Save from future misery
return False
data_files = glob.glob(os.path.join(dataset_cache_dir, "*.h5"))
if len(data_files) == 0:
logger.info(
f"[CACHE VALIDATION] No data files found in {dataset_cache_dir}, deleting cache dir. It may have been improperly created before."
)
os.system(f"rm -rf {dataset_cache_dir}") # Save from future misery
return False
if len(data_files) > 0:
for split in ["train", "valid"]:
prefix = get_cache_prefix(
cfg.prob_point_cloud, cfg.load_discriminator_dataset
)
data_files = glob.glob(
os.path.join(dataset_cache_dir, f"cache_{split}_{prefix}.h5")
)
if len(data_files) == 0:
logger.info(
f"[CACHE VALIDATION] No cache files found for split {split}, skipping"
)
return False
path_to_h5_file = data_files[0]
logger.info(
f"[CACHE VALIDATION] Found cache file for split {split}: {path_to_h5_file}. Loading cache..."
)
try:
dataset_cache = GraspGenDatasetCache.load_from_h5_file(path_to_h5_file)
except OSError as e:
logger.error(
f"[CACHE VALIDATION] Error loading cache file {path_to_h5_file}: {e}"
)
logger.error(
f"[CACHE VALIDATION] Cache file {path_to_h5_file} is not valid, deleting cache dir. It may have been improperly created before."
)
os.system(f"rm -rf {path_to_h5_file}") # Save from future misery
return False
except KeyError as e:
logger.error(
f"[CACHE VALIDATION] KeyError loading cache file {path_to_h5_file}: {e}"
)
logger.error(
f"[CACHE VALIDATION] Cache file {path_to_h5_file} is not valid, deleting cache dir. It may have been improperly created before."
)
os.system(f"rm -rf {path_to_h5_file}") # Save from future misery
return False
cache_dir = cfg.cache_dir
root_dir = cfg.root_dir
prob_point_cloud = cfg.prob_point_cloud
load_discriminator_dataset = cfg.load_discriminator_dataset
denylist_path = get_denylist_path(
cache_dir, root_dir, prob_point_cloud, load_discriminator_dataset
)
denylist = load_from_json(denylist_path)
if len(denylist) > 0:
denylist = list(denylist.keys())
splits_file_path = f"{root_dir}/{split}.txt"
scenes = load_scenes_from_text_file(splits_file_path)
logger.info(f"Loading dataset from config: {splits_file_path}")
for idx in tqdm(range(len(scenes))):
key = scenes[idx]
if key not in dataset_cache:
if key not in denylist:
logger.info(
f"[CACHE VALIDATION] Cache for split {split} is only partially complete. {key} is not in cache. {len(dataset_cache._cache.keys())}/{int(len(scenes) - len(denylist))} completed"
)
return False
logger.info(
f"[CACHE VALIDATION] Cache for split {split} is complete. It has been properly generated."
)
return True
def load_grasps_with_contacts(
data: dict,
pts: torch.Tensor,
seg: torch.Tensor,
world2cam: torch.Tensor,
contact_radius: float,
offset_bins: torch.Tensor,
inference: bool,
load_grasp: bool,
) -> dict:
names, masks, grasping_masks, matched_grasps = [], [], [], []
contact_dirs = torch.zeros_like(pts)
approach_dirs = torch.zeros_like(pts)
offsets = torch.zeros_like(pts[..., 0])
num_grasps = 0
for name, info in data["objects"].items():
seg_id = info["seg_id"][()]
mask = seg == seg_id
if seg_id == 1:
continue
if mask.sum() == 0:
# object is not visible, skip
continue
masks.append(mask)
names.append(name)
has_grasp = False
if not load_grasp:
continue
if "grasps" in info:
contacts = torch.from_numpy(info["contacts"][...]).float()
grasps = torch.from_numpy(info["grasps"][...]).float()
num_grasps += len(grasps)
if len(grasps) == 0:
continue
if world2cam is not None:
# convert contacts and grasps to camera coordinate
contacts = contacts @ world2cam[:3, :3].T + world2cam[:3, 3]
grasps = world2cam @ grasps
if len(contacts.shape) == 3:
contact_dir = contacts[:, 1] - contacts[:, 0]
offset = contact_dir.norm(dim=1)
contact_dir = contact_dir / offset.unsqueeze(1)
approach_dir = grasps[:, :3, 2]
# Mx2x3 -> 2Mx3
contacts = contacts.transpose(0, 1).reshape(-1, 3)
contact_dir = torch.cat([contact_dir, -contact_dir])
approach_dir = torch.cat([approach_dir, approach_dir])
offset = torch.cat([offset, offset])
grasps = torch.cat([grasps, grasps])
else:
assert len(contacts.shape) == 2
contact_dir = grasps[:, :3, 0]
approach_dir = grasps[:, :3, 2]
offset = 0.02 * torch.ones(len(contact_dir))
tree = KDTree(contacts.numpy())
dist, idx = tree.query(pts[mask].numpy())
matched = dist < contact_radius
idx = idx[matched]
grasps = grasps[idx]
if matched.sum() > 0:
pt_i = torch.where(mask)[0]
contact_mask = torch.zeros_like(mask)
contact_mask[pt_i[matched[:, 0]]] = 1
contact_dirs[contact_mask] = contact_dir[idx]
approach_dirs[contact_mask] = approach_dir[idx]
offsets[contact_mask] = offset[idx]
grasping_masks.append(contact_mask)
matched_grasps.append(grasps)
has_grasp = True
elif inference:
grasping_masks.append(torch.zeros_like(mask))
matched_grasps.append(torch.zeros(0, 4, 4))
else:
pass
elif inference:
grasping_masks.append(torch.zeros_like(mask))
matched_grasps.append(torch.zeros(0, 4, 4))
if len(grasping_masks) > 0:
grasping_masks = torch.stack(grasping_masks).float()
else:
# No grasp, skip
return {"invalid": True}
num_matched_grasps = np.sum([len(grasps_obj) for grasps_obj in matched_grasps])
if num_matched_grasps == 0:
# TODO - Root cause this issue
# No grasp, skip
return {"invalid": True}
contact_any_obj = grasping_masks.any(dim=0)
contact_dirs = contact_dirs[contact_any_obj]
approach_dirs = approach_dirs[contact_any_obj]
offsets = offsets[contact_any_obj]
outputs = {
"names": names,
"instance_masks": torch.stack(masks).float(),
"grasping_masks": grasping_masks,
"contact_dirs": contact_dirs,
"approach_dirs": approach_dirs,
"grasps": matched_grasps,
}
if offset_bins is not None:
if inference:
outputs["offsets"] = offsets
else:
labels = torch.bucketize(offsets, torch.tensor(offset_bins)) - 1
outputs["offsets"] = torch.clip(labels, 0, len(offset_bins) - 1)
# TODO: Explain clearly - this is for when offsets go OOB - some issue with contact points
if outputs["offsets"].max().item() == 10:
return {"invalid": True}
return outputs
def load_scenes_from_text_file(file_path: str) -> list:
"""
Load object IDs or relative paths to object meshes from a text file.
Each line in the text file should contain one object ID or relative path.
Lines are stripped of whitespace and empty lines are ignored.
Args:
file_path: Path to the text file containing object IDs/paths
Returns:
List of object IDs or relative paths as strings
Raises:
FileNotFoundError: If the text file doesn't exist
"""
all_objects = []
with open(file_path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line: # Skip empty lines
all_objects.append(line)
return all_objects
class PickDataset(Dataset):
def __init__(
self,
root_dir,
cache_dir,
split,
tasks,
num_points,
num_obj_points,
cam_coord,
num_rotations,
grid_res,
jitter_scale,
contact_radius,
dist_above_table,
offset_bins,
robot_prob,
random_seed,
inference=False,
scenes=None,
rotation_augmentation=False,
downsample_points=True,
add_depth_noise=False,
load_patch=False,
patch_width=200,
prob_point_cloud=-1.0,
object_root_dir="",
grasp_root_dir="",
dataset_name="acronym",
dataset_version="v0",
gripper_name="franka_panda",
num_grasps_per_object=20,
load_discriminator_dataset=False,
load_contact=False,
prefiltering=False,
discriminator_ratio=[0.50, 0.20, 0.25, 0.05, 0.0],
visualize_batch=False,
onpolicy_dataset_dir=None,
onpolicy_dataset_h5_path=None,
preload_dataset=False,
redundancy=1,
):
self.split = split
self.scenes, self.scene2h5 = [], {}
self.load_patch = load_patch
cache_dir = cache_dir
self.patch_width = patch_width
self.load_contact = load_contact
self.prob_point_cloud = prob_point_cloud
self.preload_dataset = preload_dataset
self.redundancy = redundancy
self.load_discriminator_dataset = load_discriminator_dataset
self._prefiltering = prefiltering
self.denylist_path = get_denylist_path(
cache_dir, root_dir, prob_point_cloud, load_discriminator_dataset
)
self._cache_dir = get_cache_path(cache_dir, root_dir)
logger.info(f"cache_dir: {self._cache_dir}")
logger.info(f"Denylist path: {self.denylist_path}")
self.denylist = load_from_json(self.denylist_path)
if len(self.denylist) > 0:
if type(self.denylist) == dict:
self.denylist = list(self.denylist.keys())
file_path = f"{root_dir}/{self.split}.txt"
self.scenes = load_scenes_from_text_file(file_path)
logger.info(f"Loading dataset from config: {file_path}")
if len(self.denylist) > 0:
init_datapoints = len(self.scenes)
self.scenes = [scene for scene in self.scenes if scene not in self.denylist]
logger.info(
f"{init_datapoints - len(self.scenes)} out of {init_datapoints} scenes in denylist. Final number of datapoints {len(self.scenes)}"
)
else:
logger.info("No filtering; All datapoints are used")
self.overfitting_mode = False
if len(self.scenes) == 1:
multiplier = 100 if self.split == "train" else 10
if self.split != "train" and not rotation_augmentation:
multiplier = 1
self.scenes = [self.scenes[0]] * multiplier
self.overfitting_mode = True
self.load_patch = load_patch
self.num_points = num_points
self.num_obj_points = num_obj_points
self.cam_coord = cam_coord
self.num_rotations = num_rotations
self.grid_res = grid_res
self.jitter_scale = jitter_scale
self.robot_prob = robot_prob
self.contact_radius = contact_radius
self.dist_above_table = dist_above_table
self.random_seed = random_seed
self.inference = inference
self.rotation_augmentation = rotation_augmentation
self.downsample_points = downsample_points
self.add_depth_noise = add_depth_noise
self.root_dir = root_dir
self.object_root_dir = object_root_dir
self.grasp_root_dir = grasp_root_dir
self.dataset_name = dataset_name
self.dataset_version = dataset_version
self.gripper_name = gripper_name
self.num_grasps_per_object = num_grasps_per_object
self.gripper_info = get_gripper_info(self.gripper_name)
self.gripper_collision_mesh = self.gripper_info.collision_mesh
self.gripper_visual_mesh = self.gripper_info.visual_mesh
self.offset_bins = self.gripper_info.offset_bins
self.discriminator_ratio = discriminator_ratio
self.visualize_batch = visualize_batch
self.onpolicy_dataset_h5_path = onpolicy_dataset_h5_path
self.onpolicy_dataset_dir = onpolicy_dataset_dir
# Initialize the appropriate grasp dataset reader
if is_webdataset(self.grasp_root_dir):
self.grasp_dataset_reader = GraspWebDatasetReader(self.grasp_root_dir)
else:
self.grasp_dataset_reader = GraspJsonDatasetReader(self.grasp_root_dir)
self.onpolicy_dataset_h5_path = onpolicy_dataset_h5_path
self.onpolicy_dataset_dir = onpolicy_dataset_dir
if self.onpolicy_dataset_dir is not None:
self.load_onpolicy_dataset()
if self.preload_dataset:
self.load_cache()
def load_onpolicy_dataset(self):
"""
Loads the onpolicy dataset.
"""
assert self.onpolicy_dataset_h5_path is not None
assert self.onpolicy_dataset_dir is not None
# The below is to load the onpolicy dataset for the validation set. if no directory is found, it is set to None
if type(self.onpolicy_dataset_h5_path) == str and self.split == "valid":
if self.onpolicy_dataset_h5_path.find("train") >= 0:
onpolicy_dataset_h5_path_valid = self.onpolicy_dataset_h5_path.replace(
"train", "valid"
)
onpolicy_dataset_dir_valid = self.onpolicy_dataset_dir.replace(
"train", "valid"
)
if os.path.exists(onpolicy_dataset_h5_path_valid):
logger.info(
f"Onpolicy dataset: Loading onpolicy dataset from {onpolicy_dataset_h5_path_valid}"
)
self.onpolicy_dataset_h5_path = onpolicy_dataset_h5_path_valid
self.onpolicy_dataset_dir = onpolicy_dataset_dir_valid
else:
self.onpolicy_dataset_h5_path = None
self.onpolicy_dataset_dir = None
if self.onpolicy_dataset_h5_path is not None:
# Computing the mapping from object ids to json paths in the onpolicy dataset
json_path_onpolicy_filename = os.path.join(
self._cache_dir, f"{os.path.basename(self.onpolicy_dataset_dir)}.json"
)
logger.info("Precomputing all the json file mapping for onpolicy dataset")
t0 = time.time()
if os.path.exists(json_path_onpolicy_filename):
logger.info(
f"Onpolicy dataset: Loading json path from cached json file {json_path_onpolicy_filename}"
)
self.map_key_to_json_path_online_dataset = load_from_json(
json_path_onpolicy_filename
)
else:
logger.info(
f"Onpolicy dataset: Loading json path from scratch {json_path_onpolicy_filename}"
)
import h5py
from tqdm import tqdm
from grasp_gen.dataset.dataset_utils import (
get_json_file_given_object_id,
)
possible_grasp_keys = [
"grasps.json",
"*suction_grasps.json",
] # This is because ACRONYM pipeline generates grasp jsons with different names
json_files = []
for grasp_key in possible_grasp_keys:
logger.info(f"Searching using grasp key {grasp_key}")
json_files += sorted(
glob.glob(
f"{self.onpolicy_dataset_dir}/**/{grasp_key}",
recursive=True,
)
)
json_files += sorted(
glob.glob(
f"{self.onpolicy_dataset_dir}/**/**/{grasp_key}",
recursive=True,
)
) # This is to account for the case when the dataset is built with MapReduce
h5 = h5py.File(self.onpolicy_dataset_h5_path, "r")
h5_object_ids = list(h5["objects"].keys())
map_object_uuid_to_id = {}
map_object_id_to_uuid = {}
for h5_object_id in h5_object_ids:
uuid = (
h5["objects"][h5_object_id]["asset_path"][...]
.item()
.decode("utf-8")
)
map_object_uuid_to_id[uuid] = h5_object_id
map_object_id_to_uuid[h5_object_id] = uuid
map_basenameuuid_to_json_file = {}
logger.info(f"Onpolicy dataset: Parsing json file to UUID mappings")
for json_file_path in tqdm(json_files):
json_path_object_id = load_from_json(json_file_path)["object"][
"file"
]
map_basenameuuid_to_json_file[
os.path.basename(json_path_object_id)
] = json_file_path
self.map_key_to_json_path_online_dataset = {}
for idx in tqdm(range(self.__len__())):
key = self.scenes[idx]
if key not in map_object_id_to_uuid:
logger.info(f"Onpolicy dataset: Key {key} not in json dataset")
continue
try:
json_path = map_basenameuuid_to_json_file[
os.path.basename(map_object_id_to_uuid[key])
]
except:
logger.info(
f"Onpolicy dataset: File path {map_object_id_to_uuid[key]}, key {key} not in dataset yet "
)
continue
self.map_key_to_json_path_online_dataset[key] = json_path
dump_object_list(
self.map_key_to_json_path_online_dataset,
json_path_onpolicy_filename,
)
logger.info(f"Onpolicy dataset: That took {time.time() - t0}s. Phew...")
def load_cache(self, cache_save_freq: int = 2000):
"""
Converts the dataset into a cached file, loads to system memory.
"""
from tqdm import tqdm
assert self.preload_dataset
logger.info("Preloading dataset to memory")
cache_save_path = ""
save_emd_data = False
all_emd_data = {}
prefix = get_cache_prefix(
self.prob_point_cloud, self.load_discriminator_dataset
)
cache_name = f"cache_{self.split}_{prefix}.h5"
cache_save_path = os.path.join(self._cache_dir, cache_name)
if os.path.exists(cache_save_path):
self.cache = GraspGenDatasetCache.load_from_h5_file(cache_save_path)
else:
logger.info(f"Unable to find cache file: {cache_save_path}")
self.cache = GraspGenDatasetCache()
if len(self.cache) > 0:
logger.info(f"Cache already has {len(self.cache)} objects")
idx_cache_loaded = 0
for idx in tqdm(range(self.__len__())):
key = self.scenes[idx]
if key in self.cache:
# Skip if the object is already in the cache
continue
else:
idx_cache_loaded += 1
onpolicy_json_path = None
onpolicy_data_found = True
if self.onpolicy_dataset_dir is not None:
try:
onpolicy_json_path = self.map_key_to_json_path_online_dataset[key]
except:
onpolicy_data_found = False
error_code, object_grasp_data = load_object_grasp_data(
key,
self.object_root_dir,
self.grasp_root_dir,
self.dataset_version,
load_discriminator_dataset=self.load_discriminator_dataset,
gripper_info=self.gripper_info,
onpolicy_dataset_dir=self.onpolicy_dataset_dir,
onpolicy_dataset_h5_path=self.onpolicy_dataset_h5_path,
onpolicy_json_path=onpolicy_json_path,
onpolicy_data_found=onpolicy_data_found,
grasp_dataset_reader=self.grasp_dataset_reader,
)
if error_code != DataLoaderError.SUCCESS:
self.save_to_denylist(self.denylist_path, key, error_code)
continue
# This is for plotting only EMD data
if save_emd_data:
from grasp_gen.dataset.dataset_utils import compute_emd_data
emd_data = compute_emd_data(object_grasp_data)
all_emd_data[key] = emd_data
rendering_output = []
POINT_CLOUD_REDUNDANCY = 3
for _ in range(self.redundancy):
mesh_mode = (
False if np.random.random() <= self.prob_point_cloud else True
)
load_contact_batch = self.load_contact
# Lazy import to avoid forcing pyrender in headless environments
from grasp_gen.dataset.renderer import render_pc
outputs, error_code = render_pc(
object_grasp_data,
self.num_points * POINT_CLOUD_REDUNDANCY,
mesh_mode=mesh_mode,
)
if error_code == DataLoaderError.RENDERING_SUCCESS:
# Check if there are grasps in the visibile area of the point clouds
T_move_to_pc_mean = outputs["T_move_to_pc_mean"]
grasps = np.array(
[
T_move_to_pc_mean @ g
for g in object_grasp_data.positive_grasps.copy()
]
)
mask_grasp_visibility = filter_grasps_by_point_cloud_visibility(
grasps,
outputs["points"],
transform_from_base_link_to_tool_tcp=self.gripper_info.transform_from_base_link_to_tool_tcp,
)
if mask_grasp_visibility is not None:
positive_grasps = object_grasp_data.positive_grasps.copy()[
mask_grasp_visibility
]
outputs["mesh_mode"] = mesh_mode
outputs["load_contact_batch"] = load_contact_batch
outputs["positive_grasps"] = positive_grasps
rendering_output.append(outputs)
else:
error_code = (
DataLoaderError.RENDERING_NO_GRASPS_IN_VISIBLE_POINT_CLOUD
)
if len(rendering_output) == 0:
logger.error(
f"{idx}: Unable to preload object even after sampling pc {self.redundancy} times"
)
self.save_to_denylist(self.denylist_path, key, error_code)
continue
self.cache[key] = (object_grasp_data, rendering_output)
if idx_cache_loaded % cache_save_freq == 0:
self.cache.save_to_h5_file(cache_save_path)
# NOTE - This round of postprocessing is needed but not sure why. It is a fail safe.
id_key_pair_missing = []
denylist = load_from_json(self.denylist_path)
for idx in tqdm(range(self.__len__())):
key = self.scenes[idx]
if key not in self.cache:
if key not in denylist:
logger.info(f"Key not in cache {key} for some reason")
self.save_to_denylist(
self.denylist_path, key, DataLoaderError.AMBIGUOUS_LOAD_ERROR
)
id_key_pair_missing.append(key)
if len(id_key_pair_missing) > 0:
logger.info(
f"{len(id_key_pair_missing)} keys were not found in the cache and were forcibly added into the denylist. Need to be rootcaused"
)
# print(id_key_pair_missing)
# If any new data was loaded, save final version for the entire dataset. This will not enter if the cache is already complete.
if idx_cache_loaded > 0:
self.cache.save_to_h5_file(cache_save_path)
if save_emd_data:
with open(os.path.join(self._cache_dir, "emd_data_100.json"), "w") as f:
json.dump(all_emd_data, f)
def save_to_denylist(self, denylist_path, key, error_code):
if self._prefiltering:
logger.info(f"Saving key: {key} to denylist at {denylist_path}")
if os.path.exists(denylist_path):
denylist = load_from_json(denylist_path)
else:
denylist = {}
denylist[key] = (error_code.code, error_code.description)
os.system(f"rm -rf {denylist_path}")
dump_object_list(denylist, denylist_path)
@classmethod
def from_config(cls, cfg):
args = {}
args["root_dir"] = cfg.root_dir
args["cache_dir"] = cfg.cache_dir
args["tasks"] = cfg.tasks
args["num_points"] = cfg.num_points
args["num_obj_points"] = cfg.num_object_points
args["cam_coord"] = cfg.cam_coord
args["num_rotations"] = cfg.num_rotations
args["grid_res"] = cfg.grid_resolution
args["jitter_scale"] = cfg.jitter_scale
args["contact_radius"] = cfg.contact_radius
args["dist_above_table"] = cfg.dist_above_table
args["offset_bins"] = cfg.offset_bins
args["robot_prob"] = cfg.robot_prob
args["random_seed"] = cfg.random_seed
args["rotation_augmentation"] = cfg.rotation_augmentation
args["downsample_points"] = cfg.downsample_points
args["add_depth_noise"] = cfg.add_depth_noise
args["load_patch"] = cfg.load_patch
args["patch_width"] = cfg.patch_width
args["prob_point_cloud"] = cfg.prob_point_cloud
args["object_root_dir"] = cfg.object_root_dir
args["grasp_root_dir"] = cfg.grasp_root_dir
args["dataset_name"] = cfg.dataset_name
args["dataset_version"] = cfg.dataset_version
args["gripper_name"] = cfg.gripper_name
args["num_grasps_per_object"] = cfg.num_grasps_per_object
args["load_discriminator_dataset"] = cfg.load_discriminator_dataset
args["load_contact"] = cfg.load_contact
args["prefiltering"] = cfg.prefiltering
args["discriminator_ratio"] = cfg.discriminator_ratio
args["visualize_batch"] = cfg.visualize_batch
args["onpolicy_dataset_dir"] = cfg.onpolicy_dataset_dir
args["onpolicy_dataset_h5_path"] = cfg.onpolicy_dataset_h5_path
args["preload_dataset"] = cfg.preload_dataset
args["redundancy"] = cfg.redundancy
return args
def __len__(self):
return len(self.scenes)
def __getitem__(self, idx):
raise NotImplementedError("This method should be implemented by the subclass")
class ObjectPickDataset(PickDataset):
def calculate_dataset_kappa(self) -> float:
"""Calculate the mean extent of grasps across all objects in the dataset. See method section in the paper.
The extent is calculated as the difference between max and min grasp positions
along each axis, averaged across all objects.
Returns:
float: Kappa - Mean grasp extent K across all objects
"""
from copy import deepcopy as copy
list_minmax = []
for j in range(self.__len__()):
key = self.scenes[j]
if key in self.cache:
(object_grasp_data, outputs_red) = self.cache[key]
outputs = copy(random.choice(outputs_red))
mesh_mode = outputs["mesh_mode"]
load_contact_batch = outputs["load_contact_batch"]
mask = torch.randint(0, outputs["points"].shape[0], (self.num_points,))
outputs["points"] = outputs["points"][mask]
T_move_to_pc_mean = outputs["T_move_to_pc_mean"]
grasps = object_grasp_data.positive_grasps.copy()
grasps = np.array([T_move_to_pc_mean @ g for g in grasps])
min_max = grasps[:, :3, 3].max(axis=0) - grasps[:, :3, 3].min(axis=0)
list_minmax.append(min_max)
kappa = 1 / np.array(list_minmax).mean()
return kappa
def __getitem__(self, idx):
key = self.scenes[idx]
if self.preload_dataset:
from copy import deepcopy as copy
if key not in self.cache:
# print(f"Key {key} not in cache")
denylist = load_from_json(self.denylist_path)
if key not in denylist:
logger.info(f"Key {key} not in cache and not in denylist")
self.save_to_denylist(
self.denylist_path, key, DataLoaderError.AMBIGUOUS_LOAD_ERROR
)
return {"invalid": True}
(object_grasp_data, outputs_red) = self.cache[key]
outputs = copy(random.choice(outputs_red))
mesh_mode = outputs["mesh_mode"]
load_contact_batch = outputs["load_contact_batch"]
mask = torch.randint(0, outputs["points"].shape[0], (self.num_points,))
outputs["points"] = outputs["points"][mask]
else:
onpolicy_json_path = None
onpolicy_data_found = True
if self.onpolicy_dataset_dir is not None:
try:
onpolicy_json_path = self.map_key_to_json_path_online_dataset[key]
except:
onpolicy_data_found = False
error_code, object_grasp_data = load_object_grasp_data(
key,
self.object_root_dir,
self.grasp_root_dir,
self.dataset_version,
load_discriminator_dataset=self.load_discriminator_dataset,
gripper_info=self.gripper_info,
onpolicy_dataset_dir=self.onpolicy_dataset_dir,
onpolicy_dataset_h5_path=self.onpolicy_dataset_h5_path,
onpolicy_json_path=onpolicy_json_path,
onpolicy_data_found=onpolicy_data_found,
grasp_dataset_reader=self.grasp_dataset_reader,
)
if object_grasp_data is None:
self.save_to_denylist(self.denylist_path, key, error_code)
# Invalid datapoint
return {"invalid": True}
mesh_mode = False if np.random.random() <= self.prob_point_cloud else True
load_contact_batch = self.load_contact
# Lazy import to avoid forcing pyrender in headless environments
from grasp_gen.dataset.renderer import render_pc
outputs, error_code = render_pc(
object_grasp_data, self.num_points, mesh_mode=mesh_mode
)
if error_code != DataLoaderError.RENDERING_SUCCESS:
# Invalid point cloud rendering
self.save_to_denylist(self.denylist_path, key, error_code)
return {"invalid": True}
T_move_to_pc_mean = outputs["T_move_to_pc_mean"]
grasps = np.array(
[
T_move_to_pc_mean @ g
for g in object_grasp_data.positive_grasps.copy()
]
)
mask_grasp_visibility = filter_grasps_by_point_cloud_visibility(
grasps,
outputs["points"],
transform_from_base_link_to_tool_tcp=self.gripper_info.transform_from_base_link_to_tool_tcp,
)
if mask_grasp_visibility is not None:
positive_grasps = object_grasp_data.positive_grasps.copy()[
mask_grasp_visibility
]
outputs["positive_grasps"] = positive_grasps
else:
if error_code == DataLoaderError.RENDERING_SUCCESS:
error_code = (
DataLoaderError.RENDERING_NO_GRASPS_IN_VISIBLE_POINT_CLOUD
)
# Invalid point cloud rendering
self.save_to_denylist(self.denylist_path, key, error_code)
return {"invalid": True}
T_move_to_pc_mean = outputs["T_move_to_pc_mean"]
grasps = outputs["positive_grasps"]
grasps = np.array([T_move_to_pc_mean @ g for g in grasps])
# Extra stuff added to outputs later. TODO - Clean this all up
xyz = outputs["points"]
if type(xyz) == np.ndarray:
xyz = torch.from_numpy(xyz).float()
num_points = self.num_points
seg = 5 * np.ones(num_points).astype(np.int32)
rgb = (
150.0
* np.vstack(
[np.ones(num_points), np.zeros(num_points), np.zeros(num_points)]
).T
)
cam_pose = np.eye(4)
rgb = torch.from_numpy(rgb).float()
seg = torch.from_numpy(seg).float()
cam_pose = torch.from_numpy(cam_pose).float()
outputs["inputs"] = torch.cat([xyz, rgb], dim=1)
outputs["seg"] = seg
outputs["rgb"] = rgb
outputs["cam_pose"] = cam_pose
data = {
"objects": {
"obj0": {
"seg_id": np.array(5),
"grasps": grasps,
}
}
}
outputs["scene"] = self.scenes[idx]
outputs["task"] = "pick"
cam_pose = outputs["cam_pose"] if self.cam_coord else None
world2cam = outputs["cam_pose"].inverse() if self.cam_coord else None
grasps_ground_truth = object_grasp_data.positive_grasps.copy()
grasps_ground_truth = np.array(
[T_move_to_pc_mean @ g for g in grasps_ground_truth]
)
outputs["grasps_ground_truth"] = grasps_ground_truth
if self.load_discriminator_dataset:
negative_grasps = object_grasp_data.negative_grasps.copy()
if negative_grasps is not None:
negative_grasps = np.array(
[T_move_to_pc_mean @ g for g in negative_grasps]
)
outputs["negative_grasps"] = negative_grasps
if load_contact_batch:
contacts = object_grasp_data.contacts.copy()
assert contacts is not None
if len(contacts.shape) == 3:
contacts[:, 0, :] = tra.transform_points(
contacts[:, 0, :], T_move_to_pc_mean
)
contacts[:, 1, :] = tra.transform_points(
contacts[:, 1, :], T_move_to_pc_mean
)
else:
contacts = tra.transform_points(contacts, T_move_to_pc_mean)
data["objects"]["obj0"]["contacts"] = contacts
outputs.update(
load_grasps_with_contacts(
data,
outputs["points"],
outputs["seg"],
world2cam,
self.contact_radius,
self.offset_bins,
self.inference,
True,
)
)
# outputs['grasps'] = outputs['grasps'][0]
outputs.update(
{
"ee_pose": torch.eye(4),
"obj_pose": torch.eye(4),
"object_inputs": torch.zeros(self.num_obj_points, 6),
"bottom_center": torch.zeros(3),
"object_center": torch.zeros(3),
"placement_masks": torch.zeros(self.num_rotations, self.num_points),
"placement_region": torch.zeros(self.num_points),
}
)
else:
outputs["grasps"] = [
torch.from_numpy(grasps),
]
outputs["names"] = ["obj0"]
if "grasps" not in outputs:
self.save_to_denylist(
self.denylist_path,
key,
DataLoaderError.GRASP_POINT_CLOUD_CORRESPONDENCE_ERROR,
)
return {"invalid": True}
if not self.load_contact:
# Sanitize the dictionary output for diffusion model training.
blacklist_keys = [
"instance_masks",
"grasping_masks",
"contact_dirs",
"approach_dirs",
"offsets",
"ee_pose",
"obj_pose",
"object_inputs",
"bottom_center",
"object_center",
"placement_masks",
"placement_region",
]
for key in blacklist_keys:
if key in outputs:
del outputs[key]
# TODO - make sure rotation_augmentation works with pc?
if self.rotation_augmentation:
pc = outputs["points"]
if type(pc) == torch.Tensor:
pc = pc.cpu().numpy()
if len(pc.shape) == 3 and pc.shape[0] == 1:
pc = pc.squeeze(0)
T_world_to_pcmean = tra.translation_matrix(-pc.mean(axis=0))
T_pcmean_to_world = tra.inverse_matrix(T_world_to_pcmean)
T_rotation = get_rotation_augmentation(stratified_sampling=False)
T_aug = T_pcmean_to_world @ T_rotation @ T_world_to_pcmean
pc = tra.transform_points(pc, T_aug)
xyz = torch.from_numpy(pc).float()
if "grasps_ground_truth" in outputs:
grasps_ground_truth = outputs["grasps_ground_truth"]
outputs["grasps_ground_truth"] = np.array(
[T_aug @ g for g in grasps_ground_truth]
)
if "grasps" in outputs:
output_grasps_rotated = []
for grasps in outputs["grasps"]:
if len(grasps) > 0:
grasps_rotated = np.array(
[T_aug @ g for g in grasps.cpu().numpy()]
)
grasps_rotated = torch.from_numpy(grasps_rotated).float()
else:
grasps_rotated = grasps
output_grasps_rotated.append(grasps_rotated)
outputs["grasps"] = output_grasps_rotated
if self.load_discriminator_dataset:
if "negative_grasps" in outputs:
negative_grasps = outputs["negative_grasps"]
outputs["negative_grasps"] = np.array(
[T_aug @ g for g in negative_grasps]
)
outputs["points"] = xyz
rgb = outputs["rgb"]
center = xyz.mean(dim=0)
outputs["inputs"] = torch.cat([xyz - center, rgb], dim=1)
positive_grasps_onpolicy = None
negative_grasps_onpolicy = None
if self.load_discriminator_dataset:
if object_grasp_data.positive_grasps_onpolicy is not None:
try:
if len(object_grasp_data.positive_grasps_onpolicy) > 0:
positive_grasps_onpolicy = np.copy(
object_grasp_data.positive_grasps_onpolicy
)
positive_grasps_onpolicy[:, 3, 3] = 1.0
positive_grasps_onpolicy = np.array(
[
T_move_to_pc_mean @ np.array(g)
for g in positive_grasps_onpolicy.tolist()
]
)
if self.rotation_augmentation:
positive_grasps_onpolicy = np.array(
[T_aug @ g for g in positive_grasps_onpolicy]
)
except Exception as e:
# print(f"Error loading positive grasps onpolicy: {e}")
positive_grasps_onpolicy = None
if object_grasp_data.negative_grasps_onpolicy is not None:
try:
if len(object_grasp_data.negative_grasps_onpolicy) > 0:
negative_grasps_onpolicy = np.copy(
object_grasp_data.negative_grasps_onpolicy
)
negative_grasps_onpolicy[:, 3, 3] = 1.0
negative_grasps_onpolicy = np.array(
[T_move_to_pc_mean @ g for g in negative_grasps_onpolicy]
)
if self.rotation_augmentation:
negative_grasps_onpolicy = np.array(
[T_aug @ g for g in negative_grasps_onpolicy]
)
except Exception as e:
# print(f"Error loading negative grasps onpolicy: {e}")
negative_grasps_onpolicy = None
if self.inference:
outputs["object_points"] = torch.rand(self.num_obj_points, 3)
obj_asset_path = object_grasp_data.object_asset_path
if obj_asset_path.find(self.object_root_dir) >= 0:
obj_asset_path_rel_idx = obj_asset_path.find(self.object_root_dir) + len(
self.object_root_dir
)
obj_asset_path_rel = obj_asset_path[obj_asset_path_rel_idx:]
else:
obj_asset_path_rel = obj_asset_path
try:
trimesh.load(obj_asset_path)
except:
logger.error(f"Error loading object asset path: {obj_asset_path}")
obj_scale = object_grasp_data.object_scale
obj_pose = T_move_to_pc_mean
if self.rotation_augmentation:
obj_pose = T_aug @ obj_pose
temp_object_path_dir = "/object_dataset/simplified"
if obj_asset_path.find(temp_object_path_dir) >= 0:
if os.path.isdir(temp_object_path_dir):
# if this dir actually exists, skip
pass
else:
assert os.path.isdir(self.object_root_dir)
obj_asset_path = obj_asset_path.replace(temp_object_path_dir, "")
if obj_asset_path.startswith("/"):
obj_asset_path = obj_asset_path[1:]
obj_asset_path = os.path.join(self.object_root_dir, obj_asset_path)
scene_info = {
"assets": [
obj_asset_path,
],
"scales": [
obj_scale,
],
"poses": [
obj_pose,
],
}
outputs["scene_info"] = scene_info
if self.load_discriminator_dataset:
positive_grasps = outputs["grasps"][0]
negative_grasps = (
outputs["negative_grasps"]
if "negative_grasps" in outputs and len(outputs["negative_grasps"]) != 0
else None
)
batch_data, scene_mesh = load_discriminator_batch_with_stratified_sampling(
self.num_grasps_per_object,
positive_grasps,
self.discriminator_ratio,
scene_info,
self.gripper_collision_mesh,
negative_grasps,
positive_grasps_onpolicy=positive_grasps_onpolicy,
negative_grasps_onpolicy=negative_grasps_onpolicy,
)
outputs.update(batch_data)
mask = np.random.randint(0, len(positive_grasps), 1000)
outputs["grasps_highres"] = positive_grasps
if self.visualize_batch:
# if len(object_grasp_data.positive_grasps) < 100:
logger.info(
"Batch Statistics for object: ",
object_grasp_data.object_asset_path,
"\n",
)
logger.info(
f"Positive Grasps: {len(object_grasp_data.positive_grasps)}"
)
logger.info(
f"Negative Grasps: {len(object_grasp_data.negative_grasps)}"
)
try:
logger.info(
f"Positive Grasps Onpolicy: {len(object_grasp_data.positive_grasps_onpolicy)}"
)
logger.info(
f"Negative Grasps Onpolicy: {len(object_grasp_data.negative_grasps_onpolicy)}"
)
except:
pass
visualize_discriminator_dataset(
batch_data,
scene_mesh,
self.gripper_name,
self.gripper_visual_mesh,
pointcloud=outputs["points"],
)
else:
if self.num_grasps_per_object != -1:
grasps_gt = outputs["grasps"][0]
mask_grasps_filtered = np.random.randint(
0, len(grasps_gt), self.num_grasps_per_object
)
outputs["grasps"] = grasps_gt[mask_grasps_filtered]
outputs["grasps_highres"] = grasps_gt
if not load_contact_batch:
for key in ["points"]:
if type(outputs[key]) == np.ndarray:
outputs[key] = torch.from_numpy(outputs[key])
outputs[key] = outputs[key].unsqueeze(0).repeat(1, 1, 1)
outputs["grasps"] = torch.from_numpy(
np.array(outputs["grasps"]).astype(np.float32)
)
if self.visualize_batch:
logger.info(
"Object asset:",
self.scenes[idx],
object_grasp_data.object_asset_path,
)
grasps_gt = outputs["grasps"]
if type(grasps_gt) == list and len(grasps_gt) == 1:
grasps_gt = grasps_gt[0]
if load_contact_batch:
contacts = object_grasp_data.contacts.copy()
pc = outputs["points"].cpu().numpy()
else:
contacts = None
pc = outputs["points"][0].cpu().numpy()
visualize_generator_dataset(
obj_pose,
grasps_gt,
self.gripper_name,
load_contact_batch,
contacts,
pc,
self.gripper_visual_mesh,
)
if len(outputs["points"].shape) == 2:
outputs["points"] = outputs["points"].unsqueeze(0)
return outputs
def generate_negative_hardnegatives(
num_grasps: int,
grasps_starter: np.ndarray,
scene_mesh: trimesh.base.Trimesh,
gripper_mesh: trimesh.base.Trimesh,
) -> Union[None, np.ndarray]:
"""Generate hard negative grasp examples by perturbing positive grasps to create collisions.
This function creates three sets of negative grasp examples:
1. Grasps with perturbed orientations
2. Grasps with perturbed translations along z-axis
3. Grasps with both perturbed orientations and translations
The function then checks for collisions between these grasps and the scene/gripper
meshes to identify valid hard negatives.
Args:
num_grasps (int): Number of hard negative grasps to generate
grasps_starter (np.ndarray): Array of initial positive grasps to perturb, shape (N, 4, 4)
scene_mesh (trimesh.base.Trimesh): Mesh of the scene/object to check collisions against
gripper_mesh (trimesh.base.Trimesh): Mesh of the gripper to check collisions against
Returns:
Union[None, np.ndarray]: Array of hard negative grasps that collide, shape (num_grasps, 4, 4).
Returns None if no valid colliding grasps are found.
"""
if num_grasps == 0:
return
upsample_factor = 0.70 # NOTE: This number is specifically tuned to ensure there are enough colliding grasps to choose from in the next step, which still being minimized (as it introduces delays)
num_grasps_upsampled = int(upsample_factor * num_grasps)
neg_grasps_src = grasps_starter[
np.random.randint(0, len(grasps_starter), num_grasps_upsampled)
]
# Add grasps by perturbing a little bit, the orientation. Make sure these collide
lims = [-np.pi / 6, np.pi / 6]
neg_grasps_candidate_set_1 = np.array(
[
tra.euler_matrix(rot[0], rot[1], rot[2]) @ g
for rot, g in zip(
np.random.uniform(lims[0], lims[1], size=(num_grasps_upsampled, 3)),
neg_grasps_src,
)
]
)
# Add grasps by perturbing the -translation, but fixed orientation. Make sure these collide
lims = [0.03, 0.08]
neg_grasps_candidate_set_2 = np.array(
[
g @ tra.translation_matrix([0, 0, z])
for z, g in zip(
np.random.uniform(lims[0], lims[1], size=num_grasps_upsampled),
neg_grasps_src,
)
]
)
# Do a combination of the two above.
lims_trans = [0.02, 0.06]
lims_rot = [-np.pi / 6, np.pi / 6]
neg_grasps_candidate_set_3 = np.array(
[
tra.euler_matrix(rot[0], rot[1], rot[2])
@ g
@ tra.translation_matrix([0, 0, z])
for z, rot, g in zip(
np.random.uniform(
lims_trans[0], lims_trans[1], size=num_grasps_upsampled
),
np.random.uniform(
lims_rot[0], lims_rot[1], size=(num_grasps_upsampled, 3)
),
neg_grasps_src,
)
]
)
neg_grasp_candidates = np.vstack(
[
# neg_grasps_candidate_set_0,
neg_grasps_candidate_set_1,
neg_grasps_candidate_set_2,
neg_grasps_candidate_set_3,
]
)
collision = check_collision(scene_mesh, gripper_mesh, neg_grasp_candidates)
neg_grasp_hn = neg_grasp_candidates[collision]
# print(f"Hardnegatives: {len(neg_grasp_hn)} out of {len(neg_grasp_candidates)} grasps are colliding")
grasps = None
if len(neg_grasp_hn) != 0:
mask = np.random.randint(0, len(neg_grasp_hn), num_grasps)
# if num_grasps >= len(neg_grasp_hn):
# print(f"Hardnegatives: Subsampling {num_grasps} from {len(neg_grasp_hn)} grasps")
grasps = neg_grasp_hn[mask]
return grasps
def generate_negative_retract(
num_grasps: int, grasps_starter: np.ndarray
) -> np.ndarray:
neg_grasps_src = grasps_starter[
np.random.randint(0, len(grasps_starter), num_grasps)
]
# Add grasps by perturbing the -translation, but fixed orientation. Make sure these collide
lims = [0.01, 0.03]
grasps = np.array(
[
g @ tra.translation_matrix([0, 0, -z])
for z, g in zip(
np.random.uniform(lims[0], lims[1], size=num_grasps), neg_grasps_src
)
]
)
return grasps
def generate_negative_freespace(num_grasps: int) -> np.ndarray:
# Step 3 - Add grasps in the air to get rid of outliers in the model
lims_trans = [-0.4, 0.4]
lims_rot = [-np.pi, np.pi]
grasps = np.array(
[
tra.translation_matrix([trans[0], trans[1], trans[2]])
@ tra.euler_matrix(rot[0], rot[1], rot[2])
for trans, rot in zip(
np.random.uniform(lims_trans[0], lims_trans[1], size=(num_grasps, 3)),
np.random.uniform(lims_rot[0], lims_rot[1], size=(num_grasps, 3)),
)
]
)
return grasps
def load_discriminator_batch_with_stratified_sampling(
num_grasps_per_batch: int,
grasps_positive: np.ndarray,
ratio: dict,
scene_info: dict,
gripper_mesh: trimesh.base.Trimesh,
grasps_negative: np.ndarray = None,
positive_grasps_onpolicy: np.ndarray = None,
negative_grasps_onpolicy: np.ndarray = None,
):
N = num_grasps_per_batch
include_true_neg = grasps_negative is not None
include_true_pos = grasps_positive is not None
assert not (
not include_true_pos and not include_true_neg
), "Cannot have a situation where both positive and negative grasps are not in the batch"
num_pos_true_grasps = int(N * ratio[MAPPING_NAME2ID["pos_true"]])
num_neg_true_grasps = int(N * ratio[MAPPING_NAME2ID["neg_true"]])
num_neg_hncolliding = int(N * ratio[MAPPING_NAME2ID["neg_hncolliding"]])
num_neg_freespace = int(N * ratio[MAPPING_NAME2ID["neg_freespace"]])
num_neg_hnretract = int(N * ratio[MAPPING_NAME2ID["neg_hnretract"]])
num_pos_true_onpolicy_grasps = int(N * ratio[MAPPING_NAME2ID["pos_true_onpolicy"]])
num_neg_true_onpolicy_grasps = int(N * ratio[MAPPING_NAME2ID["neg_true_onpolicy"]])
if positive_grasps_onpolicy is None:
num_pos_true_grasps += num_pos_true_onpolicy_grasps
num_pos_true_onpolicy_grasps = 0
num_neg_true_grasps += num_neg_true_onpolicy_grasps
num_neg_true_onpolicy_grasps = 0
if include_true_pos:
if torch.is_tensor(grasps_positive):
grasps_positive = grasps_positive.cpu().numpy()
else:
num_neg_true_grasps += num_pos_true_grasps
num_pos_true_grasps = 0
num_pos_true_onpolicy_grasps = 0
if not include_true_neg:
assert include_true_pos
num_pos_true_grasps += num_neg_true_grasps
num_neg_true_grasps = 0
obj_scale = scene_info["scales"][0]
obj_asset_path = scene_info["assets"][0]
obj_pose = scene_info["poses"][0]
scene_mesh = trimesh.load(obj_asset_path)
scene_mesh.apply_scale(obj_scale)
scene_mesh.apply_transform(obj_pose)
grasps, grasp_ids = None, None
if num_pos_true_onpolicy_grasps > 0:
mask = np.random.randint(
0, len(positive_grasps_onpolicy), num_pos_true_onpolicy_grasps
)
grasps_pos_true_onpolicy = positive_grasps_onpolicy[mask]
grasps_id_pos_true_onpolicy = (
torch.ones([num_pos_true_onpolicy_grasps, 1])
* MAPPING_NAME2ID["pos_true_onpolicy"]
)
grasps_pos_true_onpolicy = torch.from_numpy(grasps_pos_true_onpolicy)
if grasps is None:
grasps = grasps_pos_true_onpolicy
grasp_ids = grasps_id_pos_true_onpolicy
if num_pos_true_grasps > 0:
mask = np.random.randint(0, len(grasps_positive), num_pos_true_grasps)
grasps_pos_true = grasps_positive[mask]
grasps_id_pos_true = (
torch.ones([num_pos_true_grasps, 1]) * MAPPING_NAME2ID["pos_true"]
)
grasps_pos_true = torch.from_numpy(grasps_pos_true)
if grasps is None:
grasps = grasps_pos_true
grasp_ids = grasps_id_pos_true
else:
grasps = torch.vstack([grasps, grasps_pos_true]).float()
grasp_ids = torch.vstack([grasp_ids, grasps_id_pos_true]).int()
if num_neg_true_onpolicy_grasps > 0:
mask = np.random.randint(
0, len(negative_grasps_onpolicy), num_neg_true_onpolicy_grasps
)
grasps_neg_true_onpolicy = negative_grasps_onpolicy[mask]
grasps_id_neg_true_onpolicy = (
torch.ones([num_neg_true_onpolicy_grasps, 1])
* MAPPING_NAME2ID["neg_true_onpolicy"]
)
grasps_neg_true_onpolicy = torch.from_numpy(grasps_neg_true_onpolicy)
grasps = torch.vstack([grasps, grasps_neg_true_onpolicy]).float()
grasp_ids = torch.vstack([grasp_ids, grasps_id_neg_true_onpolicy]).int()
if num_neg_freespace > 0:
grasps_neg_freespace = generate_negative_freespace(num_neg_freespace)
grasps_id_neg_freespace = (
torch.ones([num_neg_freespace, 1]) * MAPPING_NAME2ID["neg_freespace"]
)
grasps_neg_freespace = torch.from_numpy(grasps_neg_freespace)
grasps = torch.vstack([grasps, grasps_neg_freespace]).float()
grasp_ids = torch.vstack([grasp_ids, grasps_id_neg_freespace]).int()
if num_neg_hnretract > 0:
# Step 4 - Add the hard negatives retraction
grasps_neg_retract = generate_negative_retract(
num_neg_hnretract, grasps_positive
)
grasps_id_neg_retract = (
torch.ones([num_neg_hnretract, 1]) * MAPPING_NAME2ID["neg_hnretract"]
)
grasps_neg_retract = torch.from_numpy(grasps_neg_retract)
# NOTE - the labels are already correct from the get-go
grasps = torch.vstack([grasps, grasps_neg_retract]).float()
grasp_ids = torch.vstack([grasp_ids, grasps_id_neg_retract]).int()
if include_true_neg:
# Step 5 - Add the true negatives from the dataset
negative_grasps = grasps_negative
grasps_neg_true = negative_grasps[
np.random.randint(0, len(negative_grasps), num_neg_true_grasps)
]
grasps_neg_true = torch.from_numpy(grasps_neg_true)
grasps_id_neg_true = (
torch.ones([num_neg_true_grasps, 1]) * MAPPING_NAME2ID["neg_true"]
)
# NOTE - the labels are already correct from the get-go
grasps = torch.vstack([grasps, grasps_neg_true]).float()
grasp_ids = torch.vstack([grasp_ids, grasps_id_neg_true]).int()
# Step 2 - Add the hard negatives
grasps_neg_hncolliding = generate_negative_hardnegatives(
num_neg_hncolliding, grasps_positive, scene_mesh, gripper_mesh
)
if grasps_neg_hncolliding is not None:
num_neg_hncolliding_actual = len(grasps_neg_hncolliding)
grasps_neg_hncolliding = torch.from_numpy(grasps_neg_hncolliding)
grasps_id_neg_hncolliding = (
torch.ones([num_neg_hncolliding_actual, 1])
* MAPPING_NAME2ID["neg_hncolliding"]
)
# NOTE - the labels are already correct from the get-go
grasps = torch.vstack([grasps, grasps_neg_hncolliding]).float()
grasp_ids = torch.vstack([grasp_ids, grasps_id_neg_hncolliding]).int()
else:
num_neg_hncolliding_actual = 0
num_total_actual = (
num_pos_true_grasps
+ num_neg_true_grasps
+ num_neg_hncolliding_actual
+ num_neg_freespace
+ num_neg_hnretract
+ num_pos_true_onpolicy_grasps
+ num_neg_true_onpolicy_grasps
)
num_total_pos = num_pos_true_grasps + num_pos_true_onpolicy_grasps
labels = torch.vstack(
[
torch.ones([num_total_pos, 1]),
torch.zeros([num_total_actual - num_total_pos, 1]),
]
).float()
assert grasps.shape[0] == num_total_actual
assert labels.shape[0] == num_total_actual
assert grasp_ids.shape[0] == num_total_actual
outputs = {"grasps": grasps, "labels": labels, "grasp_ids": grasp_ids}
return outputs, scene_mesh
def collate_batch_keys(batch):
if len(batch) < 1:
return
batch = {key: [data[key] for data in batch] for key in batch[0]}
if "task" in batch:
task = batch.pop("task")
batch["task_is_pick"] = torch.stack([torch.tensor(t == "pick") for t in task])
batch["task_is_place"] = torch.stack([torch.tensor(t == "place") for t in task])
for key in batch:
if key in [
"inputs",
"points",
"seg",
"object_inputs",
"bottom_center",
"cam_pose",
"ee_pose",
"placement_masks",
"placement_region",
]:
batch[key] = torch.stack(batch[key])
if key in ["contact_dirs", "approach_dirs", "offsets"]:
batch[key] = torch.cat(batch[key])
return batch
def collate(batch):
initial_batch_size = len(batch)
batch = [data for data in batch if not data.get("invalid", False)]
final_batch_size = len(batch)
return collate_batch_keys(batch)
================================================
FILE: grasp_gen/dataset/dataset_utils.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""
Utility functions for data preprocessing.
"""
import glob
import io
import json
import logging
import os
import time
from typing import Dict, Tuple
import h5py
import imageio
import numpy as np
import scipy
import torch
import torch.nn.functional as F
import trimesh
import trimesh.transformations as tra
from tqdm import tqdm
from grasp_gen.utils.logging_config import get_logger
logger = get_logger(__name__)
from dataclasses import dataclass
from typing import Dict, List, Tuple, Union
from grasp_gen.dataset.eval_utils import (
is_empty,
load_h5_handle_empty_case,
write_info,
write_to_h5,
)
from grasp_gen.dataset.exceptions import DataLoaderError
from grasp_gen.dataset.webdataset_utils import GraspWebDatasetReader
from grasp_gen.utils.meshcat_utils import (
create_visualizer,
get_normals_from_mesh,
visualize_grasp,
visualize_mesh,
visualize_pointcloud,
)
from grasp_gen.robot import GripperInfo
try:
import cv2
except:
pass
class GraspJsonDatasetReader:
"""Class to efficiently read grasps data from JSON files in a regular directory structure."""
def __init__(self, grasp_root_dir: str):
"""
Initialize the reader with grasp root directory and load/create the UUID to JSON path mapping.
Args:
grasp_root_dir (str): Path to directory containing grasp JSON files
"""
self.grasp_root_dir = grasp_root_dir
self.map_uuid_to_path = {}
# Check if mapping file exists
mapping_file_path = os.path.join(grasp_root_dir, "map_uuid_to_path.json")
if os.path.exists(mapping_file_path):
logger.info(
f"Loading existing UUID to JSON path mapping from {mapping_file_path}"
)
with open(mapping_file_path, "r") as f:
self.map_uuid_to_path = json.load(f)
else:
logger.info(
f"Creating new UUID to JSON path mapping for Grasp Dataset at {grasp_root_dir}. One time effort."
)
self._create_uuid_to_path_mapping()
# Save the mapping
with open(mapping_file_path, "w") as f:
json.dump(self.map_uuid_to_path, f, indent=2)
logger.info(f"Saved UUID to JSON path mapping to {mapping_file_path}")
def _create_uuid_to_path_mapping(self):
"""Create mapping from object UUIDs to JSON file paths by scanning all JSON files."""
# Get all JSON files, excluding mapping files
json_files = glob.glob(os.path.join(self.grasp_root_dir, "*.json"))
json_files = [f for f in json_files if not f.endswith("map_uuid_to_path.json")]
logger.info(f"Scanning {len(json_files)} JSON files to create UUID mapping")
for json_file in tqdm(json_files, desc="Creating UUID mapping"):
try:
with open(json_file, "r") as f:
grasps_dict = json.load(f)
# Extract file path from the grasp data according to the schema
if "object" in grasps_dict and "file" in grasps_dict["object"]:
file_path = grasps_dict["object"]["file"]
# Use the file path as the object_id (UUID)
# Save only the basename of the JSON file
self.map_uuid_to_path[file_path] = os.path.basename(json_file)
else:
logger.warning(
f"JSON file {json_file} does not contain expected 'object.file' field"
)
except Exception as e:
logger.warning(f"Error processing JSON file {json_file}: {e}")
continue
logger.info(f"Created mapping for {len(self.map_uuid_to_path)} objects")
def read_grasps_by_uuid(self, object_id: str) -> Union[Dict, None]:
"""
Read grasps data for a specific object UUID.
Args:
object_id (str): UUID/path of the object to retrieve
Returns:
Union[Dict, None]: Dictionary containing the grasps data if found, None otherwise
"""
if object_id not in self.map_uuid_to_path:
logger.debug(f"Object ID {object_id} not found in UUID mapping")
return None
json_file_basename = self.map_uuid_to_path[object_id]
json_file_path = os.path.join(self.grasp_root_dir, json_file_basename)
try:
with open(json_file_path, "r") as f:
grasps_dict = json.load(f)
return grasps_dict
except Exception as e:
logger.error(f"Error loading grasps from {json_file_path}: {e}")
return None
def sample_points(xyz, num_points):
num_replica = num_points // xyz.shape[0]
num_remain = num_points % xyz.shape[0]
pt_idx = torch.randperm(xyz.shape[0])
pt_idx = torch.cat([pt_idx for _ in range(num_replica)] + [pt_idx[:num_remain]])
return pt_idx
def load_from_json(json_path) -> dict:
if os.path.exists(json_path):
return json.load(open(json_path, "r"))
else:
return {}
def convert_trimesh_to_dict(mesh: trimesh.base.Trimesh) -> Dict:
"""Convert a trimesh object to a dictionary of arrays."""
from trimesh.caching import TrackedArray
mesh_dict = {
"vertices": mesh.vertices,
"faces": mesh.faces,
}
# Add normals if they exist
if mesh.vertex_normals is not None:
mesh_dict["vertex_normals"] = mesh.vertex_normals
if mesh.face_normals is not None:
mesh_dict["face_normals"] = mesh.face_normals
# Add visual information if it exists
if hasattr(mesh.visual, "vertex_colors") and mesh.visual.vertex_colors is not None:
mesh_dict["vertex_colors"] = mesh.visual.vertex_colors
for key, value in mesh_dict.items():
if isinstance(value, TrackedArray):
mesh_dict[key] = np.array(value, copy=True)
return mesh_dict
def convert_dict_to_trimesh(mesh_data: Dict) -> trimesh.base.Trimesh:
"""Reconstruct a trimesh object from saved arrays"""
mesh = trimesh.Trimesh(vertices=mesh_data["vertices"], faces=mesh_data["faces"])
if "vertex_normals" in mesh_data:
mesh.vertex_normals = mesh_data["vertex_normals"]
if "face_normals" in mesh_data:
mesh.face_normals = mesh_data["face_normals"]
if "vertex_colors" in mesh_data:
mesh.visual.vertex_colors = mesh_data["vertex_colors"]
return mesh
@dataclass
class ObjectGraspDataset:
object_mesh: trimesh.base.Trimesh
positive_grasps: np.ndarray
contacts: np.ndarray
object_asset_path: str
object_scale: float = 1.0
negative_grasps: np.ndarray = None
positive_grasps_onpolicy: np.ndarray = None
negative_grasps_onpolicy: np.ndarray = None
def export_to_dict(self) -> Dict:
return {
"object_mesh": None,
"positive_grasps": self.positive_grasps,
"contacts": self.contacts,
"object_asset_path": self.object_asset_path,
"object_scale": self.object_scale,
"negative_grasps": self.negative_grasps,
"positive_grasps_onpolicy": self.positive_grasps_onpolicy,
"negative_grasps_onpolicy": self.negative_grasps_onpolicy,
}
@classmethod
def from_dict(cls, data: Dict):
return cls(
object_mesh=None,
positive_grasps=load_h5_handle_empty_case(data["positive_grasps"]),
contacts=load_h5_handle_empty_case(data["contacts"]),
object_asset_path=data["object_asset_path"][...].item().decode("utf-8"),
object_scale=data["object_scale"][...].item(),
negative_grasps=load_h5_handle_empty_case(data["negative_grasps"]),
positive_grasps_onpolicy=load_h5_handle_empty_case(
data["positive_grasps_onpolicy"]
),
negative_grasps_onpolicy=load_h5_handle_empty_case(
data["negative_grasps_onpolicy"]
),
)
def filter_grasps_by_point_cloud_visibility(
grasps: np.ndarray,
pointcloud: Union[np.ndarray, torch.Tensor],
transform_from_base_link_to_tool_tcp: np.ndarray,
radius: float = 0.03,
) -> Union[np.ndarray, None]:
"""
Grasps are assumed to be in the point cloud frame.
Removes grasps are in the self-occluded regions (not visible from current camera pose) of the point cloud
"""
num_grasps_initial = grasps.shape[0]
if num_grasps_initial == 0:
return None
grasps_tool_tip_locations = np.array(
[g @ transform_from_base_link_to_tool_tcp for g in grasps]
)
grasps_tool_tip_locations = torch.from_numpy(
grasps_tool_tip_locations
).float() # (N1, 3)
if type(pointcloud) == np.ndarray:
pointcloud = torch.from_numpy(pointcloud).float() # (N2, 3)
norm = 1
num_nneighbors = 5
dist_matrix = torch.cdist(
grasps_tool_tip_locations[:, :3, 3], pointcloud, p=norm
) # (N1, N2)
nn_dists, idxs = torch.topk(dist_matrix, num_nneighbors, dim=1, largest=False)
mask = nn_dists.mean(1) < radius
if mask.sum().item() > 0:
# print(f"Filtered out {num_grasps_initial - mask.sum().item()}/{num_grasps_initial} grasps based on point cloud visibility")
mask = mask.numpy()
else:
mask = None
return mask
class GraspGenDatasetCache(dict):
def __init__(self):
self._cache = {}
def __getitem__(self, key: str) -> Tuple[ObjectGraspDataset, dict]:
return self._cache[key]
def __setitem__(self, key: str, value: Tuple[ObjectGraspDataset, dict]):
self._cache[key] = value
def __len__(self):
return len(self._cache)
def __contains__(self, key: str) -> bool:
return key in self._cache
@classmethod
def load_from_h5_file(cls, path_to_h5_file: str):
t0 = time.time()
cache = cls()
h5_file = h5py.File(path_to_h5_file, "r")
for key_h5 in tqdm(
h5_file.keys(), desc=f"Loading cache from H5 file: {path_to_h5_file}"
):
h5_obj = h5_file[key_h5]
renderings = []
object_grasp_data = ObjectGraspDataset.from_dict(h5_obj["grasp_data"])
if key_h5.find("____") >= 0:
key = key_h5.replace("____", "/")
else:
key = key_h5
for i in h5_obj["renderings"].keys():
rendering_data_h5 = h5_obj["renderings"][i]
rendering_data_dict = {}
for bool_key in ["mesh_mode", "load_contact_batch", "invalid"]:
rendering_data_dict[bool_key] = (
rendering_data_h5[bool_key][...].astype(np.bool_).item()
)
rendering_data_dict["points"] = rendering_data_h5["points"][...]
rendering_data_dict["T_move_to_pc_mean"] = rendering_data_h5[
"T_move_to_pc_mean"
][...]
rendering_data_dict["positive_grasps"] = rendering_data_h5[
"positive_grasps"
][...]
renderings.append(rendering_data_dict)
cache[key] = (object_grasp_data, renderings)
logger.info(f"Loading cache from {path_to_h5_file} took {time.time() - t0}(s)")
return cache
def save_to_h5_file(self, path_to_h5_file: str):
t0 = time.time()
logger.info(f"Deleting old cache at {path_to_h5_file}")
if os.path.exists(path_to_h5_file):
os.system(f"rm {path_to_h5_file}") # For safety
output_file = h5py.File(path_to_h5_file, "a")
for key, (object_grasp_data, rendering_output) in tqdm(
self._cache.items(), desc=f"Saving cache to H5 file: {path_to_h5_file}"
):
if key.find("/") >= 0:
key_h5 = key.replace(
"/", "____"
) # This is a hack to avoid path problems in H5 files, if the key is already a path
else:
key_h5 = key
grp = output_file.create_group(key_h5)
object_grasp_data_dict = object_grasp_data.export_to_dict()
grp_key = grp.create_group("grasp_data")
write_info(grp_key, object_grasp_data_dict)
grp_key = grp.create_group("renderings")
for i, rendering_data in enumerate(rendering_output):
grp_key_i = grp_key.create_group(f"{i}")
write_info(grp_key_i, rendering_data)
output_file.close()
logger.info(f"Saving cache to {path_to_h5_file} took {time.time() - t0}(s)")
def compute_emd_data(
ogd: ObjectGraspDataset, num_grasps: int = 1000, num_samples: int = 5
):
"""
Compute EMD data for a given object grasp dataset
"""
from grasp_gen.utils.math_utils import compute_pose_emd
data = {}
if ogd.positive_grasps is not None:
emds = [
compute_pose_emd(
ogd.positive_grasps[
torch.randint(0, ogd.positive_grasps.shape[0], (num_grasps,))
],
ogd.positive_grasps[
torch.randint(0, ogd.positive_grasps.shape[0], (num_grasps,))
],
)
for _ in range(num_samples)
]
data["off-off-pos"] = np.mean(emds)
if ogd.negative_grasps is not None:
emds = [
compute_pose_emd(
ogd.negative_grasps[
torch.randint(0, ogd.negative_grasps.shape[0], (num_grasps,))
],
ogd.negative_grasps[
torch.randint(0, ogd.negative_grasps.shape[0], (num_grasps,))
],
)
for _ in range(num_samples)
]
data["off-off-neg"] = np.mean(emds)
try:
if ogd.positive_grasps_onpolicy is not None:
if ogd.positive_grasps_onpolicy.shape[0] > 0:
emds = [
compute_pose_emd(
ogd.positive_grasps_onpolicy[
torch.randint(
0, ogd.positive_grasps_onpolicy.shape[0], (num_grasps,)
)
],
ogd.positive_grasps_onpolicy[
torch.randint(
0, ogd.positive_grasps_onpolicy.shape[0], (num_grasps,)
)
],
)
for _ in range(num_samples)
]
data["on-on-pos"] = np.mean(emds)
if ogd.negative_grasps_onpolicy is not None:
if ogd.negative_grasps_onpolicy.shape[0] > 0:
emds = [
compute_pose_emd(
ogd.negative_grasps_onpolicy[
torch.randint(
0, ogd.negative_grasps_onpolicy.shape[0], (num_grasps,)
)
],
ogd.negative_grasps_onpolicy[
torch.randint(
0, ogd.negative_grasps_onpolicy.shape[0], (num_grasps,)
)
],
)
for _ in range(num_samples)
]
data["on-on-neg"] = np.mean(emds)
if ogd.positive_grasps is not None and ogd.positive_grasps_onpolicy is not None:
if (
ogd.positive_grasps.shape[0] > 0
and ogd.positive_grasps_onpolicy.shape[0] > 0
):
emds = [
compute_pose_emd(
ogd.positive_grasps[
torch.randint(
0, ogd.positive_grasps.shape[0], (num_grasps,)
)
],
ogd.positive_grasps_onpolicy[
torch.randint(
0, ogd.positive_grasps_onpolicy.shape[0], (num_grasps,)
)
],
)
for _ in range(num_samples)
]
data["off-on-pos"] = np.mean(emds)
if ogd.negative_grasps is not None and ogd.negative_grasps_onpolicy is not None:
if (
ogd.negative_grasps.shape[0] > 0
and ogd.negative_grasps_onpolicy.shape[0] > 0
):
emds = [
compute_pose_emd(
ogd.negative_grasps[
torch.randint(
0, ogd.negative_grasps.shape[0], (num_grasps,)
)
],
ogd.negative_grasps_onpolicy[
torch.randint(
0, ogd.negative_grasps_onpolicy.shape[0], (num_grasps,)
)
],
)
for _ in range(num_samples)
]
data["off-on-neg"] = np.mean(emds)
except:
from IPython import embed
embed()
return data
def visualize_object_grasp_dataset(
ogd: ObjectGraspDataset,
gripper_mesh: trimesh.base.Trimesh,
gripper_name: str,
max_grasps_to_visualize: int = 20,
):
grasps = ogd.positive_grasps
contacts = ogd.contacts
negative_grasps = ogd.negative_grasps
positive_grasps_onpolicy = ogd.positive_grasps_onpolicy
negative_grasps_onpolicy = ogd.negative_grasps_onpolicy
vis = create_visualizer()
vis.delete()
# visualize_mesh(
# vis, "ogd/object_mesh", object_mesh, color=[192, 192, 192], transform=np.eye(4)
# )
for i, g in enumerate(grasps):
# visualize_mesh(vis, f"ogd/grasps_mesh/{i:03d}", gripper_mesh, color=[0, 250, 40], transform=g.astype(np.float32))
visualize_grasp(
vis,
f"ogd/pos_true/{i:03d}",
g.astype(np.float32),
[0, 255, 0],
gripper_name=gripper_name,
linewidth=0.2,
)
# Too many grasps will overwhelm meshcat
if i == max_grasps_to_visualize:
break
for grasp_grp, grasp_name in zip(
[negative_grasps, positive_grasps_onpolicy, negative_grasps_onpolicy],
["neg_true", "pos_onpolicy", "neg_onpolicy"],
):
if grasp_grp is not None:
for i, g in enumerate(grasp_grp):
# visualize_mesh(vis, f"ogd/grasps_mesh/{i:03d}", gripper_mesh, color=[0, 250, 40], transform=g.astype(np.float32))
color = [0, 255, 0] if grasp_name.find("pos") >= 0 else [255, 0, 0]
visualize_grasp(
vis,
f"ogd/{grasp_name}/{i:03d}",
g.astype(np.float32),
color,
gripper_name=gripper_name,
linewidth=0.2,
)
# Too many grasps will overwhelm meshcat
if i == max_grasps_to_visualize:
break
def load_object_grasp_data(
key,
object_root_dir,
grasp_root_dir,
dataset_version="v2",
load_discriminator_dataset=False,
gripper_info=None,
onpolicy_dataset_dir=None,
onpolicy_dataset_h5_path=None,
onpolicy_json_path=None,
onpolicy_data_found=False,
grasp_dataset_reader: Union[GraspWebDatasetReader, GraspJsonDatasetReader] = None,
) -> Tuple[DataLoaderError, Union[ObjectGraspDataset, None]]:
if grasp_dataset_reader is not None:
grasp_root_dir = None
if onpolicy_dataset_dir is not None:
if not onpolicy_data_found:
onpolicy_json_path = None
onpolicy_dataset_h5_path = None
onpolicy_dataset_dir = None
error_code, object_grasp_data = DataLoaderError.SUCCESS, None
if dataset_version == "v1":
error_code, object_grasp_data = load_object_grasp_acronym(
key,
object_root_dir,
grasp_root_dir,
load_discriminator_dataset=load_discriminator_dataset,
onpolicy_dataset_dir=onpolicy_dataset_dir,
onpolicy_dataset_h5_path=onpolicy_dataset_h5_path,
onpolicy_json_path=onpolicy_json_path,
)
elif dataset_version == "v2":
error_code, object_grasp_data = load_object_grasp_datapoint_objaverse(
key,
object_root_dir,
grasp_root_dir,
load_discriminator_dataset=load_discriminator_dataset,
onpolicy_dataset_dir=onpolicy_dataset_dir,
onpolicy_dataset_h5_path=onpolicy_dataset_h5_path,
onpolicy_json_path=onpolicy_json_path,
grasp_dataset_reader=grasp_dataset_reader,
)
else:
raise NotImplementedError(
f"Version {dataset_version} of object dataset {object_root_dir} not implemented"
)
if object_grasp_data is not None:
offset_transform = gripper_info.offset_transform
object_grasp_data.positive_grasps = np.array(
[g @ offset_transform for g in object_grasp_data.positive_grasps]
)
if object_grasp_data.negative_grasps is not None:
object_grasp_data.negative_grasps = np.array(
[g @ offset_transform for g in object_grasp_data.negative_grasps]
)
if object_grasp_data.negative_grasps_onpolicy is not None:
object_grasp_data.negative_grasps_onpolicy = np.array(
[
g @ offset_transform
for g in object_grasp_data.negative_grasps_onpolicy
]
)
if object_grasp_data.positive_grasps_onpolicy is not None:
object_grasp_data.positive_grasps_onpolicy = np.array(
[
g @ offset_transform
for g in object_grasp_data.positive_grasps_onpolicy
]
)
if object_grasp_data.contacts is not None:
pass
return error_code, object_grasp_data
def get_json_file_given_object_id(json_files: List[str], object_id_key: str) -> Dict:
"""This func is needed as there 1-2 discrepancies in objects."""
object_id_dict = {}
# Brute force
for json_path in json_files:
# json_path_object_id = json_path.split('/')[-2].split('_')[-1]
# json_path_object_id = load_from_json(json_path)['object']['file'].split('_')[-1].split('.')[0][1:]
json_path_object_id = load_from_json(json_path)["object"]["file"]
if os.path.basename(json_path_object_id) == os.path.basename(object_id_key):
object_id_dict[json_path_object_id] = {
"json_path": json_path,
"object_id_key": object_id_key,
}
break
return object_id_dict
def load_onpolicy_dataset(
key: str,
onpolicy_dataset_dir: str,
onpolicy_dataset_h5_path: str,
onpolicy_json_path: str,
dataset_version: str = "v4",
) -> Tuple[np.ndarray, np.ndarray]:
logger.info(f"Loading onpolicy dataset for {key}: {onpolicy_json_path}")
h5 = h5py.File(onpolicy_dataset_h5_path, "r")
h5_obj = h5["objects"][key]
json_path = onpolicy_json_path
pred_grasps = h5_obj["pred_grasps"][...]
gt_grasps = h5_obj["gt_grasps"][
...
] # TODO: This is not real ground truth. Replace this...
scores = h5_obj["confidence"][...]
collision = h5_obj["collision"][...]
mask_not_colliding = np.logical_not(collision)
num_grasps_attempted_inference = mask_not_colliding.sum()
data = json.load(open(json_path, "rb"))
try:
num_grasps_attempted_igg = len(data["grasps"]["transforms"])
except:
logger.error(f"ONPOLICY: Error in opening file {key}: {onpolicy_json_path}")
return None, None
# pred_grasps2 = np.array(data['grasps']['transforms'])
mask_eval_success = np.array(data["grasps"]["object_in_gripper"])
num_successful_attempts = np.sum(mask_eval_success)
success_result = np.zeros(len(scores))
try:
# print(f"num_grasps_attempted_inference: {num_grasps_attempted_inference}, num_grasps_attempted_igg: {num_grasps_attempted_igg}")
assert (
num_grasps_attempted_inference == num_grasps_attempted_igg
) # Sanity check if the object id in h5 files (inference) is the same as the index in the evaluated json files (IGG)
except:
logger.error(
f"ONPOLICY: Number of attempted grasps in h5 file (predicted) and Issac Sim does not match... {num_grasps_attempted_inference} vs. {num_grasps_attempted_igg} for object {key}."
)
return None, None
success_mask = np.where(mask_not_colliding)[0][np.where(mask_eval_success)[0]]
success_result[success_mask] = 1.0
positive_grasps_onpolicy = pred_grasps[success_result.astype(np.bool_)]
negative_grasps_onpolicy = pred_grasps[~success_result.astype(np.bool_)]
logger.info(
f"ONPOLICY: num pos {len(positive_grasps_onpolicy)}, num neg: {len(negative_grasps_onpolicy)}"
)
return positive_grasps_onpolicy, negative_grasps_onpolicy
def load_object_grasp_acronym(
key: str,
object_root_dir: str,
grasp_root_dir: str,
load_discriminator_dataset: bool = False,
onpolicy_dataset_dir: str = None,
onpolicy_dataset_h5_path: str = None,
onpolicy_json_path: str = None,
min_pos_grasps_gen: int = 5,
min_neg_grasps_dis: int = 5,
min_pos_grasps_dis: int = 5,
) -> ObjectGraspDataset:
"""
For a given datapoint key, returns the following:
- Object mesh, loaded and scaled
- Grasps
- Contact points [optional]
"""
positive_grasps_onpolicy = None
negative_grasps_onpolicy = None
if onpolicy_dataset_h5_path is not None:
positive_grasps_onpolicy, negative_grasps_onpolicy = load_onpolicy_dataset(
key, onpolicy_dataset_dir, onpolicy_dataset_h5_path, onpolicy_json_path
)
object_name = key.split("/")[-1]
cat, model, scale = object_name.split("_")
scale = float(scale)
asset_path = os.path.join(object_root_dir, f"meshes/{cat}/{model}.obj")
# Load grasps
# t0 = time.time()
success_og = np.load(f"{grasp_root_dir}/grasp_eval/{object_name}.npy")
grasp_data = np.load(f"{grasp_root_dir}/contacts/{object_name}.npz")
# print(f"Loading graspdata took {time.time() - t0}s") # This is minimal 0.0006771087646484375s
success = success_og[grasp_data["grasp_ids"]] & grasp_data["successful"].astype(
"bool"
)
negative_grasps = None
try:
grasps = grasp_data["grasp_transform"]
positive_grasps = grasps[success]
if load_discriminator_dataset:
not_success = np.logical_not(
success_og[grasp_data["grasp_ids"]]
) & np.logical_not(grasp_data["successful"].astype("bool"))
negative_grasps = grasps[not_success]
if positive_grasps.shape[0] < min_pos_grasps_dis:
return (
DataLoaderError.INSUFFICIENT_GRASPS_FOR_DISCRIMINATOR_DATASET,
None,
)
if negative_grasps is None:
return (
DataLoaderError.INSUFFICIENT_GRASPS_FOR_DISCRIMINATOR_DATASET,
None,
)
if negative_grasps.shape[0] < min_neg_grasps_dis:
return (
DataLoaderError.INSUFFICIENT_GRASPS_FOR_DISCRIMINATOR_DATASET,
None,
)
except:
logger.error("ERROR: grasp_transform.npy is corrupted for ", object_name)
return DataLoaderError.GRASPS_FILE_LOAD_ERROR, None
grasp_ids = grasp_data["grasp_ids"][success]
try:
contacts = grasp_data["contact_points"][success]
except:
logger.error(f"Contact not loaded for key {key}")
return DataLoaderError.GRASPS_FILE_LOAD_ERROR, None
offsets = np.linalg.norm(contacts[:, 1] - contacts[:, 0], axis=1)
valid = offsets > 1e-3
positive_grasps = positive_grasps[valid]
grasp_ids = grasp_ids[valid]
contacts = contacts[valid]
if not load_discriminator_dataset:
# Only do this for the generator
if positive_grasps.shape[0] < min_pos_grasps_gen:
# print(f"ERROR: Skipping object {object_name} since there are too few grasps ({positive_grasps.shape[0]})")
# print(f"object_name:{object_name} num pos:{len(positive_grasps)}, neg:{len(negative_grasps)} grasps")
return DataLoaderError.INSUFFICIENT_GRASPS_FOR_GENERATOR_DATASET, None
# t0 = time.time()
obj_mesh = trimesh.load(asset_path)
obj_mesh.apply_scale(scale)
return DataLoaderError.SUCCESS, ObjectGraspDataset(
obj_mesh,
positive_grasps,
contacts,
asset_path,
scale,
negative_grasps=negative_grasps,
positive_grasps_onpolicy=positive_grasps_onpolicy,
negative_grasps_onpolicy=negative_grasps_onpolicy,
)
def load_grasp_data(
object_id: str,
grasp_dataset_reader: Union[GraspWebDatasetReader, GraspJsonDatasetReader] = None,
) -> Tuple[DataLoaderError, Union[Dict, None]]:
"""
Loads grasp data from either a webdataset, json dataset reader, or a regular folder.
"""
assert grasp_dataset_reader is not None, "Must provide grasp_dataset_reader"
grasps_dict = grasp_dataset_reader.read_grasps_by_uuid(object_id)
if grasps_dict is None:
logger.info(f"No grasp found for object {object_id} using dataset reader")
return DataLoaderError.GRASPS_FILE_NOT_FOUND, None
return DataLoaderError.SUCCESS, grasps_dict
def load_object_grasp_datapoint_objaverse(
object_id: str,
object_root_dir: str = None,
grasp_root_dir: str = None,
load_discriminator_dataset: bool = False,
onpolicy_dataset_dir: str = None,
onpolicy_dataset_h5_path: str = None,
onpolicy_json_path: str = None,
grasp_dataset_reader: Union[GraspWebDatasetReader, GraspJsonDatasetReader] = None,
min_pos_grasps_gen: int = 5,
min_neg_grasps_dis: int = 5,
min_pos_grasps_dis: int = 5,
) -> Tuple[DataLoaderError, Union[ObjectGraspDataset, None]]:
"""
Args:
object_root_dir: Root directory of the object dataset
grasp_root_dir: Root directory of the grasp dataset
object_id: Key of the object to load
load_discriminator_dataset: Whether to load the discriminator dataset
onpolicy_dataset_dir: Directory of the onpolicy dataset
onpolicy_dataset_h5_path: Path to the onpolicy dataset h5 file
onpolicy_json_path: Path to the onpolicy dataset json file
grasp_dataset_reader: Dataset reader to use if the grasp dataset is stored in a webdataset or json format
min_pos_grasps_gen: Minimum number of positive grasps for the generator dataset
min_neg_grasps_dis: Minimum number of negative grasps for the discriminator dataset
min_pos_grasps_dis: Minimum number of positive grasps for the discriminator dataset
"""
positive_grasps_onpolicy = None
negative_grasps_onpolicy = None
if onpolicy_dataset_h5_path is not None:
positive_grasps_onpolicy, negative_grasps_onpolicy = load_onpolicy_dataset(
object_id,
onpolicy_dataset_dir,
onpolicy_dataset_h5_path,
onpolicy_json_path,
)
error_code, grasps_dict = load_grasp_data(object_id, grasp_dataset_reader)
if error_code != DataLoaderError.SUCCESS:
return error_code, grasps_dict
object_file = os.path.join(object_root_dir, object_id)
if not os.path.exists(object_file):
# Load object paths
uuid_object_paths_file = os.path.join(
object_root_dir, "map_uuid_to_path_simplified.json"
)
if not os.path.exists(uuid_object_paths_file):
uuid_object_paths_file = os.path.join(
object_root_dir, "map_uuid_to_path.json"
)
if not os.path.exists(uuid_object_paths_file):
raise FileNotFoundError(
f"Could not find mapping file at {uuid_object_paths_file}"
)
try:
uuid_to_path = json.load(open(uuid_object_paths_file))
if object_id not in uuid_to_path:
return DataLoaderError.UUID_NOT_FOUND_IN_MAPPING, None
object_file = uuid_to_path[object_id]
object_file = os.path.join(object_root_dir, object_file)
if not os.path.exists(object_file):
logger.error(f"Object mesh not found, at {object_file}")
return DataLoaderError.OBJECT_MESH_NOT_FOUND, None
except Exception as e:
return DataLoaderError.UUID_MAPPING_LOAD_ERROR, None
object_scale = grasps_dict["object"]["scale"]
grasps = grasps_dict["grasps"]
grasp_poses = np.array(grasps["transforms"])
grasp_mask = np.array(grasps["object_in_gripper"])
positive_grasps = grasp_poses[grasp_mask]
negative_grasps = None
contacts = None
not_success = np.logical_not(grasp_mask)
negative_grasps = grasp_poses[not_success]
if not load_discriminator_dataset:
# Only do this for the generator
if positive_grasps.shape[0] < min_pos_grasps_gen:
logger.debug(
f"Skipping object {object_id} since there are too few grasps: num pos:{len(positive_grasps)}, neg:{len(negative_grasps) if negative_grasps is not None else 0} grasps"
)
return DataLoaderError.INSUFFICIENT_GRASPS_FOR_GENERATOR_DATASET, None
if "contact_locations" in grasps:
contacts = np.array(grasps["contact_locations"])
contacts = contacts[grasp_mask]
mask_grasp_with_contacts = np.isnan(contacts)
mask_grasp_with_contacts = np.any(mask_grasp_with_contacts, axis=1)
if contacts.shape[-1] == 3:
mask_grasp_with_contacts = np.any(mask_grasp_with_contacts, axis=1)
mask_grasp_with_contacts = np.logical_not(mask_grasp_with_contacts)
if mask_grasp_with_contacts.sum() == 0:
logger.debug(
f"No grasp with contacts found for object {object_id}. Number of positive grasps: {positive_grasps.shape[0]}"
)
return DataLoaderError.GRASPS_HAVE_INVALID_CONTACT_POINTS, None
positive_grasps = positive_grasps[mask_grasp_with_contacts]
contacts = contacts[mask_grasp_with_contacts]
try:
object_mesh = trimesh.load(object_file)
if type(object_mesh) == trimesh.Scene:
object_mesh = object_mesh.dump(concatenate=True)
object_mesh.apply_scale(object_scale)
except:
logger.debug(f"Unable to load object mesh at {object_file}")
return DataLoaderError.OBJECT_MESH_LOAD_ERROR, None
# HACK - Only works for single cup suction gripper
if contacts is None:
cp = np.array([[0.0, 0, 0]])
if len(positive_grasps) > 0:
contacts = np.vstack([tra.transform_points(cp, g) for g in positive_grasps])
return DataLoaderError.SUCCESS, ObjectGraspDataset(
object_mesh,
positive_grasps,
contacts,
object_file,
object_scale,
negative_grasps=negative_grasps,
positive_grasps_onpolicy=positive_grasps_onpolicy,
negative_grasps_onpolicy=negative_grasps_onpolicy,
)
def dump_object_list(objects, json_file_path):
json_file = open(json_file_path, "w")
json.dump(objects, json_file)
json_file.close()
def purge_redundant_checkpoints(list_of_dirs: List[str], execute: bool = True):
for ws_dir in list_of_dirs:
for subdir in os.listdir(ws_dir):
exp_dir = os.path.join(ws_dir, subdir)
ckpt_files = sorted(glob.glob(os.path.join(exp_dir, "*.pth")))
ckpt_files = [
ckpt_file for ckpt_file in ckpt_files if ckpt_file.find("last") < 0
]
ckpt_files = [
ckpt_file
for ckpt_file in ckpt_files
if len(os.path.basename(ckpt_file).split("_")[-1].split(".")[0]) > 3
]
whitelisted_files = ckpt_files[-3:] + [
os.path.join(exp_dir, "last.pth"),
]
blacklisted_files = [
ckpt_file
for ckpt_file in sorted(glob.glob(os.path.join(exp_dir, "*.pth")))
if ckpt_file not in whitelisted_files
]
for blacklisted_file in blacklisted_files:
cmd = f"rm -rf {blacklisted_file}"
logger.info(cmd)
if execute:
os.system(cmd)
def get_rotation_augmentation(stratified_sampling: bool = True) -> np.ndarray:
"""
Applies stratified sampling for rotation augmentation.
Somestimes the object/point cloud are axis aligned. Random sampling over the rotation space has a very low chance of
hitting these axis-aligned orientations. This function applies stratified sampling to ensure some rotations are sampled accordingly
"""
# Stratified Sampling for rotation augmentation
p = np.random.random()
if p < 0.25: # 25%
base_rotation = np.eye(4)
elif p < 0.50: # 25%
base_rotation = tra.euler_matrix(np.pi / 2, 0, 0)
elif p < 0.75: # 25%
base_rotation = tra.euler_matrix(0, np.pi / 2, 0)
else: # 25%
base_rotation = tra.euler_matrix(0, 0, np.pi / 2)
rotation = np.random.uniform(-np.pi, np.pi, 3)
p = np.random.random()
if not stratified_sampling:
rotation = tra.euler_matrix(rotation[0], rotation[1], rotation[2])
elif p < 0.25: # 25%
rotation = np.eye(4)
elif p < 0.30: # 5%
rotation = tra.euler_matrix(rotation[0], 0, 0)
elif p < 0.35: # 5%
rotation = tra.euler_matrix(0, rotation[1], 0)
elif p < 0.40: # 5%
rotation = tra.euler_matrix(0, 0, rotation[2])
else: # 60%
rotation = tra.euler_matrix(rotation[0], rotation[1], rotation[2])
rotation = rotation @ base_rotation
return rotation
================================================
FILE: grasp_gen/dataset/eval_utils.py
================================================
import logging
import sys
from logging.handlers import QueueHandler
from pathlib import Path
from typing import List, Tuple
import h5py
import numpy as np
import trimesh
import trimesh.transformations as tra
import yaml
from h5py._hl.group import Group
from trimesh.collision import CollisionManager
def log_worker(log_queue, log_file=None):
logger = logging.getLogger()
logger.setLevel(logging.INFO)
if log_file is None:
handler = logging.StreamHandler(sys.stdout)
else:
handler = logging.FileHandler(log_file)
logger.addHandler(handler)
while True:
record = log_queue.get()
if record is None:
break
logger.handle(record)
def get_logger(name, log_queue):
log = logging.getLogger(name)
log.setLevel(logging.INFO)
handler = QueueHandler(log_queue)
formatter = logging.Formatter(
"[%(asctime)s][%(name)s][%(levelname)s] - %(message)s"
)
handler.setFormatter(formatter)
log.addHandler(handler)
return log
def get_timestamp():
import datetime
now = datetime.datetime.now()
year = "{:02d}".format(now.year)
month = "{:02d}".format(now.month)
day = "{:02d}".format(now.day)
hour = "{:02d}".format(now.hour)
minute = "{:02d}".format(now.minute)
day_month_year = "{}-{}-{}-{}-{}".format(year, month, day, hour, minute)
return day_month_year
def check_collision(scene_mesh, object_mesh, transforms):
"""Args:
scene_mesh: trimesh.Trimesh
object_mesh: trimesh.Trimesh
transforms: list of 4x4 np.array
"""
scene_manager = CollisionManager()
scene_manager.add_object("object", scene_mesh)
obj_manager = CollisionManager()
obj_manager.add_object("object", object_mesh)
collision = []
for tr in transforms:
obj_manager.set_transform("object", tr)
collision.append(scene_manager.in_collision_other(obj_manager))
return np.array(collision).astype("bool")
def write_to_h5(key, src, dst):
if isinstance(src, dict):
group = dst.create_group(key)
for k, v in src.items():
write_to_h5(k, v, group)
else:
dst.create_dataset(key, data=src)
def is_empty(data):
return data == h5py.Empty("f")
def load_h5_handle_empty_case(data):
if is_empty(data):
return None
else:
return data[...]
def write_info(group: Group, info: dict):
"""Writes dictionary info to h5 file, given h5 object handle.
Args:
group: hdf5 group
info: data to write into hdf5
"""
for key, data in info.items():
if isinstance(data, dict):
subgroup = group.create_group(key)
write_info(subgroup, data)
else:
dtype = None
if isinstance(data, np.ndarray):
if str(data.dtype) == "float64":
dtype = "float32"
if str(data.dtype) == "int64":
dtype = "int16"
if str(data.dtype) == "uint8":
dtype = "uint8"
if isinstance(data, float):
dtype = "float32"
if isinstance(data, int):
dtype = "uint8"
if dtype is None:
if data is None:
group.create_dataset(key, data=h5py.Empty("f"))
else:
group.create_dataset(key, data=data)
else:
group.create_dataset(key, data=data, dtype=dtype)
def pose_as_dict(p):
trans = tra.translation_from_matrix(p).tolist()
rpy = tra.euler_from_matrix(p)
return {
"p": {"x": trans[0], "y": trans[1], "z": trans[2]},
"r": {"roll": rpy[0], "pitch": rpy[1], "yaw": rpy[2]},
}
def create_scene(robot_pose, object_asset):
return {
"actors": [
{
"asset": "robot",
"pose": pose_as_dict(robot_pose),
},
{
"asset": object_asset,
"pose": pose_as_dict(np.eye(4)),
},
]
}
def create_object_asset(file_name, scale, actor_name, asset_root, resolution):
return {
"actor_name": actor_name,
"collision_group": 0,
"collision_filter": 2,
"asset_root": asset_root,
"file": file_name,
"scale": scale,
"textured": False,
"options": {
"fix_base_link": False,
"flip_visual_attachments": False,
"thickness": 0.003,
"armature": 0.002,
"vhacd_enabled": True,
"vhacd_params": {
"resolution": resolution,
"concavity": 0.00001,
"plane_downsampling": 16,
"convex_hull_downsampling": 16,
"alpha": 0.15,
"beta": 0.15,
"pca": 0,
"max_num_vertices_per_ch": 128,
"min_volume_per_ch": 0.0001,
},
},
"rigid_shape_properties": {
"friction": 2.0,
},
"rigid_body_properties": {
"mass": 0.2,
},
}
def create_robot_asset():
return {
"actor_name": "robot",
"asset_root": "assets/",
"file": "urdf/franka_description/robots/franka_panda_gripper_spherical_dof_acronym.urdf",
"controllable": True,
"collision_group": 0,
"collision_filter": 1,
"default_config": [0, 0, 0, 0, 0, 0, 0.04, 0.04],
"options": {
"fix_base_link": True,
"flip_visual_attachments": True,
"armature": 0.01,
},
"dof_properties": {
"driveMode": [1, 1, 1, 1, 1, 1, 2, 2],
"stiffness": [1e3, 1e3, 1e3, 1e3, 1e3, 1e3, 0, 0],
"damping": [50, 50, 50, 50, 50, 50, 800, 800],
"effort": 1400.0,
},
}
def save_to_isaac_grasp_format(
grasps: np.ndarray, confidences: np.ndarray, output_path: str
):
"""Saves grasps and confidences to output file, in a Isaac grasp format
The Isaac Grasp format is detailed here:
https://docs.isaacsim.omniverse.nvidia.com/latest/robot_setup/grasp_editor.html#what-is-an-isaac-grasp-file
grasps (np.array): Grasp poses, 4x4 homogenous matrices
confidences (np.array): Confidences, predicted by the network, for each grasp above
output_path (str): Path to the yaml file to save
"""
data = {"format": "isaac_grasp", "format_version": 1.0, "grasps": {}}
confidences = confidences.tolist()
assert len(grasps) == len(confidences)
for i, (g, c) in enumerate(zip(grasps, confidences)):
xyz = g[:3, 3].tolist()
q = tra.quaternion_from_matrix(g[:3, :3])
qw = float(q[0])
qxyz = q[1:].tolist()
data["grasps"][f"grasp_{i}"] = {
"confidence": c,
"position": xyz,
"orientation": {
"w": qw,
"xyz": qxyz,
},
}
# don't write out if there are no grasps or if there is no output_path
if len(grasps) > 0:
if output_path is not None:
with open(output_path, "w") as f:
yaml.dump(data, f, default_flow_style=False, sort_keys=False)
return data
def load_from_isaac_grasp_format(file_path: str) -> Tuple[np.ndarray, np.ndarray]:
"""Loads grasps and confidences from output file, which is written in the Isaac grasp format
The Isaac Grasp format is detailed here:
https://docs.isaacsim.omniverse.nvidia.com/latest/robot_setup/grasp_editor.html#what-is-an-isaac-grasp-file
output_path (str): Path to the yaml file
returns:
grasps (np.array): Grasp poses, 4x4 homogenous matrices
confidences (np.array): Confidences, predicted by the network, for each grasp above
"""
with open(file_path, "r") as f:
data = yaml.safe_load(f)
grasps = data["grasps"]
confidences = [g["confidence"] for _, g in grasps.items()]
positions = [tra.translation_matrix(g["position"]) for _, g in grasps.items()]
rotations = [
tra.quaternion_matrix([g["orientation"]["w"]] + g["orientation"]["xyz"])
for _, g in grasps.items()
]
grasps = [p @ r for p, r in zip(positions, rotations)]
assert len(grasps) == len(confidences)
grasps = np.array(grasps)
confidences = np.array(confidences)
return grasps, confidences
from yourdfpy.urdf import URDF
def load_urdf_scene(urdf_path: str) -> URDF:
"""Loads urdf scene given path."""
import yourdfpy
scene = yourdfpy.URDF.load(
urdf_path,
build_scene_graph=True,
load_meshes=True,
build_collision_scene_graph=True,
load_collision_meshes=True,
force_mesh=False,
force_collision_mesh=False,
)
return scene
================================================
FILE: grasp_gen/dataset/exceptions.py
================================================
from dataclasses import dataclass
from enum import Enum
@dataclass
class ErrorInfo:
code: int
description: str
class DataLoaderError(Enum):
# Success
SUCCESS = ErrorInfo(0, "Datapoint loaded successfully")
# Data loading errors (100-199)
GRASPS_FILE_NOT_FOUND = ErrorInfo(100, "No grasp file located for object")
GRASPS_FILE_LOAD_ERROR = ErrorInfo(101, "Unable to load grasp file successfully")
INSUFFICIENT_GRASPS_FOR_GENERATOR_DATASET = ErrorInfo(
102, "Not enough grasps provided (minimum 5 required)"
)
INSUFFICIENT_GRASPS_FOR_DISCRIMINATOR_DATASET = ErrorInfo(
103, "Not enough grasps provided (minimum 5 required)"
)
GRASPS_HAVE_INVALID_CONTACT_POINTS = ErrorInfo(
104, "Grasps have invalid contact points"
)
OBJECT_FILE_MAPPING_ERROR = ErrorInfo(
105, "Object file was not mapped properly to grasp dataset"
)
OBJECT_FILE_NOT_FOUND = ErrorInfo(106, "Object file was not found")
OBJECT_MESH_LOAD_ERROR = ErrorInfo(107, "Unable to load mesh")
AMBIGUOUS_LOAD_ERROR = ErrorInfo(108, "Not sure why this error occurred.")
GRASP_POINT_CLOUD_CORRESPONDENCE_ERROR = ErrorInfo(
109, "Unable to find valids grasps for visible point cloud"
)
OBJECT_MESH_NOT_FOUND = ErrorInfo(110, "Object mesh not found")
UUID_MAPPING_NOT_FOUND = ErrorInfo(111, "UUID mapping not found")
UUID_NOT_FOUND_IN_MAPPING = ErrorInfo(112, "UUID not found in mapping")
UUID_MAPPING_LOAD_ERROR = ErrorInfo(113, "Error loading UUID mapping")
# Rendering errors (200-299)
RENDERING_SUCCESS = ErrorInfo(200, "Rendering successful")
RENDERING_ERROR = ErrorInfo(201, "Error during rendering")
RENDERING_ERROR_POINT_CLOUD_TOO_SMALL = ErrorInfo(202, "Point cloud is too small")
RENDERING_ERROR_POINT_CLOUD_VERY_FEW_POINTS = ErrorInfo(
203, "Point cloud is too small"
)
RENDERING_NO_GRASPS_IN_VISIBLE_POINT_CLOUD = ErrorInfo(
110, "No grasps are in the visible point cloud"
)
# Rendering errors (300-399)
@property
def code(self) -> int:
return self.value.code
@property
def description(self) -> str:
return self.value.description
def is_success(self) -> bool:
return self == ErrorCode.SUCCESS
def is_error(self) -> bool:
return not self.is_success()
================================================
FILE: grasp_gen/dataset/image_utils.py
================================================
# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""Visualization utils."""
# Standard Library
import io
import math
import os
import sys
import scipy
import imageio
from PIL import Image
from torchvision import transforms
from functools import lru_cache
from typing import Dict, List, Tuple, Union
# Third Party
try:
import cv2
except ImportError:
logger.warning("Unable to import cv2")
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ImageDraw
from torchvision import transforms
from grasp_gen.utils.logging_config import get_logger
logger = get_logger(__name__)
def gen_lut() -> np.ndarray:
"""Generate a label colormap compatible with opencv lookup table.
Based on Rick Szelski algorithm in Computer Vision: Algorithms and Applications.
Creates a color lookup table for converting label images to RGB.
Returns:
np.ndarray: OpenCV compatible color lookup table of shape (256, 3)
"""
tobits = lambda x, o: np.array(list(np.binary_repr(x, 24)[o::-3]), np.uint8)
arr = np.arange(256)
r = np.concatenate([np.packbits(tobits(x, -3)) for x in arr])
g = np.concatenate([np.packbits(tobits(x, -2)) for x in arr])
b = np.concatenate([np.packbits(tobits(x, -1)) for x in arr])
return np.concatenate([[[b]], [[g]], [[r]]]).T
def labels2rgb(labels: np.ndarray, lut: np.ndarray = None) -> np.ndarray:
"""Convert a label image to an rgb image using a lookup table.
Applies color mapping to label images for visualization purposes.
Uses the provided lookup table or generates a default one.
Args:
labels: Label image of type np.uint8 2D array
lut: Lookup table of shape (256, 3) and type np.uint8
Returns:
np.ndarray: Colorized label image
"""
if lut is None:
lut = gen_lut()
return cv2.LUT(cv2.merge((labels, labels, labels)), lut)
def depth2rgb(depth: np.ndarray, colormap: int = None) -> np.ndarray:
"""Colorize a depth image using a colormap.
Converts depth values to RGB colors for better visualization.
Normalizes depth values to [0, 255] range before applying colormap.
Args:
depth: Depth image array
colormap: Colormap to use, defaults to cv2.COLORMAP_JET
Returns:
np.ndarray: Colorized depth image
"""
if colormap is None:
colormap = cv2.COLORMAP_JET
min_depth, max_depth = depth.min(), depth.max()
if max_depth == min_depth:
rgb = np.zeros_like(depth)
else:
rgb = (depth - min_depth) / (max_depth - min_depth) * 255.0
return cv2.applyColorMap(rgb.astype(np.uint8), colormap)
def draw_circles(
im: np.ndarray, circles: np.ndarray, color: Tuple[int, int, int] = (0, 0, 255)
) -> np.ndarray:
"""Draw circles on an image at specified locations.
Draws filled circles with given radius and color at each point.
Modifies the input image in-place and returns the result.
Args:
im: Input image to draw on
circles: Nx2 numpy array of circle centers
color: RGB color tuple for circles
Returns:
np.ndarray: Image with circles drawn
"""
circles = circles.astype(np.int32)
for n in range(circles.shape[0]):
circle = tuple(circles[n, :2])
im = cv2.circle(im, circle, 2, color, 2)
return im
def draw_lines(
im: np.ndarray,
points: np.ndarray,
lines: np.ndarray,
colors: List[List[int]] = None,
thickness: int = 2,
) -> np.ndarray:
"""Draw lines on an image connecting specified points.
Draws lines between pairs of points with specified colors and thickness.
Each line connects two points from the points array.
Args:
im: Input image to draw on
points: Array of point coordinates
lines: Nx2 array of point indices to connect
colors: List of RGB colors for each line
thickness: Line thickness in pixels
Returns:
np.ndarray: Image with lines drawn
"""
if colors is None:
colors = [[0, 1, 0] for i in range(len(lines))]
points = np.array(points, dtype=np.int32)
for line, color in zip(lines, colors):
im = cv2.line(
im, tuple(points[line[0]][:2]), tuple(points[line[1]][:2]), color, thickness
)
return im
def draw_rectangles(
im: np.ndarray,
boxes: np.ndarray,
color: Tuple[int, int, int] = (0, 255, 0),
thickness: int = 2,
) -> np.ndarray:
"""Draw rectangles on an image using bounding box coordinates.
Draws rectangles for each bounding box in the format [xmin, ymin, xmax, ymax].
Uses OpenCV rectangle drawing function.
Args:
im: Input image to draw on
boxes: Nx4 array of bounding boxes [xmin, ymin, xmax, ymax]
color: RGB color tuple for rectangles
thickness: Rectangle border thickness
Returns:
np.ndarray: Image with rectangles drawn
"""
for box in boxes:
im = cv2.rectangle(im, tuple(box[:2]), tuple(box[2:]), color, thickness)
return im
def blend_images(images: List[np.ndarray], ratios: List[float] = None) -> np.ndarray:
"""Blend a list of images with specified ratios.
Combines multiple images using weighted averaging. All images must have
the same dimensions and be in the range [0, 255].
Args:
images: List of images to blend
ratios: List of blending weights, defaults to equal weights
Returns:
np.ndarray: Blended image
"""
out = None
if ratios is None:
ratios = [1.0 / len(images)] * len(images)
for im, ratio in zip(images, ratios):
if out is None:
out = ratio * im.astype(np.float32)
else:
out += ratio * im.astype(np.float32)
return out.astype(np.uint8)
def image_grid(
image_list: List[np.ndarray], rows: int = -1, margin: int = 10
) -> np.ndarray:
"""Generate a grid layout of images.
Arranges multiple images in a grid format with specified margins.
All images must have the same shape and number of channels.
Args:
image_list: List of images to arrange in grid
rows: Number of rows, -1 for single row
margin: Pixel margin between images
Returns:
np.ndarray: Combined grid image
Raises:
ValueError: If images have different shapes
"""
n = len(image_list)
if rows == -1:
cols = n
rows = 1
else:
cols = math.ceil(n * 1.0 / rows)
if any(i.shape != image_list[0].shape for i in image_list[1:]):
raise ValueError("Not all images have the same shape.")
img_h, img_w, img_c = image_list[0].shape
imgmatrix = np.zeros(
(img_h * cols + margin * (cols - 1), img_w * rows + margin * (rows - 1), img_c),
np.uint8,
)
imgmatrix.fill(255)
for idx, im in enumerate(image_list):
x_i = idx % rows
y_i = idx // rows
x = x_i * (img_w + margin)
y = y_i * (img_h + margin)
imgmatrix[y : y + img_h, x : x + img_w, :] = im
return imgmatrix
def imshow(im: np.ndarray, name: str = "", is_blocking: bool = True) -> None:
"""Display an image using OpenCV window.
Shows image in a named window. Can be blocking or non-blocking.
Blocking mode waits for key press to close window.
Args:
im: Image to display
name: Window name
is_blocking: Whether to wait for key press
"""
cv2.imshow(name, im)
if is_blocking:
logger.info("press any key to exit...")
cv2.waitKey(0)
else:
cv2.waitKey(1)
def save_image(img: Union[np.ndarray, Image.Image], image_fname: str) -> None:
"""Save an image to file.
Converts numpy array to PIL Image if needed and saves to specified path.
Supports common image formats based on file extension.
Args:
img: Image as numpy array or PIL Image
image_fname: Output file path
"""
if type(img) == np.ndarray:
img = Image.fromarray(img)
img.save(image_fname)
def draw_bbox(
im: np.ndarray,
boxes: np.ndarray = None,
centers: np.ndarray = None,
color: Tuple[int, int, int] = (0, 255, 0),
thickness: int = 2,
) -> Image.Image:
"""Draw bounding boxes and center points on an image.
Uses PIL drawing functions to draw rectangles and circles.
Can draw both bounding boxes and center point markers.
Args:
im: Input image as numpy array
boxes: Nx4 array of bounding boxes [xmin, ymin, xmax, ymax]
centers: Nx2 array of center points
color: RGB color for drawing
thickness: Line thickness
Returns:
Image.Image: PIL Image with drawings
"""
img = Image.fromarray(im)
draw = ImageDraw.Draw(img)
for box in boxes:
draw.rectangle(box, outline=color)
if centers is not None:
for center in centers:
xmid = center[0]
ymid = center[1]
draw.ellipse(
(xmid - 10, ymid - 10, xmid + 10, ymid + 10), fill="red", outline="red"
)
return img
def convert_label_img_to_seg(seg: np.ndarray) -> np.ndarray:
"""Convert label image to segmentation visualization.
Assigns random colors to each unique label value for visualization.
Creates a color-coded segmentation map.
Args:
seg: Label image with integer labels
Returns:
np.ndarray: RGB segmentation visualization
"""
map_label_to_color = np.random.uniform(size=[200, 3]) * 255.0
h, w = seg.shape
img_rgb = np.zeros((h, w, 3), dtype=np.uint8)
for seg_id in list(set(seg.flatten().tolist())):
img_rgb[seg == seg_id, :] = map_label_to_color[seg_id]
return img_rgb
def decompress_png(image_comp: np.ndarray, return_unit8: bool) -> np.ndarray:
"""Decompress PNG image from compressed byte array.
Reads PNG data from numpy byte array and optionally normalizes to [0,1].
Uses imageio for PNG decoding.
Args:
image_comp: Compressed PNG data as uint8 array
return_unit8: Whether to return uint8 or float32 [0,1]
Returns:
np.ndarray: Decompressed image
"""
assert image_comp.dtype == "uint8"
with io.BytesIO(image_comp) as f:
image = imageio.imread(f, format="png")
if not return_unit8:
image = image.astype(np.float32)
image = image / 255.0
return image
def convert_img_float_uint8(image: np.ndarray) -> Image.Image:
"""Convert float32 image in range [0,1] to uint8 PIL Image.
Scales float values from [0,1] to [0,255] and converts to PIL Image.
Assumes input is float32 with values in valid range.
Args:
image: Float32 image with values in [0,1]
Returns:
Image.Image: Converted PIL Image
Raises:
AssertionError: If input is not float32 or values out of range
"""
assert isinstance(image, np.ndarray)
assert not image.dtype == "uint8"
assert image.dtype == "float32"
assert np.all(np.logical_and(image >= 0.0, image <= 1.0))
return Image.fromarray((image * 255).round().astype(np.uint8))
def compress_img(image: Union[np.ndarray, Image.Image], is_unit8: bool) -> np.ndarray:
"""Compress image to PNG format as byte array.
Converts image to PNG format and returns as compressed byte array.
Handles both uint8 and float32 input formats.
Args:
image: Input image as numpy array or PIL Image
is_unit8: Whether input is uint8 or float32 [0,1]
Returns:
np.ndarray: Compressed PNG data as uint8 array
"""
_image = np.array(image)
if is_unit8:
assert _image.dtype == "uint8"
assert np.all(np.logical_and(_image >= 0, _image <= 255))
else:
assert np.all(np.logical_and(_image >= 0.0, _image <= 1.0)), _image
assert _image.dtype == "float32"
image = convert_img_float_uint8(_image)
with io.BytesIO() as b:
imageio.imwrite(b, image, format="png")
b.seek(0)
image_comp = np.frombuffer(b.read(), dtype="uint8")
return image_comp
def jitter_gaussian(xyz: torch.Tensor, std: float, clip: float) -> torch.Tensor:
"""Add Gaussian jitter to xyz coordinates.
Adds random Gaussian noise to point cloud coordinates.
Clips the noise to specified range to prevent excessive distortion.
Args:
xyz: Point cloud coordinates
std: Standard deviation of Gaussian noise
clip: Maximum absolute value for noise clipping
Returns:
torch.Tensor: Jittered coordinates
"""
return xyz + torch.clip(torch.randn_like(xyz) * std, -clip, clip)
def add_noise_to_xyz(
xyz_img: np.ndarray, depth_img: np.ndarray, noise_params: Dict
) -> np.ndarray:
"""Add Gaussian Process noise to ordered point cloud.
Applies approximate Gaussian Process noise using bicubic interpolation.
Adapted from DexNet 2.0 codebase for depth sensor simulation.
Args:
xyz_img: HxWx3 ordered point cloud
depth_img: HxW depth image for masking
noise_params: Dictionary with 'gp_rescale_factor_range' and 'gaussian_scale_range'
Returns:
np.ndarray: Noisy point cloud
"""
assert isinstance(xyz_img, np.ndarray)
assert isinstance(depth_img, np.ndarray)
H, W, C = xyz_img.shape
gp_rescale_factor = np.random.uniform(
noise_params["gp_rescale_factor_range"][0],
noise_params["gp_rescale_factor_range"][1],
)
gaussian_scale = np.random.uniform(
noise_params["gaussian_scale_range"][0], noise_params["gaussian_scale_range"][1]
)
small_H, small_W = (np.array([H, W]) / gp_rescale_factor).astype(int)
additive_noise = np.random.normal(
loc=0.0, scale=gaussian_scale, size=(small_H, small_W, C)
)
additive_noise = cv2.resize(additive_noise, (W, H), interpolation=cv2.INTER_CUBIC)
xyz_img[depth_img != 0, :] += additive_noise[depth_img != 0, :]
return xyz_img
def add_gaussian_noise_to_depth(
depth_img: np.ndarray, noise_params: Dict
) -> np.ndarray:
"""Add Gaussian noise to depth image.
Applies zero-mean Gaussian noise with random standard deviation.
Used for simulating depth sensor noise.
Args:
depth_img: HxW depth image
noise_params: Dictionary with 'gaussian_std_range' parameter
Returns:
np.ndarray: Noisy depth image
"""
assert isinstance(depth_img, np.ndarray)
std = np.random.uniform(
noise_params["gaussian_std_range"][0], noise_params["gaussian_std_range"][1]
)
rng = np.random.default_rng()
noise = rng.standard_normal(size=depth_img.shape, dtype=np.float32) * std
depth_img += noise
return depth_img
def dropout_random_ellipses(
depth_img: Union[np.ndarray, torch.Tensor], noise_params: Dict
) -> np.ndarray:
"""Randomly drop ellipses in depth image for robustness.
Creates random elliptical regions of zero depth to simulate occlusions.
Uses PyTorch for efficient computation and gamma distribution for ellipse sizes.
Args:
depth_img: HxW depth image
noise_params: Dictionary with ellipse parameters
Returns:
np.ndarray: Depth image with elliptical dropouts
"""
depth_img = (
torch.tensor(depth_img, dtype=torch.float32)
if not isinstance(depth_img, torch.Tensor)
else depth_img.clone()
)
num_ellipses_to_dropout = torch.poisson(
torch.tensor(noise_params["ellipse_dropout_mean"])
)
num_ellipses_to_dropout = int(num_ellipses_to_dropout.item())
valid_pixel_indices = torch.nonzero(
(depth_img != 0) & (depth_img != float("inf")) & (depth_img != float("-inf"))
)
if valid_pixel_indices.shape[0] == 0:
return depth_img.numpy()
dropout_centers_indices = torch.randint(
0, valid_pixel_indices.shape[0], (num_ellipses_to_dropout,)
)
dropout_centers = valid_pixel_indices[dropout_centers_indices, :]
x_radii = torch.round(
torch.distributions.Gamma(
noise_params["ellipse_gamma_shape"], noise_params["ellipse_gamma_scale"]
).sample((num_ellipses_to_dropout,))
).int()
y_radii = torch.round(
torch.distributions.Gamma(
noise_params["ellipse_gamma_shape"], noise_params["ellipse_gamma_scale"]
).sample((num_ellipses_to_dropout,))
).int()
angles = torch.randint(0, 360, (num_ellipses_to_dropout,))
mask = torch.zeros_like(depth_img)
for i in range(num_ellipses_to_dropout):
center = dropout_centers[i].tolist()
x_radius = x_radii[i].item()
y_radius = y_radii[i].item()
angle = angles[i].item()
yy, xx = torch.meshgrid(
torch.arange(depth_img.shape[0]),
torch.arange(depth_img.shape[1]),
indexing="ij",
)
cos_a = torch.cos(torch.deg2rad(torch.tensor(angle, dtype=torch.float32)))
sin_a = torch.sin(torch.deg2rad(torch.tensor(angle, dtype=torch.float32)))
x_shifted = xx - center[1]
y_shifted = yy - center[0]
x_rot = cos_a * x_shifted + sin_a * y_shifted
y_rot = -sin_a * x_shifted + cos_a * y_shifted
ellipse_mask = ((x_rot / x_radius) ** 2 + (y_rot / y_radius) ** 2) <= 1
mask[ellipse_mask] = 1
depth_img[mask == 1] = 0
return depth_img.numpy()
@lru_cache(maxsize=2)
def get_xp_yp(rows: int, cols: int) -> Tuple[np.ndarray, np.ndarray]:
"""Generate meshgrid coordinates for image processing.
Creates coordinate grids for bilinear interpolation and warping.
Cached for efficiency when called repeatedly with same dimensions.
Args:
rows: Number of rows
cols: Number of columns
Returns:
Tuple[np.ndarray, np.ndarray]: X and Y coordinate grids
"""
xp, yp = np.meshgrid(
np.arange(cols, dtype=np.float32), np.arange(rows, dtype=np.float32)
)
return xp, yp
def add_gaussian_shifts(
depth: Union[np.ndarray, torch.Tensor], std: float = 1 / 2.0
) -> np.ndarray:
"""Add Gaussian shifts to depth image using bilinear interpolation.
Applies random pixel shifts to simulate camera motion or calibration errors.
Uses both bilinear and nearest-neighbor interpolation for robust handling.
Args:
depth: HxW depth image
std: Standard deviation of Gaussian shifts
Returns:
np.ndarray: Shifted depth image
"""
depth = (
torch.tensor(depth, dtype=torch.float32)
if not isinstance(depth, torch.Tensor)
else depth.clone()
)
rows, cols = depth.shape
rng = torch.randn((rows, cols, 2), dtype=torch.float32) * std
xp, yp = get_xp_yp(rows, cols)
xp = torch.tensor(xp, dtype=torch.float32)
yp = torch.tensor(yp, dtype=torch.float32)
xp_interp = torch.clamp(xp + rng[:, :, 0], 0.0, cols - 1)
yp_interp = torch.clamp(yp + rng[:, :, 1], 0.0, rows - 1)
depth_interp = F.grid_sample(
depth.unsqueeze(0).unsqueeze(0),
torch.stack((xp_interp, yp_interp), dim=-1).unsqueeze(0),
mode="bilinear",
align_corners=True,
).squeeze()
robot_depth_interp = F.grid_sample(
depth.unsqueeze(0).unsqueeze(0),
torch.stack((xp_interp, yp_interp), dim=-1).unsqueeze(0),
mode="nearest",
align_corners=True,
).squeeze()
depth[depth == float("inf")] = depth.min()
combine_depth_interp = torch.where(
(depth_interp > depth.max() - 0.6) | (depth_interp < depth.min()),
robot_depth_interp,
depth_interp,
)
combine_depth_interp[combine_depth_interp != combine_depth_interp] = float("inf")
return combine_depth_interp.numpy()
def mask_object_edge(
depth: Union[np.ndarray, torch.Tensor], thresh: float
) -> Tuple[np.ndarray, np.ndarray]:
"""Mask object edges in depth image using Sobel edge detection.
Detects edges using Sobel filters and masks them to remove boundary artifacts.
Useful for cleaning depth images before processing.
Args:
depth: HxW depth image
thresh: Threshold for edge detection
Returns:
Tuple[np.ndarray, np.ndarray]: Masked depth image and edge mask
"""
depth = (
torch.tensor(depth, dtype=torch.float32)
if not isinstance(depth, torch.Tensor)
else depth.clone()
)
img = depth.clone()
img[img == float("inf")] = 0
img = (img / img.max() * 256).to(torch.uint8)
sobel_x = (
torch.tensor([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=torch.float32)
.unsqueeze(0)
.unsqueeze(0)
)
sobel_y = (
torch.tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32)
.unsqueeze(0)
.unsqueeze(0)
)
img = img.unsqueeze(0).unsqueeze(0).float()
sobelx = F.conv2d(img, sobel_x, padding=1).squeeze()
sobely = F.conv2d(img, sobel_y, padding=1).squeeze()
index_x = (sobelx > -thresh) & (sobelx < thresh)
index_y = (sobely > -thresh) & (sobely < thresh)
mask_index = index_x & index_y
depth[~mask_index] = float("inf")
return depth.numpy(), mask_index.numpy()
def add_kinect_noise_to_depth(depth: np.ndarray, noise_params: Dict) -> np.ndarray:
"""Add Kinect-style noise to depth image.
Combines edge masking and Gaussian shifts to simulate Kinect sensor noise.
Typical noise pattern includes edge artifacts and depth-dependent noise.
Args:
depth: HxW depth image
noise_params: Dictionary with 'std_range' and 'thresh_range' parameters
Returns:
np.ndarray: Depth image with Kinect-style noise
"""
assert isinstance(depth, np.ndarray)
std = np.random.uniform(noise_params["std_range"][0], noise_params["std_range"][1])
thresh = np.random.uniform(
noise_params["thresh_range"][0], noise_params["thresh_range"][1]
)
depth, _ = mask_object_edge(depth, thresh)
noisy_depth = add_gaussian_shifts(depth, std)
return noisy_depth
class NormalizeInverse(transforms.Normalize):
"""Inverse normalization transform for denormalizing images.
Computes inverse of normalization parameters to reverse the normalization.
Useful for converting normalized tensors back to original image range.
"""
def __init__(
self,
mean: Union[List[float], torch.Tensor],
std: Union[List[float], torch.Tensor],
):
"""Initialize inverse normalization transform.
Args:
mean: Mean values used in original normalization
std: Standard deviation values used in original normalization
"""
mean = torch.as_tensor(mean)
std = torch.as_tensor(std)
std_inv = 1 / (std + 1e-7)
mean_inv = -mean * std_inv
super().__init__(mean=mean_inv, std=std_inv)
def __call__(self, tensor: torch.Tensor) -> torch.Tensor:
"""Apply inverse normalization to tensor.
Args:
tensor: Normalized tensor to denormalize
Returns:
torch.Tensor: Denormalized tensor
"""
return super().__call__(tensor.clone())
normalize_rgb = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)
denormalize_rgb = transforms.Compose(
[NormalizeInverse(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]
)
================================================
FILE: grasp_gen/dataset/renderer.py
================================================
# Copyright (c) 2024, 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 math
import os
import sys
import time
from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import trimesh
import trimesh.transformations as tra
from scipy.ndimage import convolve
from grasp_gen.dataset.dataset_utils import ObjectGraspDataset
from grasp_gen.dataset.exceptions import DataLoaderError
from grasp_gen.utils.meshcat_utils import (
create_visualizer,
make_frame,
visualize_grasp,
visualize_mesh,
visualize_pointcloud,
)
from grasp_gen.utils.logging_config import get_logger
logger = get_logger(__name__)
# Try to import pyrender - it requires OpenGL/display which may not be available in headless environments
try:
import pyrender
RENDERING_AVAILABLE = True
except ImportError as e:
logger.warning(
f"Pyrender import failed: {e}. "
"Rendering features are disabled. This is expected in headless environments. "
"For inference-only usage, ensure mesh_mode=True is used."
)
pyrender = None
RENDERING_AVAILABLE = False
except Exception as e:
# Catch other errors like missing GLU library, display initialization, etc.
logger.warning(
f"Pyrender initialization failed: {e}. "
"Rendering features are disabled. This is expected in headless/no-display environments. "
"For inference-only usage, ensure mesh_mode=True is used."
)
pyrender = None
RENDERING_AVAILABLE = False
NOISE_PARAMS = {
"kin_noi": {"std_range": [0.25, 0.75], "thresh_range": [100, 1000]},
"gau_noi": {"gaussian_std_range": [0.001, 0.002]},
"dp_ell": {
"ellipse_dropout_mean": 10,
"ellipse_gamma_shape": 5.0,
"ellipse_gamma_scale": 1.0,
},
"noi_xyz": {
"gaussian_scale_range": [0.0, 0.005],
"gp_rescale_factor_range": [6, 20],
},
"wrist_cali_err": {"low": -0.005, "high": 0.005},
"rgb_color_jitter": {
"brightness": 0.1,
"contrast": 0.1,
"saturation": 0.1,
"hue": 0.1,
},
}
def fov_and_size_to_intrinsics(fov, img_size, device="cpu"):
img_h, img_w = img_size
fx = img_w / (2 * math.tan(math.radians(fov) / 2))
fy = img_h / (2 * math.tan(math.radians(fov) / 2))
# intrinsics = torch.tensor(
# [[fx, 0, img_h / 2], [0, fy, img_w / 2], [0, 0, 1]],
# dtype=torch.float,
# device=device,
# )
intrinsics = np.array([[fx, 0, img_h / 2], [0, fy, img_w / 2], [0, 0, 1]])
return intrinsics
def depth2points(
depth: np.array,
fx: int,
fy: int,
cx: int,
cy: int,
xmap: np.array = None,
ymap: np.array = None,
rgb: np.array = None,
seg: np.array = None,
mask: np.arange = None,
) -> Dict:
"""Compute point cloud from a depth image.
Args:
depth (np.array): 2D depth image.
fx (int): Focal length x.
fy (int): Focal length y.
cx (int): Camera principal point x.
cy (int): Camera principal point y.
xmap (np.array, optional): precomputed meshgrid map X. Defaults to None.
ymap (np.array, optional): precomputed meshgrid map Y. Defaults to None.
rgb (np.array, optional): RGB imgae for colored point cloud. Defaults to None.
seg (np.array, optional): Segmentation image corresponding to the rgb image Defaults to None.
mask (np.arange, optional): Binary mask. 1 to keep. Defaults to None.
Returns:
Dict: Output is a dictionary with fields `xyz`, `rgb`, and `index`,
where `xyz` and `rgb` are Nx3 numpy arrays and `index` is a Nx1 array
to indicate which point is valid.
"""
"""convert depth image to point cloud"""
if rgb is not None:
assert rgb.shape[0] == depth.shape[0] and rgb.shape[1] == depth.shape[1]
if xmap is not None:
assert xmap.shape[0] == depth.shape[0] and xmap.shape[1] == depth.shape[1]
if ymap is not None:
assert ymap.shape[0] == depth.shape[0] and ymap.shape[1] == depth.shape[1]
im_height, im_width = depth.shape[0], depth.shape[1]
if xmap is None or ymap is None:
ww = np.linspace(0, im_width - 1, im_width)
hh = np.linspace(0, im_height - 1, im_height)
xmap, ymap = np.meshgrid(ww, hh)
pt2 = depth
pt0 = (xmap - cx) * pt2 / fx
pt1 = (ymap - cy) * pt2 / fy
mask_depth = np.ma.getmaskarray(np.ma.masked_greater(pt2, 0))
if mask is None:
mask = mask_depth
else:
mask_semantic = np.ma.getmaskarray(np.ma.masked_equal(mask, 1))
mask = mask_depth * mask_semantic
index = mask.flatten().nonzero()[0]
pt2_valid = pt2.flatten()[:, np.newaxis].astype(np.float32)
pt0_valid = pt0.flatten()[:, np.newaxis].astype(np.float32)
pt1_valid = pt1.flatten()[:, np.newaxis].astype(np.float32)
pc_xyz = np.concatenate((pt0_valid, pt1_valid, pt2_valid), axis=1)
if rgb is not None:
r = rgb[:, :, 0].flatten()[:, np.newaxis]
g = rgb[:, :, 1].flatten()[:, np.newaxis]
b = rgb[:, :, 2].flatten()[:, np.newaxis]
pc_rgb = np.concatenate((r, g, b), axis=1)
else:
pc_rgb = None
if seg is not None:
pc_seg = seg.flatten()[:, np.newaxis]
else:
pc_seg = None
return {"xyz": pc_xyz, "rgb": pc_rgb, "seg": pc_seg, "index": index}
def compute_camera_pose(center, distance, azimuth, elevation):
cam_tf = tra.euler_matrix(np.pi / 2, 0, 0).dot(tra.euler_matrix(0, np.pi / 2, 0))
pose = tra.euler_matrix(0, -elevation, azimuth) @ tra.translation_matrix(
[distance, 0, 0]
)
pose = tra.translation_matrix(center) @ pose
pose_opengl = pose @ cam_tf
pose_opencv = pose_opengl.copy()
pose_opencv[:, 1:3] *= -1.0
# pose_opengl = pose_opencv @ T_opencv_to_opengl_format
return pose_opencv, pose_opengl
def sample_camera_pose(
lookat_point=[0, 0, 0],
azimuth_lims=[-0.2, 0.2],
elevation_lims=[0.6, 1.0],
radius_lims=[1.5, 2.0],
num_cameras=1,
):
camera_poses = []
assert len(lookat_point) == 3
assert len(azimuth_lims) == 2
assert len(elevation_lims) == 2
assert len(radius_lims) == 2
assert num_cameras > 0
camera_poses_opengl = []
camera_poses_opencv = []
for _ in range(num_cameras):
target = lookat_point
radius = np.random.uniform(*radius_lims)
elevation = np.random.uniform(*elevation_lims)
azimuth = np.random.uniform(*azimuth_lims)
pose_opencv, pose_opengl = compute_camera_pose(
target, radius, azimuth, elevation
)
camera_poses_opengl.append(pose_opengl)
camera_poses_opencv.append(pose_opencv)
return camera_poses_opengl, camera_poses_opencv
class Renderer:
def __init__(self):
if not RENDERING_AVAILABLE:
raise RuntimeError(
"Rendering is not available in this environment. "
"Pyrender requires OpenGL/GLU libraries and a display or EGL/OSMesa backend. "
"For inference-only usage, use mesh_mode=True to avoid rendering."
)
self._scene = pyrender.Scene()
self._node_dict = {}
self._camera_intr = None
self._camera_node = None
self._light_node = None
self._renderer = None
def create_camera(self, intrinsics, image_size, znear=0.04, zfar=100.0):
fx = intrinsics["fx"]
fy = intrinsics["fy"]
cx = intrinsics["cx"]
cy = intrinsics["cy"]
cam = pyrender.IntrinsicsCamera(fx, fy, cx, cy, znear, zfar)
self._camera_intr = intrinsics
self._camera_node = pyrender.Node(camera=cam, matrix=np.eye(4))
self._scene.add_node(self._camera_node)
light = pyrender.PointLight(color=[1.0, 1.0, 1.0], intensity=4.0)
self._light_node = pyrender.Node(light=light, matrix=np.eye(4))
self._scene.add_node(self._light_node)
self._renderer = pyrender.OffscreenRenderer(
viewport_width=image_size[0],
viewport_height=image_size[1],
# point_size=5.0,
)
def set_camera_pose(self, cam_pose):
if self._camera_node is None:
raise ValueError("call create camera first!")
self._scene.set_pose(self._camera_node, cam_pose)
self._scene.set_pose(self._light_node, cam_pose)
def get_camera_pose(self):
if self._camera_node is None:
raise ValueError("call create camera first!")
return self._camera_node.matrix
def render_rgbd(self, depth_only=True):
if depth_only:
depth = self._renderer.render(self._scene, pyrender.RenderFlags.DEPTH_ONLY)
color = None
else:
color, depth = self._renderer.render(self._scene)
return color, depth
def render_segmentation(self, full_depth=None):
if full_depth is None:
_, full_depth = self.render_rgbd(depth_only=True)
self.hide_objects()
output = np.zeros(full_depth.shape, dtype=np.uint8)
for i, obj_name in enumerate(self._node_dict):
self._node_dict[obj_name].mesh.is_visible = True
_, depth = self.render_rgbd(depth_only=True)
mask = np.logical_and(
(np.abs(depth.data - full_depth) < 1e-6),
np.abs(full_depth) > 0,
)
if np.any(output[mask] != 0):
raise ValueError("wrong label")
output[mask] = i + 1
self._node_dict[obj_name].mesh.is_visible = False
self.show_objects()
return output, ["BACKGROUND"] + list(self._node_dict.keys())
def render_all(self):
rgb, depth = self.render_rgbd()
seg, labels = self.render_segmentation(depth)
return rgb, depth, seg, labels
def add_object(self, name, mesh, pose=None):
if pose is None:
pose = np.eye(4, dtype=np.float32)
node = pyrender.Node(
name=name,
mesh=pyrender.Mesh.from_trimesh(mesh, smooth=False),
matrix=pose,
)
self._node_dict[name] = node
self._scene.add_node(node)
def set_object_pose(self, name, pose):
self._scene.set_pose(self._node_dict[name], pose)
def has_object(self, name):
return name in self._node_dict
def remove_object(self, name):
self._scene.remove_node(self._node_dict[name])
del self._node_dict[name]
def show_objects(self, names=None):
for name, node in self._node_dict.items():
if names is None or name in names:
node.mesh.is_visible = True
def toggle_wireframe(self, names=None):
for name, node in self._node_dict.items():
if names is None or name in names:
node.mesh.primitives[0].material.wireframe ^= True
def hide_objects(self, names=None):
for name, node in self._node_dict.items():
if names is None or name in names:
node.mesh.is_visible = False
def reset(self):
for name in self._node_dict:
self._scene.remove_node(self._node_dict[name])
self._node_dict = {}
def add_gaussian_noise_to_depth(
depth_img: np.ndarray, noise_params: Dict
) -> np.ndarray:
"""Add Gaussian noise to depth image.
Applies zero-mean Gaussian noise with random standard deviation.
Used for simulating depth sensor noise.
Args:
depth_img: HxW depth image
noise_params: Dictionary with 'gaussian_std_range' parameter
Returns:
np.ndarray: Noisy depth image
"""
assert isinstance(depth_img, np.ndarray)
std = np.random.uniform(
noise_params["gaussian_std_range"][0], noise_params["gaussian_std_range"][1]
)
rng = np.random.default_rng()
noise = rng.standard_normal(size=depth_img.shape, dtype=np.float32) * std
depth_img += noise
return depth_img
def add_depth_noise(depth: np.array) -> np.array:
depth = add_gaussian_noise_to_depth(depth, NOISE_PARAMS["gau_noi"])
depth_mask_after_noise = np.isinf(depth)
depth[depth_mask_after_noise] = 0
return depth
def add_noise_to_random_object_region(
segmentation_image, noise_level=0.05, region_ratio=0.5
):
"""
Adds noise near the edges of the object (class 1) in a randomly chosen quadrant.
Parameters:
- segmentation_image (numpy array, shape (H, W)): The input segmentation image.
- noise_level (float): Fraction of object edge pixels (class 1) to perturb.
- region_ratio (float): Fraction of the object bounding box height/width for selecting the region.
Returns:
- noisy_image (numpy array, shape (H, W)): The noisy segmentation image.
"""
import random
noisy_image = segmentation_image.copy()
H, W = segmentation_image.shape
# Define edge detection kernel
kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
# Detect edges (but keep only class 1 edges)
edge_map = np.abs(
convolve((segmentation_image == 1).astype(np.float32), kernel, mode="constant")
)
object_edge_pixels = np.argwhere(edge_map > 0) # Edge pixels of class 1
if len(object_edge_pixels) == 0:
return noisy_image # No object edges found, return original image
# Find bounding box of the object (class 1)
obj_pixels = np.argwhere(segmentation_image == 1)
min_y, min_x = obj_pixels.min(axis=0)
max_y, max_x = obj_pixels.max(axis=0)
# Define the region sizes based on the object bounding box
region_H = int((max_y - min_y) * region_ratio)
region_W = int((max_x - min_x) * region_ratio)
# Randomly select a region to apply noise
selected_region = random.choice(
["top-right", "top-left", "bottom-right", "bottom-left"]
)
if selected_region == "top-right":
selected_edges = np.array(
[
p
for p in object_edge_pixels
if p[0] < min_y + region_H and p[1] > max_x - region_W
]
)
elif selected_region == "top-left":
selected_edges = np.array(
[
p
for p in object_edge_pixels
if p[0] < min_y + region_H and p[1] < min_x + region_W
]
)
elif selected_region == "bottom-right":
selected_edges = np.array(
[
p
for p in object_edge_pixels
if p[0] > max_y - region_H and p[1] > max_x - region_W
]
)
elif selected_region == "bottom-left":
selected_edges = np.array(
[
p
for p in object_edge_pixels
if p[0] > max_y - region_H and p[1] < min_x + region_W
]
)
if len(selected_edges) == 0:
return noisy_image # No edge pixels in the chosen region
# Determine number of edge pixels to modify
num_noisy_pixels = int(noise_level * len(selected_edges))
if num_noisy_pixels == 0:
return noisy_image # Avoid modifying too few pixels
# Select a random subset of edge pixels in the chosen region
noisy_indices = np.random.choice(
len(selected_edges), num_noisy_pixels, replace=False
)
# Apply noise to selected edge pixels
for idx in noisy_indices:
y, x = selected_edges[idx]
noisy_image[y, x] = np.random.choice(
[0, 2]
) # Object (1) flips to background (0) or support surface (2)
return noisy_image
def render_images_given_scene(
scene_info: Dict, camera_intrinsics: np.array, camera_poses: List, image_size: int
) -> Tuple[np.array, np.array, np.array]:
# TODO: Make rendering step more efficient
r = Renderer()
r.create_camera(camera_intrinsics, image_size, znear=0.04, zfar=100)
for s in scene_info:
r.add_object(s["name"], s["mesh"], s["pose"])
colors = []
depths = []
segs = []
labels = []
for cam_pose in camera_poses:
r.set_camera_pose(cam_pose)
color, depth, seg, labels = r.render_all()
depth = add_depth_noise(depth)
colors.append(color)
depths.append(depth)
segs.append(seg)
labels.append(labels)
colors, depths, segs = np.array(colors), np.array(depths), np.array(segs)
return colors, depths, segs, labels
def add_edge_noise(segmentation_image, noise_level=0.40):
"""
Adds noise near the internal edges of objects in a segmentation image while avoiding image borders.
Parameters:
- segmentation_image (numpy array, shape (H, W)): The input segmentation image.
- noise_level (float): Fraction of internal edge pixels to perturb.
Returns:
- noisy_image (numpy array, shape (H, W)): The noisy segmentation image.
"""
noisy_image = segmentation_image.copy()
H, W = segmentation_image.shape
# Define a simple 3x3 kernel to detect edges
kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
# Apply convolution to detect edges (nonzero values indicate edges)
edge_map = np.abs(
convolve(segmentation_image.astype(np.float32), kernel, mode="constant")
)
# Get edge pixel coordinates, but exclude image borders
edge_pixels = np.argwhere(
(edge_map > 0)
& (np.arange(H)[:, None] > 0)
& (np.arange(H)[:, None] < H - 1) # Avoid first & last row
& (np.arange(W) > 0)
& (np.arange(W) < W - 1)
) # Avoid first & last column
num_edge_pixels = len(edge_pixels)
if num_edge_pixels == 0:
return noisy_image # No change if no internal edges detected
# Determine number of edge pixels to modify
num_noisy_pixels = int(noise_level * num_edge_pixels)
if num_noisy_pixels == 0:
return noisy_image # Avoid modifying too few pixels
# Select a random subset of internal edge pixels
noisy_indices = np.random.choice(num_edge_pixels, num_noisy_pixels, replace=False)
# Apply noise to selected internal edge pixels
for idx in noisy_indices:
y, x = edge_pixels[idx]
current_label = segmentation_image[y, x]
if current_label == 1:
# Object (1) can flip to background (0) or support surface (2)
new_label = np.random.choice([0, 2])
elif current_label == 2:
# Support surface (2) can flip to object (1)
new_label = 1
else:
continue # Background (0) remains unchanged
noisy_image[y, x] = new_label
return noisy_image
def render_point_cloud_from_object(
object_grasp_data: ObjectGraspDataset,
num_points: int,
prob_object_only: float = 0.75,
) -> Tuple[Union[np.array, None], DataLoaderError]:
if not RENDERING_AVAILABLE:
raise RuntimeError(
"Rendering is not available in this environment. "
"Pyrender requires OpenGL/GLU libraries and a display or EGL/OSMesa backend. "
"For inference-only usage, use mesh_mode=True to avoid rendering."
)
object_only = np.random.random() < prob_object_only
scene_info = []
T_move_to_obj_frame = tra.translation_matrix(
-object_grasp_data.object_mesh.centroid
)
if object_only:
camera_pose_randomization_params = {
"azimuth": [np.radians(-180), np.radians(180)],
"elevation": [np.radians(-180), np.radians(180)],
"radius": [0.30, 0.60],
}
camera_look_at_point = np.array([0.0, 0.0, 0.0])
scene_info.append(
{
"name": "obj",
"mesh": object_grasp_data.object_mesh,
"pose": T_move_to_obj_frame,
}
)
else:
# Places object on a table, adds noise to the instance segmentation
camera_pose_randomization_params = {
"azimuth": [np.radians(-180), np.radians(180)],
"elevation": [np.radians(70), np.radians(110)],
"radius": [0.30, 0.60],
}
import scene_synthesizer
from scene_synthesizer import procedural_assets as pa
obj_id = "obj"
scene = scene_synthesizer.Scene()
table_asset = pa.TableAsset(10.0, 10.4, 0.1)
scene.add_object(
asset=table_asset,
obj_id="table",
transform=np.eye(4),
)
scene.label_support(label="support", erosion_distance=4.0)
obj_asset = scene_synthesizer.assets.MeshAsset(
fname=object_grasp_data.object_asset_path,
scale=object_grasp_data.object_scale,
origin=("center", "center", "bottom"),
)
scene.place_object(
obj_id=obj_id,
obj_asset=obj_asset,
support_id="support",
)
original_asset_origin = scene.graph.transforms.children[obj_id][0]
T_original_assert_origin_to_world = scene.get_transform(original_asset_origin)
scene_info.append(
{"name": "obj", "mesh": object_grasp_data.object_mesh, "pose": np.eye(4)}
)
camera_look_at_point = np.array([0.0, 0.0, 0.0])
scene_info.append(
{
"name": "support",
"mesh": table_asset.mesh(),
"pose": tra.inverse_matrix(T_original_assert_origin_to_world),
}
)
image_size = [256, 256]
fov = 60
azimuth = camera_pose_randomization_params["azimuth"]
elevation = camera_pose_randomization_params["elevation"]
radius = camera_pose_randomization_params["radius"]
num_cameras = 1
camera_poses_opengl, camera_poses_opencv = sample_camera_pose(
camera_look_at_point,
azimuth,
elevation,
radius,
num_cameras,
)
camera_intrinsics_raw = fov_and_size_to_intrinsics(
fov, (image_size[0], image_size[1]), device="cuda"
)
camera_intrinsics = {
"fx": int(camera_intrinsics_raw[0][0].item()),
"fy": int(camera_intrinsics_raw[1][1].item()),
"cx": int(camera_intrinsics_raw[0][2].item()),
"cy": int(camera_intrinsics_raw[1][2].item()),
}
colors, depths, segs, labels = render_images_given_scene(
scene_info, camera_intrinsics, camera_poses_opengl, image_size
)
debug = False
if debug:
vis = create_visualizer()
for s in scene_info:
visualize_mesh(
vis,
f'renderer/{s["name"]}/object_mesh',
s["mesh"],
transform=s["pose"],
color=[169, 169, 169],
)
OBJ_SEG_ID = 1
assert len(camera_poses_opencv) == 1 # This is a decent assumption for now
for i, cam_pose in enumerate(camera_poses_opencv):
depth = depths[i]
pts = depth2points(
depth=depth,
fx=camera_intrinsics["fx"],
fy=camera_intrinsics["fy"],
cx=camera_intrinsics["cx"],
cy=camera_intrinsics["cy"],
)
xyz = pts["xyz"]
mask = pts["index"]
xyz = xyz[mask]
seg = segs[i]
seg = add_edge_noise(seg, noise_level=np.random.uniform(low=0.2, high=0.6))
seg_mask = seg.flatten()[:, np.newaxis][mask] == OBJ_SEG_ID
xyz = xyz[seg_mask.reshape(-1)]
xyz = tra.transform_points(xyz, cam_pose)
if debug:
make_frame(vis, f"renderer/poses/camera/{i}", T=cam_pose)
visualize_pointcloud(
vis, f"renderer/scene_pc/{i}", xyz, color=[10, 250, 10], size=0.003
)
if debug:
input()
init_num_pts = xyz.shape[0]
if init_num_pts < 150:
# Meaningless point cloud, too few pts to give any context
return None, DataLoaderError.RENDERING_ERROR_POINT_CLOUD_VERY_FEW_POINTS
xyz_mask = np.random.randint(0, xyz.shape[0], num_points)
xyz = xyz[xyz_mask] # bring back number of points to self.num_points
if object_only:
xyz = tra.transform_points(
xyz, tra.inverse_matrix(T_move_to_obj_frame)
) # Move back to object frame
return xyz, DataLoaderError.RENDERING_SUCCESS
def render_pc(
object_asset_data: ObjectGraspDataset, num_points: int, mesh_mode: bool = True
) -> Tuple[Dict, DataLoaderError]:
xyz = None
max_partial_pc_size = 0.075
if mesh_mode:
# Sample points from mesh surface
object_mesh = object_asset_data.object_mesh.copy()
xyz, _ = trimesh.sample.sample_surface(object_mesh, num_points)
xyz = np.array(xyz)
else:
# Sample partial point cloud
try:
xyz, error_code = render_point_cloud_from_object(
object_asset_data, num_points
)
except Exception as e:
logger.error(
f"Encoutered exception when rendering point cloud of object: {str(e)}"
)
xyz = None
return {"invalid": True}, DataLoaderError.RENDERING_ERROR
if error_code == DataLoaderError.RENDERING_ERROR_POINT_CLOUD_VERY_FEW_POINTS:
return {
"invalid": True
}, DataLoaderError.RENDERING_ERROR_POINT_CLOUD_VERY_FEW_POINTS
bounds = xyz.max(axis=0) - xyz.min(axis=0)
if bounds.max() < max_partial_pc_size:
logger.error(
f"Partial point cloud is too small? Bounds [x, y, z]: {bounds}"
)
xyz = None
return {
"invalid": True
}, DataLoaderError.RENDERING_ERROR_POINT_CLOUD_TOO_SMALL
T_move_to_pc_mean = tra.translation_matrix(-xyz.mean(axis=0))
xyz = tra.transform_points(xyz, T_move_to_pc_mean)
xyz = torch.from_numpy(xyz).float()
outputs = {"T_move_to_pc_mean": T_move_to_pc_mean, "points": xyz, "invalid": False}
return outputs, DataLoaderError.RENDERING_SUCCESS
================================================
FILE: grasp_gen/dataset/suction.py
================================================
import os
import sys
import tqdm
import json
import scipy
import trimesh
import argparse
import numpy as np
from qpsolvers import solve_qp
from trimesh import transformations as tra
# Add meshcat imports for visualization
from grasp_gen.utils.meshcat_utils import (
create_visualizer,
visualize_pointcloud,
visualize_mesh,
visualize_grasp,
get_color_from_score,
)
def make_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--object", default="meshes/metal_sheet.obj", help="Object mesh."
)
parser.add_argument(
"--root-dir",
default="",
help="Root directory for object mesh path. This will not be stored in the output.json.",
)
parser.add_argument(
"--gripper",
required=False,
help="JSON or Yaml file describing the vacuum gripper.",
)
parser.add_argument(
"--grasps",
default=None,
help="If specified, will evaluate grasps only (no sampling).",
)
parser.add_argument(
"--num", default=1000, type=int, help="Number of suction grasps."
)
parser.add_argument(
"--scale", type=float, default=1.0, required=False, help="Scale of object mesh."
)
parser.add_argument(
"--num-disturbances",
default=10,
type=int,
help="Number of random disturbance samples.",
)
parser.add_argument(
"--qp-solver",
default="clarabel",
choices=("clarabel", "cvxopt", "daqp", "ecos", "osqp", "scs"),
help="Name of the QP solver to use.",
)
parser.add_argument(
"--output",
default=None,
help="Name of the JSON file to write results to. Compatible with acronym-pipeline format.",
)
parser.add_argument("--no-viz", action="store_true", help="No visualization.")
parser.add_argument(
"--random-seed",
type=int,
default=None,
help="Random seed. Defaults to None.",
)
parser.add_argument(
"--random-seed-eval",
type=int,
default=None,
help="Random seed for evaluation. Defaults to None.",
)
return parser
def colorized_points(points, quality):
return trimesh.PointCloud(vertices=points, colors=colorize(quality))
def colorize(quality):
colors = np.zeros(shape=(len(quality), 4), dtype=np.uint8)
colors[:, 3] = 255
# colors[:, 1] = np.array(quality) * 255
# colors[:, 0] = 255 - np.array(quality) * 255
for i, qual in enumerate(quality):
colors[i, :3] = color_interpolation(qual)
return colors
def color_interpolation(input):
red = np.array([255, 0, 0])
yellow = np.array([255, 255, 0])
green = np.array([0, 255, 0])
if input < 0.5:
return (1.0 - 2.0 * input) * red + 2.0 * input * yellow
else:
return (2.0 - 2.0 * input) * yellow + (2.0 * input - 1.0) * green
def colorize_for_meshcat(quality):
"""Convert quality scores to colors for meshcat visualization."""
colors = np.zeros(shape=(len(quality), 3), dtype=np.uint8)
for i, qual in enumerate(quality):
colors[i, :3] = color_interpolation(qual)
return colors
def skew(x):
"""Create skew-symmetric matrix from vector for cross-product via matrix multiplication."""
return np.array([[0, -x[2], x[1]], [x[2], 0, -x[0]], [-x[1], x[0], 0]])
def adjoint_transform(frame_a, frame_b):
"""Used to transform wrenches or twists between different reference frames.
E.g.: adj_ab = adjoint_transform(a, b) can be used to transform
this wrench: f_a = np.dot(adj_ab.T, f_b)
see e.g. p. 62 of MLS: 'A Mathematical Introduction to Robotic Manipulation'
"""
transform_ab = np.matmul(frame_a, trimesh.transformations.inverse_matrix(frame_b))
rot_ab = transform_ab[:3, :3]
tra_ab = transform_ab[:3, 3]
adjoint = np.zeros((6, 6))
adjoint[:3, :3] = rot_ab
adjoint[3:, 3:] = rot_ab
adjoint[:3, 3:] = np.matmul(skew(tra_ab), rot_ab)
return adjoint
def sunflower_radius(k, n, b):
if k > n - b:
return 1.0
else:
return np.sqrt(k - 0.5) / np.sqrt(n - (b + 1) / 2)
def sunflower(n, radius, alpha=2.0, geodesic=False):
golden_ratio = (1 + np.sqrt(5)) / 2
points = []
angle_stride = 360 * golden_ratio if geodesic else 2 * np.pi / golden_ratio**2
b = round(alpha * np.sqrt(n)) # number of boundary points
for k in range(1, n + 1):
r = sunflower_radius(k, n, b) * radius
theta = k * angle_stride
points.append((r * np.cos(theta), r * np.sin(theta), 0.0))
return np.array(points)
class SuctionCupArray(object):
def __init__(
self,
poses,
num_sides,
radius,
height,
spring_max_length_change=0.1,
suction_force=250.0,
material_kappa=0.005,
friction_mu=0.5,
interpolated_perimeter_vertices=0,
standoff_distance=0.001,
num_suction_points_for_hole_check=50,
collision_mesh_fname=None,
):
# pose of apex (?) (height w.r.t. base)
# number of base vertices
# that form an n-gon with circumradius r and
# location of base vertices (opening angle of right pyramid)
self.poses = np.array(poses)
self.num_suction_cups = len(poses)
self.num_sides = num_sides
self.radius = radius
self.height = height
self.standoff_distance = standoff_distance # take scale into account !
self.V = suction_force # newton suction force
self.kappa = (
material_kappa # elastic limit / yield strength of the suction cup material
)
self.mu = friction_mu
self._create_pyramid(
self.num_sides,
self.radius,
self.height,
interpolated_perimeter_vertices=interpolated_perimeter_vertices,
)
self.num_suction_points_for_hole_check = num_suction_points_for_hole_check
self.suction_points = sunflower(
n=num_suction_points_for_hole_check,
radius=self.radius,
alpha=2.0,
geodesic=False,
)
# 10% according to: "Deformation constraints in a mass-spring model to describe rigid cloth behaviour" by X. Provot et al.
# assumption is that all springs are equal - and they are currently in their steady state
self.spring_max_length_change = spring_max_length_change
perimeter_spring_lengths, flexion_spring_lengths, cone_spring_lengths = (
self.get_spring_lengths(obj_mesh=None, suction_ring_transform=None)
)
spring_length = perimeter_spring_lengths[0]
self.perimeter_spring_length_max = spring_length * (
1.0 + spring_max_length_change
)
self.perimeter_spring_length_min = spring_length * (
1.0 - spring_max_length_change
)
spring_length = cone_spring_lengths[0]
self.cone_spring_length_max = spring_length * (1.0 + spring_max_length_change)
self.cone_spring_length_min = spring_length * (1.0 - spring_max_length_change)
spring_length = flexion_spring_lengths[0]
self.flexion_spring_length_max = spring_length * (
1.0 + spring_max_length_change
)
self.flexion_spring_length_min = spring_length * (
1.0 - spring_max_length_change
)
self.collision_mesh = None
if collision_mesh_fname is not None:
self.collision_mesh = trimesh.load(collision_mesh_fname, force="mesh")
self.collision_mesh_fname = collision_mesh_fname
def _create_pyramid(
self, sides, radius, height, rotation=0.0, interpolated_perimeter_vertices=0
):
# generate base polygon
one_segment = np.pi * 2 / sides
vertices = []
total_number_of_vertices = sides + sides * interpolated_perimeter_vertices + 1
mesh_vertex_indices = []
faces = []
perimeter_spring_paths = [] # edges we need to use to calculate path lengths
perimeter_springs = []
cone_springs = []
flexion_springs = []
apex_index = total_number_of_vertices - 1
for i in range(sides):
point = (
np.sin(one_segment * i + rotation) * radius,
np.cos(one_segment * i + rotation) * radius,
0.0,
)
vertices.append(point)
mesh_vertex_indices.append(len(vertices) - 1)
# append interpolated points along segment
if interpolated_perimeter_vertices > 0:
point2 = (
np.sin(one_segment * (i + 1) + rotation) * radius,
np.cos(one_segment * (i + 1) + rotation) * radius,
0.0,
)
pts_x = np.linspace(
point[0],
point2[0],
interpolated_perimeter_vertices + 1,
endpoint=False,
)
pts_y = np.linspace(
point[1],
point2[1],
interpolated_perimeter_vertices + 1,
endpoint=False,
)
pts_z = np.linspace(
point[2],
point2[2],
interpolated_perimeter_vertices + 1,
endpoint=False,
)
tmp = []
for j in range(interpolated_perimeter_vertices):
tmp.append([len(vertices) - 1, len(vertices)])
vertices.append((pts_x[j + 1], pts_y[j + 1], pts_z[j + 1]))
tmp.append(
[len(vertices) - 1, len(vertices) % (total_number_of_vertices - 1)]
)
perimeter_spring_paths.append(tmp)
first_vertex = i * (1 + interpolated_perimeter_vertices)
second_vertex = apex_index
third_vertex = (first_vertex + 1 + interpolated_perimeter_vertices) % (
total_number_of_vertices - 1
)
faces.append([i, sides, (i + 1) % sides])
perimeter_springs.append([first_vertex, third_vertex])
cone_springs.append([first_vertex, second_vertex])
flexion_springs.append(
[
first_vertex,
(third_vertex + 1 + interpolated_perimeter_vertices)
% (total_number_of_vertices - 1),
]
)
# add apex
vertices.append((0, 0, height))
vertices = np.array(vertices)
mesh_vertex_indices.append(len(vertices) - 1)
self.vertices = vertices
self.perimeter_spring_paths = np.array(perimeter_spring_paths)
self.perimeter_springs = np.array(perimeter_springs)
self.interpolated_perimeter_vertices = interpolated_perimeter_vertices
self.cone_springs = np.array(cone_springs)
self.flexion_springs = np.array(flexion_springs)
self.mesh_vertex_indices = np.array(mesh_vertex_indices)
self.faces = np.array(faces)
# self.mesh = trimesh.Trimesh(vertices=vertices[mesh_vertex_indices], faces=faces)
def sample_grasps(self, obj_mesh, num_grasps):
# First sample which suction cup from the array to chose
suction_cup_indices = np.random.randint(
low=0, high=self.num_suction_cups, size=num_grasps
)
suction_cup_poses = self.poses[suction_cup_indices]
# Sample where to attach the suction cup on the object mesh
point_on_surface, face_index = trimesh.sample.sample_surface(
obj_mesh, num_grasps
)
approach_vector = obj_mesh.face_normals[face_index]
# get transformation from point and approach vector
grasp_transforms = []
for p, v, T in zip(point_on_surface, approach_vector, suction_cup_poses):
contact_transform = trimesh.geometry.plane_transform(p, v)
grasp_transform = (
T
@ tra.rotation_matrix(
angle=np.random.uniform(-np.pi, np.pi), direction=[0, 0, 1]
)
@ trimesh.transformations.translation_matrix(
[0, 0, -self.standoff_distance]
)
@ contact_transform
)
# TODO: maybe this can be avoided
grasp_transform = trimesh.transformations.inverse_matrix(grasp_transform)
grasp_transforms.append(grasp_transform)
new_points_on_surface = np.array([ct[:3, 3] for ct in grasp_transforms])
return new_points_on_surface, approach_vector, np.array(grasp_transforms)
def get_spring_lengths(self, obj_mesh, suction_ring_transform):
# intersect all rays emerging from points on suction cup ring
# (shifted towards apex) in approach_vector direction
#
# - Project n-gon vertices onto object surface (along approach vector)
# - Calculate apex distance to base such that average distance == height
if obj_mesh is None:
transformed_vertices = self.vertices
else:
if trimesh.ray.has_embree:
intersector = trimesh.ray.ray_pyembree.RayMeshIntersector(
obj_mesh, scale_to_box=True
)
else:
intersector = trimesh.ray.ray_triangle.RayMeshIntersector(obj_mesh)
transformed_vertices = trimesh.transform_points(
self.vertices, suction_ring_transform, translate=True
)
ray_origins = transformed_vertices[:-1]
# transformed_apex = transformed_vertices[-1]
suction_ring, index_ray, _ = intersector.intersects_location(
ray_origins,
np.tile(-suction_ring_transform[:3, 2], (len(ray_origins), 1)),
multiple_hits=False,
)
if len(suction_ring) != len(ray_origins):
raise RuntimeError("no suction")
inv_index_ray = np.empty(index_ray.size, dtype=np.int32)
for i in np.arange(index_ray.size):
inv_index_ray[index_ray[i]] = i
# suction_ring = np.copy(suction_ring[inv_index_ray])
suction_ring = suction_ring[inv_index_ray]
# calculate apex
apex_param = min(
0,
np.sum(
(suction_ring - suction_ring_transform[:3, 3]).dot(
suction_ring_transform[:3, 2]
)
)
/ self.num_sides
- self.height,
)
apex = (
suction_ring_transform[:3, 3]
- apex_param * suction_ring_transform[:3, 2]
)
transformed_vertices = np.vstack([suction_ring, apex])
if self.interpolated_perimeter_vertices > 0:
perimeter_spring_lengths = np.sum(
[
[
np.linalg.norm(
transformed_vertices[v[0]] - transformed_vertices[v[1]]
)
for v in p
]
for p in self.perimeter_spring_paths
],
axis=1,
)
else:
perimeter_spring_lengths = np.linalg.norm(
transformed_vertices[self.perimeter_springs[:, 0]]
- transformed_vertices[self.perimeter_springs[:, 1]],
axis=1,
)
flexion_spring_lengths = np.linalg.norm(
transformed_vertices[self.flexion_springs[:, 0]]
- transformed_vertices[self.flexion_springs[:, 1]],
axis=1,
)
cone_spring_lengths = np.linalg.norm(
transformed_vertices[self.cone_springs[:, 0]]
- transformed_vertices[self.cone_springs[:, 1]],
axis=1,
)
return perimeter_spring_lengths, flexion_spring_lengths, cone_spring_lengths
def is_sealed(self, obj_mesh, grasp_transform):
# From: https://arxiv.org/pdf/1709.06670.pdf
#
# Calculate energy of suction cup using a spring-mass model
#
#
# (a) The cone faces of the suction cup do not collide with the object
# during approach or in the contact configuration.
sealed = [True] * self.num_suction_cups
suction_cup_collision = False
if suction_cup_collision:
return [False] * self.num_suction_cups
#
# Check for object holes.
#
# (b) The object surface has no holes within the contact ring traced
# out by C's perimeter springs.
holes_present = False
if trimesh.ray.has_embree:
intersector = trimesh.ray.ray_pyembree.RayMeshIntersector(
obj_mesh, scale_to_box=True
)
else:
intersector = trimesh.ray.ray_triangle.RayMeshIntersector(obj_mesh)
# Check all three suction cup seals for holes
for i, suction_cup_T in enumerate(self.poses):
# sample points in suction cup bottom
suction_ring_transform = grasp_transform @ suction_cup_T
suction_points_transformed = trimesh.transform_points(
self.suction_points, suction_ring_transform, translate=True
)
collisions, _, _ = intersector.intersects_location(
suction_points_transformed,
np.tile(-suction_ring_transform[:3, 2], (len(self.suction_points), 1)),
multiple_hits=False,
)
# trimesh.Scene((trimesh.points.PointCloud(suction_points), obj_mesh)).show()
holes_present = len(suction_points_transformed) != len(collisions)
if holes_present:
# s = trimesh.Scene([obj_mesh])
# s.add_geometry(transform=self._last_suction_transform, geometry=dbg_mesh)
# s.show()
sealed[i] = False
if not any(sealed):
return sealed
#
# Calculate Spring Energy
#
# (c) The energy required in each spring to maintain the contact
# configuration is below a real-valued threshold modeling the
# maximum deformation of the suction cup material against the
# object surface.
for i, suction_cup_T in enumerate(self.poses):
if sealed[i]:
try:
(
perimeter_spring_lengths,
flexion_spring_lengths,
cone_spring_lengths,
) = self.get_spring_lengths(
obj_mesh=obj_mesh,
suction_ring_transform=grasp_transform @ suction_cup_T,
)
if any(
perimeter_spring_lengths > self.perimeter_spring_length_max
) or any(
perimeter_spring_lengths < self.perimeter_spring_length_min
):
# logging.warn("Perimenter spring length violation")
sealed[i] = False
if any(cone_spring_lengths > self.cone_spring_length_max) or any(
cone_spring_lengths < self.cone_spring_length_min
):
# logging.warn("Cone spring length violation")
sealed[i] = False
if any(
flexion_spring_lengths > self.flexion_spring_length_max
) or any(flexion_spring_lengths < self.flexion_spring_length_min):
# logging.warn("Flexion spring length violation")
sealed[i] = False
except RuntimeError:
sealed[i] = False
return sealed
def is_wrench_resistant(self, contact_frames, w, qp_solver="clarabel"):
"""Given a contact_frame and disturbance wrench, return whether this grasp can resist the disurbance."""
object_frame = np.eye(4)
contact_wrench_basis = np.eye(6)
grasp_map = []
num_fingers = len(contact_frames)
for contact_frame in contact_frames:
adjoint = adjoint_transform(object_frame, contact_frame)
# the grasp map maps each finger's contact forces/torques
# into the common object's coordinate frame
# It is of dimensioniality 6 x (num_fingers * 6)
grasp_map.append(np.matmul(adjoint.T, contact_wrench_basis))
grasp_map = np.hstack(grasp_map)
# generate quadratic program of form:
#
# minimize
# (1/2) * x.T * P * x + q.T * x
#
# subject to
# G * x <= h
# A * x == b
#
# Our objective:
# ||grasp_map x - w||_2^2 = x^T grasp_map^T grasp_map x - 2 w^T grasp_map x + w^T w
# That means:
# P = 2 * grasp_map^T grasp_map
# q = -2 * w^T grasp_map
P = 2.0 * np.matmul(grasp_map.T, grasp_map)
q = -2.0 * np.dot(w.T, grasp_map)
# Note: f_N = f_z + V
# Our constraints:
# (friction)
# sqrt(3) | f_x | <= mu f_N
# sqrt(3) | f_y | <= mu f_N
# sqrt(3) | t_z | <= r mu f_N
# (material)
# sqrt(2) | t_x | <= pi radius kappa
# sqrt(2) | t_y | <= pi radius kappa
# (suction)
# f_z >= -V
sqrt3 = np.sqrt(3.0)
sqrt2 = np.sqrt(2.0)
G = np.array(
[
[sqrt3, 0, -self.mu, 0, 0, 0],
[-sqrt3, 0, -self.mu, 0, 0, 0],
[0, sqrt3, -self.mu, 0, 0, 0],
[0, -sqrt3, -self.mu, 0, 0, 0],
[0, 0, -self.mu * self.radius, 0, 0, sqrt3],
[0, 0, -self.mu * self.radius, 0, 0, -sqrt3],
[0, 0, 0, sqrt2, 0, 0],
[0, 0, 0, -sqrt2, 0, 0],
[0, 0, 0, 0, sqrt2, 0],
[0, 0, 0, 0, -sqrt2, 0],
[0, 0, -1.0, 0, 0, 0],
]
)
G = scipy.linalg.block_diag(*([G] * num_fingers))
h = np.array(
[
self.mu * self.V,
self.mu * self.V,
self.mu * self.V,
self.mu * self.V,
self.radius * self.mu * self.V,
self.radius * self.mu * self.V,
np.pi * self.radius * self.kappa,
np.pi * self.radius * self.kappa,
np.pi * self.radius * self.kappa,
np.pi * self.radius * self.kappa,
self.V,
]
)
h = np.tile(h, num_fingers)
x = solve_qp(
P=scipy.sparse.csc_matrix(P),
q=q,
G=scipy.sparse.csc_matrix(G),
h=h,
solver=qp_solver,
)
# other options: ['cvxopt', 'daqp', 'ecos', 'osqp', 'scs']
# ['clarabel', 'daqp', 'ecos', 'osqp', 'scs']
#
# Seem to work: osqp, scs, clarabel
#
# Run-time for 2000 grasps:
# clarabel: 31s (without np.ndarray)
# clarabel: 30s (with scipy.sparse.csc_matrix) <---
# osqp: 34s
# scs: 67s
if x is None:
return False, None
res = np.linalg.norm(grasp_map.dot(x) - w) ** 2
# print("QP solution: res = {}".format(res))
return np.isclose(res, 0.0, atol=1e-3), x
def evaluate_grasps(
self,
obj_mesh,
points_on_surface,
approach_vectors,
grasp_transforms,
num_disturbances=10,
qp_solver="clarabel",
tqdm_disable=True,
):
"""Check seal formation and wrench resistance of suction grasps."""
# check sealing
sealed = []
for tf in tqdm.tqdm(grasp_transforms, disable=tqdm_disable):
seal = self.is_sealed(obj_mesh, grasp_transform=tf)
sealed.append(seal)
sealed = np.array(sealed)
# sealing operation; here: if a single cup is sealed it's fine
# sealed = sealed.sum(axis=1).clip(0, 1)
# here: all suction cups need to be sealed
sealed = sealed.all(axis=1)
# print(f"Number of sealed grasps: {sum(sealed)} out of {len(sealed)}")
# Maybe move this into is_sealed(), see (a)
# check collisions
collision_mesh_gripper = self.collision_mesh
collision_mgr_gripper, collision_mgr_gripper_node_names = (
trimesh.collision.scene_to_collision(collision_mesh_gripper.scene())
)
collision_mgr_gripper_node_names = list(collision_mgr_gripper_node_names.keys())
assert len(collision_mgr_gripper_node_names) <= 1
collision_mgr, _ = trimesh.collision.scene_to_collision(obj_mesh.scene())
grasp_in_collision = []
# create random disturbances
disturbances = np.random.normal(size=(num_disturbances, 3))
disturbances = disturbances / np.linalg.norm(disturbances, axis=1).reshape(
-1, 1
)
grasp_success = []
for tf, is_sealed in tqdm.tqdm(
zip(grasp_transforms, sealed),
total=len(grasp_transforms),
disable=tqdm_disable,
):
# contact_frame = trimesh.geometry.plane_transform(point_on_surf, approach_v)
# contact_frame = trimesh.transformations.inverse_matrix(contact_frame)
# contact_frame = np.matmul(
# contact_frame, trimesh.transformations.euler_matrix(np.pi, 0, 0)
# )
if not is_sealed:
grasp_success.append(0.0)
grasp_in_collision.append(False)
else:
# check collisions
collision_mgr_gripper.set_transform(
name=collision_mgr_gripper_node_names[0], transform=tf
)
in_collision = collision_mgr.in_collision_other(collision_mgr_gripper)
grasp_in_collision.append(in_collision)
suction_contacts = [p @ tf for p in self.poses]
succ = []
for dist in disturbances:
disturbance = np.hstack([dist, [0, 0, 0]])
# TODO transform to suction ring transform
suc, _ = self.is_wrench_resistant(
suction_contacts, disturbance, qp_solver=qp_solver
)
succ.append(suc)
grasp_success.append(sum(succ) / len(succ))
return (
points_on_surface,
approach_vectors,
grasp_transforms,
sealed,
np.array(grasp_success),
np.array(grasp_in_collision),
)
def as_dict(self):
return {
"poses": self.poses.tolist(),
"num_sides": self.num_sides,
"radius": self.radius,
"height": self.height,
"suction_force": self.V,
"material_kappa": self.kappa,
"friction_mu": self.mu,
"interpolated_perimeter_vertices": self.interpolated_perimeter_vertices,
"spring_max_length_change": self.spring_max_length_change,
"standoff_distance": self.standoff_distance,
"num_suction_points_for_hole_check": self.num_suction_points_for_hole_check,
"collision_mesh_fname": self.collision_mesh_fname,
}
@classmethod
def from_file(cls, fname, key=None):
if fname.endswith(".json"):
import json
data = json.load(open(fname, "r"))
elif fname.endswith(".yaml"):
import yaml
data = yaml.safe_load(open(fname, "r"))
if key is not None:
data = data[key]
return cls(
poses=data["poses"],
num_sides=data["num_sides"],
radius=data["radius"],
height=data["height"],
suction_force=data["suction_force"],
material_kappa=data["material_kappa"],
friction_mu=data["friction_mu"],
interpolated_perimeter_vertices=data["interpolated_perimeter_vertices"],
spring_max_length_change=data["spring_max_length_change"],
standoff_distance=data["standoff_distance"],
num_suction_points_for_hole_check=data["num_suction_points_for_hole_check"],
collision_mesh_fname=data["collision_mesh_fname"],
)
# make function so I can call from fastapi service
def main_func(args):
if args.output is not None:
if os.path.isfile(args.output) and os.access(args.output, os.R_OK):
print(f"File {args.output} already exists. Skipping.")
sys.exit(0)
if args.random_seed:
np.random.seed(args.random_seed)
if args.gripper:
suction_gripper = SuctionCupArray.from_file(fname=args.gripper)
else:
suction_gripper = SuctionCupArray(
poses=np.array([np.eye(4)]),
num_sides=15,
radius=0.025,
height=0.06,
friction_mu=0.5,
material_kappa=0.005,
collision_mesh_fname=os.path.join(args.root_dir, args.object),
)
if not args.grasps:
# load mesh
fname_object_mesh = os.path.join(args.root_dir, args.object)
obj_mesh = trimesh.load(fname_object_mesh)
if args.scale != 1.0:
obj_mesh.apply_scale(args.scale)
# fix object's CoM
obj_center_mass = obj_mesh.center_mass
obj_mesh.apply_translation(-obj_center_mass)
print(
f"Dimensions of {fname_object_mesh}: {obj_mesh.extents}, CoM: {obj_center_mass}"
)
# sample grasps
import time
t0 = time.time()
points_on_surface, approach_vectors, grasp_transforms = (
suction_gripper.sample_grasps(obj_mesh=obj_mesh, num_grasps=args.num)
)
print(f"Sampling {args.num} grasps took {time.time() - t0}s")
else:
# Use grasps from input file -- for now assume JSON
grasps = json.load(open(args.grasps, "r"))
args.object = grasps["object"]["file"]
args.scale = grasps["object"]["scale"]
fname_object_mesh = os.path.join(args.root_dir, grasps["object"]["file"])
obj_mesh = trimesh.load(fname_object_mesh)
if grasps["object"].get("scale", 1.0) != 1.0:
obj_mesh.apply_scale(grasps["object"]["scale"])
# fix object's CoM
obj_center_mass = obj_mesh.center_mass
obj_mesh.apply_translation(-obj_center_mass)
grasp_transforms = np.array(grasps["grasps"]["transforms"])
grasp_transforms[:, :3, 3] -= obj_center_mass
approach_vectors = np.array([None] * len(grasp_transforms))
points_on_surface = np.copy(grasp_transforms[:, :3, 3])
# evaluate
if args.random_seed_eval:
np.random.seed(args.random_seed_eval)
import time
t0 = time.time()
points, approach_vectors, contact_transforms, sealed, success, in_collision = (
suction_gripper.evaluate_grasps(
obj_mesh=obj_mesh,
points_on_surface=points_on_surface,
approach_vectors=approach_vectors,
grasp_transforms=grasp_transforms,
num_disturbances=args.num_disturbances,
qp_solver=args.qp_solver,
)
)
print(f"Evaluating {args.num} grasps took {time.time() - t0}s")
if args.output is not None:
if False:
# Filter only successful ones -- not used
# won't work with in_collision None
success_idx = sealed.nonzero()
success_idx = np.intersect1d(success_idx, (~in_collision).nonzero())
object_in_gripper = [True] * len(success_idx)
else:
success_idx = np.arange(len(contact_transforms))
object_in_gripper = np.logical_and(sealed, ~(in_collision.astype(bool)))
# Transform data according to mesh.center_mass
suction_points = points
if points[0] is not None:
suction_points += obj_center_mass
output_transforms = np.copy(contact_transforms)
output_transforms[:, :3, 3] += obj_center_mass
# ACRONYM dataset file format
output_dict = {
"object": {
"file": args.object, # this will be without root_dir
"scale": args.scale,
},
"gripper": suction_gripper.as_dict(),
"grasps": {
"transforms": output_transforms[success_idx].tolist(),
"suction_points": suction_points[success_idx].tolist(),
"approach_vectors": approach_vectors[success_idx].tolist(),
"object_in_gripper": object_in_gripper[success_idx].tolist(),
"is_sealed": sealed[success_idx].tolist(),
"score": success[success_idx].tolist(),
},
}
print(
f"Writing result to ({sum(object_in_gripper)}/{len(success_idx)} successes): {args.output}"
)
with open(args.output, "w") as f:
json.dump(output_dict, f)
if not args.no_viz:
# Create meshcat visualizer
vis = create_visualizer()
gripper_name = "single_suction_cup_30mm"
# Visualize object mesh
visualize_mesh(vis, "object_mesh", obj_mesh, color=[169, 169, 169])
# Visualize sealed configurations
print(f"Visualizing sealed configurations ({sum(sealed)}/{len(sealed)})")
sealed_colors = colorize_for_meshcat(sealed.astype(float))
visualize_pointcloud(vis, "sealed_points", points, sealed_colors, size=0.005)
# Visualize configurations not in collision
print(
f"Visualizing configurations not in collision ({sum(~in_collision)}/{len(in_collision)})"
)
collision_colors = colorize_for_meshcat((~in_collision).astype(float))
visualize_pointcloud(
vis, "collision_free_points", points, collision_colors, size=0.005
)
# Visualize quality of configurations
print(
f"Visualizing quality of configurations (configs greater zero: {sum(success > 0)}/{len(success)}). Max: {np.max(success)}"
)
quality_colors = colorize_for_meshcat(success)
visualize_pointcloud(vis, "quality_points", points, quality_colors, size=0.005)
# Visualize best configurations (top 10)
print("Visualizing 'best' configurations (top 10).")
combined_criteria = sealed * success
# Get indices of top 10 grasps
top_indices = np.argsort(combined_criteria)[-10:][
::-1
] # Sort descending and take top 10
# Visualize the top 10 grasps
for i, idx in enumerate(top_indices):
grasp_transform = contact_transforms[idx]
grasp_score = combined_criteria[idx]
# Use different colors for different ranks (red for best, orange for others)
if i == 0:
color = [255, 0, 0] # Red for best
else:
color = [255, 165, 0] # Orange for others
# Visualize the grasp
visualize_grasp(
vis,
f"best_grasps/grasp_{i:02d}",
grasp_transform,
color=color,
gripper_name=gripper_name,
linewidth=0.6,
)
# # Visualize gripper mesh at this position
# if suction_gripper.collision_mesh is not None:
# visualize_mesh(
# vis,
# f"best_grippers/gripper_{i:02d}",
# suction_gripper.collision_mesh,
# color=color,
# transform=grasp_transform
# )
print(f" Grasp {i+1}: Score = {grasp_score:.3f}")
print(
f"Visualized {len([idx for idx in top_indices if combined_criteria[idx] > 0])} grasps with positive scores."
)
print("Visualization complete. Check meshcat browser window.")
input("Press Enter to continue...")
if __name__ == "__main__":
parser = make_parser()
args = parser.parse_args()
main_func(args)
================================================
FILE: grasp_gen/dataset/visualize_utils.py
================================================
import numpy as np
import torch
import trimesh
from grasp_gen.utils.meshcat_utils import (
create_visualizer,
make_frame,
visualize_grasp,
visualize_mesh,
visualize_pointcloud,
)
MAPPING_ID2NAME = {
0: "pos_true",
1: "neg_true",
2: "neg_hncolliding",
3: "neg_freespace",
4: "neg_hnretract",
5: "pos_true_onpolicy",
6: "neg_true_onpolicy",
}
MAPPING_NAME2ID = {val: key for key, val in MAPPING_ID2NAME.items()}
def visualize_generator_dataset(
obj_pose,
grasps,
gripper_name,
load_contact,
contacts,
pointcloud,
gripper_visual_mesh=None,
obj_mesh=None,
):
vis = create_visualizer()
vis.delete()
if obj_mesh is not None:
visualize_mesh(
vis, "object_mesh", obj_mesh, color=[192, 192, 192], transform=obj_pose
)
for i, g in enumerate(grasps):
if torch.is_tensor(g):
g = g.cpu().numpy()
if i < 20:
visualize_grasp(
vis,
f"grasps/{i:03d}",
g.astype(np.float32),
[0, 255, 0],
gripper_name=gripper_name,
linewidth=0.2,
)
if pointcloud is not None:
visualize_pointcloud(
vis, f"point_cloud", pointcloud, [10, 10, 250], size=0.0025
)
if load_contact:
if len(contacts.shape) == 3:
contacts[:, 0, :] = tra.transform_points(contacts[:, 0, :], obj_pose)
contacts[:, 1, :] = tra.transform_points(contacts[:, 1, :], obj_pose)
else:
contacts = tra.transform_points(contacts, obj_pose)
visualize_pointcloud(vis, f"contact_points", contacts, [0, 0, 255], size=0.007)
input()
def visualize_discriminator_dataset(
batch_data: dict,
scene_mesh: trimesh.base.Trimesh,
gripper_name: str,
gripper_mesh: trimesh.base.Trimesh = None,
pointcloud: torch.Tensor = None,
):
grasps = batch_data["grasps"]
grasp_ids = batch_data["grasp_ids"].squeeze(1)
pos_grasps = grasps[grasp_ids == MAPPING_NAME2ID["pos_true"]].cpu().numpy()
neg_grasps_tn = grasps[grasp_ids == MAPPING_NAME2ID["neg_true"]].cpu().numpy()
neg_grasps_hn = (
grasps[grasp_ids == MAPPING_NAME2ID["neg_hncolliding"]].cpu().numpy()
)
neg_grasps_fs = grasps[grasp_ids == MAPPING_NAME2ID["neg_freespace"]].cpu().numpy()
neg_grasps_retract = (
grasps[grasp_ids == MAPPING_NAME2ID["neg_hnretract"]].cpu().numpy()
)
pos_grasps_onpolicy = (
grasps[grasp_ids == MAPPING_NAME2ID["pos_true_onpolicy"]].cpu().numpy()
)
neg_grasps_onpolicy = (
grasps[grasp_ids == MAPPING_NAME2ID["neg_true_onpolicy"]].cpu().numpy()
)
vis = create_visualizer()
vis.delete()
visualize_mesh(vis, "scene_mesh", scene_mesh, color=[192, 192, 192])
max_grasps_to_visualize = 15
for j, grasp in enumerate(pos_grasps):
# visualize_mesh(vis, f"gt/pos_mesh/all/grasp_{j:03d}", gripper_mesh, color=[0, 255, 0], transform=grasp.astype(np.float32))
visualize_grasp(
vis,
f"gt-offline/pos/pos_true/grasp_{j:03d}",
grasp,
[0, 255, 0],
gripper_name=gripper_name,
linewidth=0.2,
)
if j > max_grasps_to_visualize:
break
for j, grasp in enumerate(neg_grasps_tn):
visualize_grasp(
vis,
f"gt-offline/neg/neg_true/grasp_{j:03d}",
grasp,
[255, 0, 0],
gripper_name=gripper_name,
linewidth=0.2,
)
if j > max_grasps_to_visualize:
break
for j, grasp in enumerate(neg_grasps_hn):
visualize_grasp(
vis,
f"gt-offline/neg/neg_hncolliding/grasp_{j:03d}",
grasp,
[255, 0, 0],
gripper_name=gripper_name,
linewidth=0.2,
)
if j > max_grasps_to_visualize:
break
for j, grasp in enumerate(neg_grasps_fs):
visualize_grasp(
vis,
f"gt-offline/neg/neg_freespace/grasp_{j:03d}",
grasp,
[255, 0, 0],
gripper_name=gripper_name,
linewidth=0.2,
)
if j > max_grasps_to_visualize:
break
for j, grasp in enumerate(neg_grasps_retract):
visualize_grasp(
vis,
f"gt-offline/neg/neg_hnretract/grasp_{j:03d}",
grasp,
[255, 0, 0],
gripper_name=gripper_name,
linewidth=0.2,
)
if j > max_grasps_to_visualize:
break
for j, grasp in enumerate(pos_grasps_onpolicy):
# visualize_mesh(vis, f"gt/pos_mesh/all/grasp_{j:03d}", gripper_mesh, color=[0, 255, 0], transform=grasp.astype(np.float32))
visualize_grasp(
vis,
f"gt-ongenerator/pos/grasp_{j:03d}",
grasp,
[0, 255, 0],
gripper_name=gripper_name,
linewidth=0.2,
)
if j > max_grasps_to_visualize:
break
for j, grasp in enumerate(neg_grasps_onpolicy):
visualize_grasp(
vis,
f"gt-ongenerator/neg/grasp_{j:03d}",
grasp,
[255, 0, 0],
gripper_name=gripper_name,
linewidth=0.2,
)
if j > max_grasps_to_visualize:
break
if pointcloud is not None:
if type(pointcloud) == torch.Tensor:
pointcloud = pointcloud.cpu().numpy()
visualize_pointcloud(
vis, f"point_cloud", pointcloud, [10, 10, 250], size=0.0025
)
input()
================================================
FILE: grasp_gen/dataset/webdataset_utils.py
================================================
import glob
import json
import os
import time
from pathlib import Path
from typing import Dict, Optional, Tuple
import webdataset as wds
from tqdm import tqdm
from grasp_gen.utils.logging_config import get_logger
logger = get_logger(__name__)
def load_uuid_list(uuid_list_path: str) -> list[str]:
"""
Load a list of UUIDs from a JSON or text file.
Args:
uuid_list_path (str): Path to the UUID list file
Returns:
list[str]: List of UUIDs
"""
if not os.path.exists(uuid_list_path):
raise FileNotFoundError(f"UUID list file not found: {uuid_list_path}")
if uuid_list_path.endswith(".json"):
with open(uuid_list_path, "r") as f:
uuids = json.load(f)
if type(uuids) == list:
return uuids
elif type(uuids) == dict:
return list(uuids.keys())
else:
raise ValueError(f"UUID list is not a list or dict: {uuids}")
elif uuid_list_path.endswith(".txt"):
with open(uuid_list_path, "r") as f:
uuids = [line.strip() for line in f.readlines()]
else:
raise ValueError(f"Unsupported file format: {uuid_list_path}")
return uuids
def convert_to_webdataset(
root_dir: str, num_shards: int = 8, output_dir: str = None
) -> None:
"""
Convert dataset to WebDataset format and create an index file.
Args:
root_dir (str): Path to root directory containing object folders
num_shards (int): Number of shards to create (default: 8)
"""
# Create output directory for shards
if output_dir is None:
output_dir = os.path.join(root_dir, "webdataset_shards")
os.makedirs(output_dir, exist_ok=True)
# Initialize UUID to shard index mapping
uuid_to_shard = {}
# Get all folders matching the pattern _
object_folders = glob.glob(os.path.join(root_dir, "*_*"))
# Calculate items per shard
items_per_shard = len(object_folders) // num_shards
# Create WebDataset shards
for shard_idx in range(num_shards):
shard_name = f"{output_dir}/shard_{shard_idx:03d}.tar"
start_idx = shard_idx * items_per_shard
end_idx = (
start_idx + items_per_shard
if shard_idx < num_shards - 1
else len(object_folders)
)
logger.info(f"Creating shard {shard_idx + 1}/{num_shards}")
with wds.TarWriter(shard_name) as sink:
# Process folders for this shard
for folder in tqdm(object_folders[start_idx:end_idx]):
# Get integer_id and uuid from folder name
folder_name = os.path.basename(folder)
integer_id, uuid = folder_name.split("_", 1)
# Store shard location for this UUID
uuid_to_shard[uuid] = shard_idx
# Read grasps data
grasp_file = os.path.join(folder, "grasps.json")
with open(grasp_file, "r") as f:
grasps_data = json.load(f)
# Create sample dictionary using uuid as key
sample = {
"__key__": uuid,
"integer_id": integer_id,
"grasps.json": json.dumps(grasps_data),
}
# Write sample to shard
sink.write(sample)
# Save the UUID to shard mapping
index_path = os.path.join(output_dir, "uuid_index.json")
with open(index_path, "w") as f:
json.dump(uuid_to_shard, f)
def is_webdataset(dataset_path: str) -> bool:
"""
Check if directory has tar files.
Args:
directory (str): Path to directory to check
Returns:
bool: True if directory contains at least one .tar file, False otherwise
"""
# Check if directory exists
if not os.path.isdir(dataset_path):
return False
# Look for .json files
tar_files = glob.glob(os.path.join(dataset_path, "*.tar"))
return len(tar_files) > 0
class GraspWebDatasetReader:
"""Class to efficiently read grasps data using a pre-loaded index."""
def __init__(self, dataset_path: str):
"""
Initialize the reader with dataset path and load the index.
Args:
dataset_path (str): Path to directory containing WebDataset shards
"""
self.dataset_path = dataset_path
self.shards_dir = self.dataset_path
# Load the UUID index
index_path = os.path.join(self.shards_dir, "uuid_index.json")
with open(index_path, "r") as f:
self.uuid_index = json.load(f)
# Cache for open datasets
self.shard_datasets = {}
def read_grasps_by_uuid(self, object_uuid: str) -> Optional[Dict]:
"""
Read grasps data for a specific object UUID using the index.
Args:
object_uuid (str): UUID of the object to retrieve
Returns:
Optional[Dict]: Dictionary containing the grasps data if found, None otherwise
"""
if object_uuid not in self.uuid_index:
return None
shard_idx = self.uuid_index[object_uuid]
# Get or create dataset for this shard
if shard_idx not in self.shard_datasets:
shard_path = f"{self.shards_dir}/shard_{shard_idx:03d}.tar"
self.shard_datasets[shard_idx] = wds.WebDataset(shard_path)
dataset = self.shard_datasets[shard_idx]
# Search for the UUID in the specific shard
for sample in dataset:
if sample["__key__"] == object_uuid:
return json.loads(sample["grasps.json"])
return None
================================================
FILE: grasp_gen/grasp_server.py
================================================
# Copyright (c) 2025, 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
import sys
import time
import numpy as np
import omegaconf
import torch
from pathlib import Path
from grasp_gen.dataset.dataset import collate
from grasp_gen.models.grasp_gen import GraspGen
from grasp_gen.models.m2t2 import M2T2
from grasp_gen.utils.point_cloud_utils import knn_points, point_cloud_outlier_removal
from grasp_gen.robot import load_control_points_for_visualization
from grasp_gen.utils.logging_config import get_logger
logger = get_logger(__name__)
def load_grasp_cfg(gripper_config: str) -> omegaconf.DictConfig:
"""
Loads the grasp configuration file and updates the checkpoint paths to be relative to the gripper config file.
Assumes that the checkpoint paths are in the same directory as the gripper config file.
Args:
gripper_config: Path to the gripper configuration file
Returns:
grasp_cfg: Hydra config object with updated checkpoint paths
"""
cfg = omegaconf.OmegaConf.load(gripper_config)
ckpt_root_dir = Path(gripper_config).parent
cfg.eval.checkpoint = str(ckpt_root_dir / cfg.eval.checkpoint)
cfg.discriminator.checkpoint = str(ckpt_root_dir / cfg.discriminator.checkpoint)
assert (
cfg.data.gripper_name
== cfg.diffusion.gripper_name
== cfg.discriminator.gripper_name
)
return cfg
class GraspGenSampler:
def __init__(
self,
cfg: omegaconf.DictConfig,
):
"""
Args:
cfg: Hydra config object
"""
self.cfg = cfg
# Initialize model and load checkpoint
if cfg.eval.model_name == "m2t2":
model = M2T2.from_config(cfg.m2t2)
ckpt = torch.load(cfg.eval.checkpoint)
model.load_state_dict(ckpt["model"])
elif cfg.eval.model_name == "diffusion-discriminator":
model = GraspGen.from_config(
cfg.diffusion,
cfg.discriminator,
)
if not os.path.exists(cfg.eval.checkpoint):
raise FileNotFoundError(
f"Checkpoint {cfg.eval.checkpoint} does not exist"
)
if not os.path.exists(cfg.discriminator.checkpoint):
raise FileNotFoundError(
f"Checkpoint {cfg.discriminator.checkpoint} does not exist"
)
model.load_state_dict(cfg.eval.checkpoint, cfg.discriminator.checkpoint)
model.eval()
else:
raise NotImplementedError(
f"Model name not implemented {cfg.eval.model_name}"
)
self.model = model.cuda().eval()
@staticmethod
def run_inference(
object_pc: np.ndarray | torch.Tensor,
grasp_sampler: "GraspGenSampler",
grasp_threshold: float = -1.0,
num_grasps: int = 200,
topk_num_grasps: int = -1,
min_grasps: int = 40,
max_tries: int = 6,
remove_outliers: bool = True,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Run grasp generation inference on a point cloud.
Args:
object_pc: Point cloud to generate grasps for
grasp_sampler: Initialized GraspGenSampler instance
grasp_threshold: Threshold for valid grasps. If -1.0, then the top topk_grasps grasps will be ranked and returned
num_grasps: Number of grasps to generate
topk_grasps: Maximum number of grasps to return
min_grasps: Minimum number of grasps required. If fewer grasps are found, inference will be retried
max_tries: Maximum number of inference attempts to make before returning results
Returns:
grasps: Generated grasp poses
grasp_conf: Confidence scores for the grasps
"""
if type(object_pc) == np.ndarray:
object_pc = torch.from_numpy(object_pc).cuda().float()
if grasp_threshold == -1.0 and topk_num_grasps == -1:
topk_num_grasps = 100
all_grasps = []
all_conf = []
num_tries = 0
while sum(len(g) for g in all_grasps) < min_grasps and num_tries < max_tries:
num_tries += 1
t0 = time.time()
output = grasp_sampler.sample(
object_pc,
threshold=grasp_threshold,
num_grasps=num_grasps,
remove_outliers=remove_outliers,
)
grasp_conf = output[1]
grasps = output[0]
# Sort and prune grasps within this iteration
if topk_num_grasps != -1 and len(grasps) > 0:
grasp_conf, grasps = zip(
*sorted(zip(grasp_conf, grasps), key=lambda x: x[0], reverse=True)
)
grasps = torch.stack(grasps)
grasp_conf = torch.stack(grasp_conf)
grasps = grasps[:topk_num_grasps]
grasp_conf = grasp_conf[:topk_num_grasps]
all_grasps.append(grasps)
all_conf.append(grasp_conf)
logger.info(
f"Found {len(grasps)} grasps in iteration {len(all_grasps)}, total grasps: {sum(len(g) for g in all_grasps)}"
)
t1 = time.time()
logger.info(f"Time taken for inference: {t1 - t0} seconds")
if len(all_grasps) == 0:
return torch.tensor([]), torch.tensor([])
# Concatenate all grasps and confidences
grasps = torch.cat(all_grasps, dim=0)
grasp_conf = torch.cat(all_conf, dim=0)
grasps[:, 3, 3] = 1 # TODO: Fix this in grasp_gen.py later.
return grasps, grasp_conf
@torch.inference_mode()
def sample(
self,
obj_pcd: np.ndarray,
threshold: float = -1.0,
num_grasps: int = 200,
remove_outliers: bool = True,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Args:
obj_pcd: np.array of shape (N, 3)
obj_pts_color (Optional): np.array of shape (N, 4)
Returns:
grasps: torch.tensor of shape (M, 6)
grasp_conf: torch.tensor of shape (M,)
grasp_contacts: torch.tensor of shape (M, 3)
"""
if remove_outliers:
obj_pcd, _ = point_cloud_outlier_removal(obj_pcd)
obj_pcd_center = obj_pcd.mean(axis=0)
obj_pts_color = torch.zeros_like(obj_pcd)
obj_mean_points = obj_pcd - obj_pcd_center[None]
data = {}
data["task"] = "pick"
data["inputs"] = torch.cat(
[obj_mean_points, obj_pts_color[:, :3].squeeze(1)], dim=-1
).float()
data["points"] = obj_mean_points
data_batch = collate([data])
grasp_key = "grasps"
with torch.inference_mode():
if self.cfg.eval.model_name == "m2t2":
model_outputs = self.model.infer(data_batch, self.cfg.eval)
elif self.cfg.eval.model_name == "diffusion-discriminator":
grasp_key = "grasps_pred"
self.model.grasp_generator.num_grasps_per_object = num_grasps
model_outputs, _, _ = self.model.infer(data_batch)
else:
raise NotImplementedError(f"Invalid model {self.cfg.eval.model_name}!")
if len(model_outputs[grasp_key][0]) == 0:
return [], [], []
grasps = model_outputs[grasp_key][0]
if self.cfg.eval.model_name == "diffusion-discriminator":
grasp_conf = model_outputs["grasp_confidence"][0][:, 0]
logger.info(
f"Confidences min: {grasp_conf.min():.5f}, max: {grasp_conf.max():.5f}"
)
mask_best_grasps = grasp_conf >= threshold
logger.info(
f"Thresholding grasps @ {threshold}. Only {mask_best_grasps.sum()}/{mask_best_grasps.shape[0]} grasps remaining"
)
grasps = grasps[mask_best_grasps]
grasp_conf = grasp_conf[mask_best_grasps]
elif self.cfg.eval.model_name == "m2t2":
grasps = grasps[0]
grasp_conf = model_outputs["grasp_confidence"][0][0]
else:
raise NotImplementedError(f"Invalid model {self.cfg.eval.model_name}!")
grasps[:, :3, 3] += obj_pcd_center
return grasps, grasp_conf, None
# def get_grasp_points(self, pose_array: Pose, gripper_name = None):
# if gripper_name is None:
# gripper_name = self.cfg.data.gripper_name
# line_points = load_control_points_for_visualization(gripper_name)
# line_points = torch.as_tensor(line_points, device="cuda", dtype=torch.float32).view(-1,3)
# line_points = line_points.unsqueeze(0).repeat(pose_array.shape[0],1,1)
# line_points = pose_array.batch_transform_points(line_points)
# return line_points
================================================
FILE: grasp_gen/metrics.py
================================================
# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
from __future__ import annotations
# Third Party
import numpy as np
import torch
import trimesh.transformations as tra
from scipy.spatial import KDTree
from torch import Tensor, nn
from torch.autograd import Function
from grasp_gen.robot import GripperInfo
from grasp_gen.utils.rotation_conversions import matrix_to_quaternion
def compute_recall(
pose_set_a: np.ndarray, pose_set_b: np.ndarray, radius: float = 0.02
) -> float:
# Recompute for tighter tolerance
tree = KDTree(pose_set_a[:, :3, 3])
visited = set()
for i, grasp in enumerate(pose_set_b):
close_indexes = tree.query_ball_point(grasp[:3, 3], radius)
visited.update([(close_index) for close_index in close_indexes])
recall = len(visited) / len(pose_set_a)
return recall
def compute_metrics_given_two_sets_of_poses(
poses_A: torch.Tensor,
poses_B: torch.Tensor,
gripper_info: GripperInfo,
consider_symmetry: bool = False,
) -> Dict[str, float]:
"""
Note that poses_A and poses_B have the same batch size and shape. e.g. [N, 4, 4]
"""
actual_noise_pts_quat = normalize_quaternion(
matrix_to_quaternion(poses_A[:, :3, :3])
)
pred_noise_pts_quat = normalize_quaternion(matrix_to_quaternion(poses_B[:, :3, :3]))
phi3 = angular_distance_phi3(actual_noise_pts_quat, pred_noise_pts_quat)
criterion = GeodesicLoss(reduction="none")
geodesic_dist = criterion(poses_A[:, :3, :3], poses_B[:, :3, :3])
device = geodesic_dist.device
if gripper_info.symmetric and consider_symmetry:
poses_A_mirror = np.array(
[
g @ tra.euler_matrix(0, 0, np.pi)
for g in poses_A.clone().detach().cpu().numpy()
]
)
poses_A_mirror = torch.from_numpy(poses_A_mirror).to(geodesic_dist.device)
geodesic_dist_mirror = criterion(poses_A_mirror[:, :3, :3], poses_B[:, :3, :3])
geodesic_dist = torch.vstack([geodesic_dist_mirror, geodesic_dist])
geodesic_dist = geodesic_dist.min(axis=0)[0]
actual_noise_pts_quat = normalize_quaternion(
matrix_to_quaternion(poses_A_mirror[:, :3, :3])
)
phi3_mirror = angular_distance_phi3(actual_noise_pts_quat, pred_noise_pts_quat)
phi3 = torch.vstack([phi3_mirror, phi3])
phi3 = phi3.min(axis=0)[0]
phi3 = phi3.mean()
geodesic_dist = geodesic_dist.mean()
depth = gripper_info.depth
# TODO - read this depth value from cfg file
poses_A_shifted = np.array(
[
g @ tra.translation_matrix([0, 0, depth])
for g in poses_A.clone().detach().cpu().numpy()
]
)
poses_B_shifted = np.array(
[
g @ tra.translation_matrix([0, 0, depth])
for g in poses_B.clone().detach().cpu().numpy()
]
)
actual_noise_pts_t = poses_A_shifted[:, :3, 3]
pred_noise_pts_t = poses_B_shifted[:, :3, 3]
error = torch.tensor(actual_noise_pts_t - pred_noise_pts_t)
translation_error = torch.linalg.norm(error, dim=1).mean().to(geodesic_dist.device)
stats = {
"error_trans_l2": translation_error,
"error_rot_geodesic": geodesic_dist,
"error_rot_phi3": phi3,
}
return stats
def angular_distance_phi3(
goal_quat: torch.Tensor, current_quat: torch.Tensor
) -> torch.Tensor:
"""This function computes the angular distance phi_3.
See Huynh, Du Q. "Metrics for 3D rotations: Comparison and analysis." Journal of Mathematical
Imaging and Vision 35 (2009): 155-164.
Args:
goal_quat: _description_
current_quat: _description_
Returns:
Angular distance in range [0,1]
"""
dot_prod = (
goal_quat[..., 0] * current_quat[..., 0]
+ goal_quat[..., 1] * current_quat[..., 1]
+ goal_quat[..., 2] * current_quat[..., 2]
+ goal_quat[..., 3] * current_quat[..., 3]
)
dot_prod = torch.abs(dot_prod)
distance = dot_prod
distance = torch.arccos(dot_prod) / (torch.pi * 0.5)
return distance
def quat_multiply(
q1: torch.Tensor, q2: torch.Tensor, q_res: torch.Tensor
) -> torch.Tensor:
a_w = q1[..., 0]
a_x = q1[..., 1]
a_y = q1[..., 2]
a_z = q1[..., 3]
b_w = q2[..., 0]
b_x = q2[..., 1]
b_y = q2[..., 2]
b_z = q2[..., 3]
q_res[..., 0] = a_w * b_w - a_x * b_x - a_y * b_y - a_z * b_z
q_res[..., 1] = a_w * b_x + b_w * a_x + a_y * b_z - b_y * a_z
q_res[..., 2] = a_w * b_y + b_w * a_y + a_z * b_x - b_z * a_x
q_res[..., 3] = a_w * b_z + b_w * a_z + a_x * b_y - b_x * a_y
return q_res
class OrientationError(Function):
@staticmethod
def geodesic_distance(goal_quat, current_quat, quat_res):
conjugate_quat = current_quat.clone()
conjugate_quat[..., 1:] *= -1.0
quat_res = quat_multiply(goal_quat, conjugate_quat, quat_res)
quat_res = -1.0 * quat_res * torch.sign(quat_res[..., 0]).unsqueeze(-1)
quat_res[..., 0] = 0.0
# quat_res = conjugate_quat * 0.0
return quat_res
@staticmethod
def forward(ctx, goal_quat, current_quat, quat_res):
quat_res = OrientationError.geodesic_distance(goal_quat, current_quat, quat_res)
rot_error = torch.norm(quat_res, dim=-1, keepdim=True)
ctx.save_for_backward(quat_res, rot_error)
return rot_error
@staticmethod
def backward(ctx, grad_out):
grad_mul = None
if ctx.needs_input_grad[1]:
(quat_error, r_err) = ctx.saved_tensors
scale = 1 / r_err
scale = torch.nan_to_num(scale, 0, 0, 0)
grad_mul = grad_out * scale * quat_error
# print(grad_out.shape)
# if grad_out.shape[0] == 6:
# #print(grad_out.view(-1))
# #print(grad_mul.view(-1)[-6:])
# #exit()
return None, grad_mul, None
def normalize_quaternion(in_quaternion: torch.Tensor) -> torch.Tensor:
k = torch.sign(in_quaternion[..., 0:1])
# NOTE: torch sign returns 0 as sign value when value is 0.0
k = torch.where(k == 0, 1.0, k)
k2 = k / torch.linalg.norm(in_quaternion, dim=-1, keepdim=True)
# normalize quaternion
in_q = k2 * in_quaternion
return in_q
class GeodesicLoss(nn.Module):
r"""Creates a criterion that measures the distance between rotation matrices, which is
useful for pose estimation problems.
The distance ranges from 0 to :math:`pi`.
See: http://www.boris-belousov.net/2016/12/01/quat-dist/#using-rotation-matrices and:
"Metrics for 3D Rotations: Comparison and Analysis" (https://link.springer.com/article/10.1007/s10851-009-0161-2).
Both `input` and `target` consist of rotation matrices, i.e., they have to be Tensors
of size :math:`(minibatch, 3, 3)`.
The loss can be described as:
.. math::
\text{loss}(R_{S}, R_{T}) = \arccos\left(\frac{\text{tr} (R_{S} R_{T}^{T}) - 1}{2}\right)
Args:
eps (float, optional): term to improve numerical stability (default: 1e-7). See:
https://github.com/pytorch/pytorch/issues/8069.
reduction (string, optional): Specifies the reduction to apply to the output:
``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will
be applied, ``'mean'``: the weighted mean of the output is taken,
``'sum'``: the output will be summed. Default: ``'mean'``
Shape:
- Input: Shape :math:`(N, 3, 3)`.
- Target: Shape :math:`(N, 3, 3)`.
- Output: If :attr:`reduction` is ``'none'``, then :math:`(N)`. Otherwise, scalar.
"""
def __init__(self, eps: float = 1e-7, reduction: str = "mean") -> None:
super().__init__()
self.eps = eps
self.reduction = reduction
def forward(self, input: Tensor, target: Tensor) -> Tensor:
input = input.double()
target = target.double()
R_diffs = input @ target.permute(0, 2, 1)
# See: https://github.com/pytorch/pytorch/issues/7500#issuecomment-502122839.
traces = R_diffs.diagonal(dim1=-2, dim2=-1).sum(-1)
dists = torch.acos(torch.clamp((traces - 1) / 2, -1 + self.eps, 1 - self.eps))
if self.reduction == "none":
return dists
elif self.reduction == "mean":
return dists.mean()
elif self.reduction == "sum":
return dists.sum()
================================================
FILE: grasp_gen/models/action_decoder.py
================================================
# 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.
#
# Author: Wentao Yuan
"""
Modules to compute gripper poses from contact masks and parameters.
"""
import numpy as np
import torch
import torch.nn.functional as F
import trimesh.transformations as tra
from grasp_gen.models.model_utils import MLP, repeat_new_axis
from grasp_gen.robot import get_gripper_depth, get_gripper_info
def double_split(tensor, chunks):
tensor = list(tensor.split([sum(chunk) for chunk in chunks]))
tensor = [
list(elem.split([n for n in chunk])) for elem, chunk in zip(tensor, chunks)
]
return tensor
def build_6d_grasp(
contact_pt, contact_dir, approach_dir, offset, gripper_name="franka_panda"
):
gripper_info = get_gripper_info(gripper_name)
gripper_depth = gripper_info.depth
grasp_translation = contact_pt - gripper_depth * approach_dir
if gripper_info.symmetric_antipodal:
# TODO: Make this a mask
grasp_translation += contact_dir * offset.unsqueeze(-1) / 2
grasp_tr = torch.stack(
[
contact_dir,
torch.cross(approach_dir, contact_dir),
approach_dir,
grasp_translation,
],
axis=-1,
)
last_row = torch.tensor([[0, 0, 0, 1]]).to(grasp_tr.device)
if len(grasp_tr.shape) > 2:
last_row = last_row * torch.ones(
*grasp_tr.shape[:-2], 1, 4, device=grasp_tr.device
)
grasp_tr = torch.cat([grasp_tr, last_row], dim=-2)
return grasp_tr
def build_6d_place(contact_pts, rot, offset, ee_pose):
# Transformation order: first rotate gripper to grasp pose,
# then add offset between gripper center and reference point,
# then rotate around object center, finally translate to contact point.
rot = rot @ ee_pose[..., :3, :3]
trans = (contact_pts + offset).unsqueeze(-1)
place_tr = torch.cat([rot, trans], axis=-1)
last_row = torch.tensor([[0, 0, 0, 1]]).to(place_tr.device)
if len(place_tr.shape) > 2:
last_row = last_row * torch.ones(
*place_tr.shape[:-2], 1, 4, device=place_tr.device
)
place_tr = torch.cat([place_tr, last_row], dim=-2)
return place_tr
def compute_offset(obj_pts, ee_pose, rot, grid_res=0, cam_pose=None):
# rot R is about object center o
# offset is ee position e - target position t
# R(e - o) - R(t - o) = -R(t - e)
if cam_pose is not None:
rot = cam_pose[:3, :3] @ rot
obj_pts_stable = (obj_pts - ee_pose[:3, 3]) @ rot.transpose(-1, -2)
if grid_res > 0:
obj_pts_grid = (obj_pts_stable[..., :2] / grid_res).round()
offset = obj_pts_stable.min(dim=0)[0]
offset[:2] = obj_pts_grid.unique(dim=0).mean(dim=0) * grid_res
else:
offset = obj_pts_stable.mean(dim=0)
offset[..., 2] = obj_pts_stable[..., 2].min(dim=1)[0]
offset = -offset
if cam_pose is not None:
offset = offset @ cam_pose[:3, :3]
return offset
def infer_placements(
xyz, logits, bottom_center, ee_poses, cam_poses, conf_thresh, height
):
rot_prompts = torch.stack(
[
torch.from_numpy(tra.euler_matrix(0, 0, 2 * np.pi / logits.shape[1] * i))[
:3, :3
].float()
for i in range(logits.shape[1])
]
).to(xyz.device)
rot_prompts = repeat_new_axis(rot_prompts, xyz.shape[1], dim=1)
xyz = repeat_new_axis(xyz, logits.shape[1], dim=1)
xyz_world = xyz @ cam_poses[:, :3, :3].transpose(1, 2) + cam_poses[:, :3, 3]
placements, confidence, contact_points = [], [], []
for i, (bc, ee_pose, logit) in enumerate(zip(bottom_center, ee_poses, logits)):
conf = logit.sigmoid()
mask = conf > conf_thresh
num = list(mask.sum(dim=1))
rot = rot_prompts[mask]
offsets = (ee_pose[:3, 3] - bc) @ rot.transpose(1, 2)
contacts = xyz_world[i][mask]
place = build_6d_place(contacts, rot, offsets, ee_pose)
place[:, 2, 3] = place[:, 2, 3] + height
if cam_poses is not None:
place = cam_poses[i].inverse() @ place
placements.append(list(place.split(num)))
confidence.append(list(conf[mask].split(num)))
contact_points.append(list(xyz[i][mask].split(num)))
outputs = {
"placements": placements,
"placement_confidence": confidence,
"placement_contacts": contact_points,
}
return outputs
class ActionDecoder(torch.nn.Module):
def __init__(
self,
mask_dim,
use_embed,
embed_dim,
max_num_pred,
hidden_dim,
num_layers,
activation,
offset_bins,
gripper_depth,
gripper_name,
):
super(ActionDecoder, self).__init__()
feat_dim = mask_dim
if use_embed:
feat_dim += embed_dim
self.feat_dim = feat_dim
self.use_embed = use_embed
self.contact_dir_head = MLP(feat_dim, hidden_dim, 3, num_layers, activation)
self.approach_dir_head = MLP(feat_dim, hidden_dim, 3, num_layers, activation)
self.offset_head = MLP(
feat_dim, hidden_dim, len(offset_bins) - 1, num_layers, activation
)
offset_bins = torch.tensor(offset_bins).float()
self.offset_vals = (offset_bins[:-1] + offset_bins[1:]) / 2
self.max_num_pred = max_num_pred
self.gripper_depth = gripper_depth
self.gripper_name = gripper_name
@classmethod
def from_config(cls, cfg, contact_decoder):
args = {}
args["mask_dim"] = contact_decoder.mask_dim
args["use_embed"] = cfg.use_embed
args["embed_dim"] = contact_decoder.embed_dim
args["max_num_pred"] = cfg.max_num_pred
args["hidden_dim"] = cfg.hidden_dim
args["num_layers"] = cfg.num_layers
args["activation"] = cfg.activation
offset_bins, _ = get_gripper_offset_bins(cfg.gripper_name)
args["offset_bins"] = offset_bins
args["gripper_depth"] = cfg.gripper_depth
args["gripper_name"] = cfg.gripper_name
return cls(**args)
def forward(
self, xyz, mask_feats, confidence, mask_thresh, embedding, gt_masks=None
):
if len(xyz.shape) == 4:
assert xyz.shape[1] == 1
xyz = xyz.squeeze(1)
mask_feats = mask_feats.moveaxis(1, -1) # [B, H, W, mask_dim]
contacts, conf_all, inputs, num_grasps = [], [], [], []
total_grasps, num_objs = 0, 0
for i, (pts, feat, emb, conf_scene) in enumerate(
zip(xyz, mask_feats, embedding, confidence)
):
masks = conf_scene > mask_thresh
if gt_masks is not None:
masks = masks | (gt_masks[i] > 0)
conf_list, num = [], []
for e, m, conf in zip(emb, masks, conf_scene):
f, p, c = feat[m], pts[m], conf[m]
if self.max_num_pred is not None:
perm = torch.randperm(f.shape[0])[: self.max_num_pred]
perm = perm.to(f.device)
f, p, c = f[perm], p[perm], c[perm]
if self.use_embed:
f = torch.cat([f, repeat_new_axis(e, f.shape[0], dim=0)], dim=-1)
contacts.append(p)
inputs.append(f)
conf_list.append(c)
num.append(f.shape[0])
total_grasps += f.shape[0]
conf_all.append(conf_list)
num_grasps.append(num)
num_objs += len(conf_list)
if total_grasps > 0:
contacts = torch.cat(contacts)
inputs = torch.cat(inputs)
else:
contacts = torch.zeros(0, 3).to(xyz.device)
inputs = torch.zeros(0, self.feat_dim).to(xyz.device)
if gt_masks is not None:
if self.use_embed:
embed = torch.stack(
[emb.T @ mask for emb, mask in zip(embedding, gt_masks)]
).transpose(1, 2)
mask_feats = torch.cat([mask_feats, embed], dim=-1)
gt_inputs = torch.cat(
[
feat[(mask > 0).any(dim=0)]
for feat, mask in zip(mask_feats, gt_masks)
]
)
total_gt_grasps = gt_inputs.shape[0]
inputs = torch.cat([inputs, gt_inputs])
contact_dirs = F.normalize(self.contact_dir_head(inputs), dim=-1)
approach_dirs = self.approach_dir_head(inputs)
approach_dirs = F.normalize(
approach_dirs
- contact_dirs * (approach_dirs * contact_dirs).sum(dim=-1, keepdim=True),
dim=-1,
)
offset_logits = self.offset_head(inputs)
offsets_one_hot = F.one_hot(
offset_logits.argmax(dim=-1), self.offset_vals.shape[0]
)
offsets = (
offsets_one_hot.float() @ self.offset_vals.to(inputs.device)
).squeeze(-1)
outputs = {}
if gt_masks is not None:
contact_dirs, outputs["contact_dirs"] = contact_dirs.split(
[total_grasps, total_gt_grasps], dim=0
)
approach_dirs, outputs["approach_dirs"] = approach_dirs.split(
[total_grasps, total_gt_grasps], dim=0
)
offsets = offsets[:total_grasps]
outputs["offsets"] = offset_logits[total_grasps:]
grasps = build_6d_grasp(
contacts,
contact_dirs,
approach_dirs,
offsets,
gripper_depth=get_gripper_depth(self.gripper_name),
gripper_name=self.gripper_name,
)
grasps = double_split(grasps, num_grasps)
contacts = double_split(contacts, num_grasps)
outputs.update(
{
"grasps": grasps,
"grasp_confidence": conf_all,
"grasp_contacts": contacts,
"num_pred_grasps": torch.tensor(
total_grasps / max(num_objs, 1), device=inputs.device
),
}
)
if gt_masks is not None:
outputs["num_gt_grasps"] = torch.tensor(
total_gt_grasps / max(num_objs, 1), device=inputs.device
)
return outputs
================================================
FILE: grasp_gen/models/contact_decoder.py
================================================
# 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.
#
# Author: Wentao Yuan
"""
Transformer network that generate contact masks from scene features.
"""
from typing import List
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from grasp_gen.models.model_utils import (
MLP,
AttentionLayer,
FFNLayer,
PositionEncoding3D,
PositionEncodingOld3D,
get_activation_fn,
repeat_new_axis,
)
def compute_attention_mask(attn_mask, nn_ids, context_size, num_heads):
# resize attention mask to the size of context features
if nn_ids is None:
attn_mask = F.interpolate(attn_mask, context_size)
else:
j = 0
while attn_mask.shape[-1] != context_size:
# BxQxM -> BxQxMxK
attn_mask = repeat_new_axis(attn_mask, nn_ids[j].shape[-1], dim=3)
# BxNxK -> BxQxNxK
idx = repeat_new_axis(nn_ids[j], attn_mask.shape[1], dim=1)
# BxQxMxK -> BxQxNxK
attn_mask = torch.gather(attn_mask, dim=-2, index=idx.long())
# BxQxNxK -> BxQxN
attn_mask = attn_mask.max(dim=-1)[0]
j += 1
# If a BoolTensor is provided as attention mask,
# positions with ``True`` are not allowed to attend.
# We block attention where predicted mask logits < 0.
attn_mask = (attn_mask < 0).bool()
# [B, Q, N] -> [B, h, Q, N] -> [B*h, Q, N]
attn_mask = repeat_new_axis(attn_mask, num_heads, dim=1).flatten(
start_dim=0, end_dim=1
)
# If attn mask is empty for any query, allow attention anywhere.
attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False
return attn_mask
class ContactDecoder(nn.Module):
def __init__(
self,
embed_dim: int,
feedforward_dim: int,
num_grasp_queries: int,
num_place_queries: int,
scene_in_features: List[str],
scene_in_channels: List[int],
mask_feature: str,
mask_dim: int,
place_feature: str,
place_dim: int,
num_layers: int,
num_heads: int,
use_attn_mask: bool,
use_task_embed: bool,
activation: str,
pos_enc: str,
):
"""
Args:
activation: activation function for the feedforward network
embed_dim: transformer feature dimension
feedforward_dim: hidden dimension of the feedforward network
in_channels: feature dimensions of the input feature maps
mask_dim: feature dimension of mask embedding
num_layers: number of transformer decoder layers
num_heads: number of attention heads
num_queries: number of object queries
use_attn_mask: mask attention with downsampled instance mask
predicted by the previous layer
"""
super(ContactDecoder, self).__init__()
self.num_grasp_queries = num_grasp_queries
self.num_place_queries = num_place_queries
# learnable grasp query features
self.query_embed = nn.Embedding(
num_grasp_queries + num_place_queries, embed_dim
)
# learnable query p.e.
self.query_pos_enc = nn.Embedding(
num_grasp_queries + num_place_queries, embed_dim
)
self.place_feature = place_feature
if place_dim != embed_dim and num_place_queries > 0:
self.place_embed_proj = nn.Linear(place_dim, embed_dim)
else:
self.place_embed_proj = nn.Identity()
self.scene_in_features = scene_in_features
self.num_scales = len(scene_in_features)
# context scale embedding
self.scale_embed = nn.Embedding(self.num_scales, embed_dim)
# scene feature projection
self.scene_feature_proj = nn.ModuleList(
[
(
nn.Conv2d(channel, embed_dim, kernel_size=1)
if channel != embed_dim
else nn.Identity()
)
for channel in scene_in_channels
]
)
# context positional encoding
if pos_enc == "old":
self.pe_layer = PositionEncodingOld3D(embed_dim)
else:
self.pe_layer = PositionEncoding3D(embed_dim)
# transformer decoder
self.embed_dim = embed_dim
self.num_heads = num_heads
self.num_layers = num_layers
self.cross_attention_layers = nn.ModuleList()
self.self_attention_layers = nn.ModuleList()
self.ffn_layers = nn.ModuleList()
for _ in range(num_layers):
self.cross_attention_layers.append(AttentionLayer(embed_dim, num_heads))
self.self_attention_layers.append(AttentionLayer(embed_dim, num_heads))
self.ffn_layers.append(FFNLayer(embed_dim, feedforward_dim, activation))
self.use_attn_mask = use_attn_mask
# prediction MLPs
self.mask_feature = mask_feature
self.mask_dim = mask_dim
self.norm = nn.LayerNorm(embed_dim)
num_tasks = 0
if num_grasp_queries > 0:
self.object_head = nn.Linear(embed_dim, 1)
self.grasp_mask_head = MLP(
embed_dim, embed_dim, mask_dim, num_layers=3, activation=activation
)
num_tasks += 1
if num_place_queries > 0:
self.place_mask_head = MLP(
embed_dim, embed_dim, mask_dim, num_layers=3, activation=activation
)
num_tasks += 1
self.use_task_embed = use_task_embed
if use_task_embed:
# learnable task embedding
self.task_embed = nn.Embedding(num_tasks, embed_dim)
@classmethod
def from_config(cls, cfg, scene_channels, obj_channels):
args = {}
args["mask_feature"] = cfg.mask_feature
args["embed_dim"] = cfg.embed_dim
args["feedforward_dim"] = cfg.feedforward_dim
args["scene_in_features"] = cfg.in_features[::-1]
args["scene_in_channels"] = [scene_channels[f] for f in cfg.in_features[::-1]]
args["num_grasp_queries"] = cfg.num_grasp_queries
args["num_place_queries"] = cfg.num_place_queries
args["mask_dim"] = scene_channels[cfg.mask_feature]
args["place_feature"] = cfg.place_feature
if obj_channels is None:
args["place_dim"] = cfg.embed_dim
else:
args["place_dim"] = obj_channels[cfg.place_feature]
args["num_layers"] = cfg.num_layers
args["num_heads"] = cfg.num_heads
args["use_attn_mask"] = cfg.use_attn_mask
args["use_task_embed"] = cfg.use_task_embed
args["activation"] = cfg.activation
args["pos_enc"] = cfg.pos_enc
return cls(**args)
def predict(self, embed, mask_features):
embed = self.norm(embed)
grasp_embed, place_embed = embed.split(
[self.num_grasp_queries, self.num_place_queries]
)
pred, embed, attn_mask = {}, {}, []
if grasp_embed.shape[0] > 0:
embed["grasp"] = grasp_embed.transpose(0, 1)
pred["objectness"] = self.object_head(embed["grasp"]).squeeze(-1)
emb = self.grasp_mask_head(embed["grasp"])
pred["grasping_masks"] = torch.einsum("bqc,bcn->bqn", emb, mask_features)
attn_mask.append(pred["grasping_masks"])
if place_embed.shape[0] > 0:
embed["place"] = place_embed.transpose(0, 1)
emb = self.place_mask_head(embed["place"])
pred["placement_masks"] = torch.einsum("bqc,bcn->bqn", emb, mask_features)
attn_mask.append(pred["placement_masks"])
attn_mask = torch.cat(attn_mask, dim=1).detach()
return pred, embed, attn_mask
def construct_context(self, features, feature_keys, feature_proj):
context = [features["features"][f] for f in feature_keys]
pos_encs, context_sizes = [], []
for i, f in enumerate(feature_keys):
pos_enc = self.pe_layer(features["context_pos"][f])
context_sizes.append(context[i].shape[-1])
pos_enc = pos_enc.flatten(start_dim=2).permute(2, 0, 1)
pos_encs.append(pos_enc)
context[i] = feature_proj[i](context[i].unsqueeze(-1)).squeeze(
-1
) # Project different dim PointNet++ features to embed_dim
context[i] = context[i] + self.scale_embed.weight[i].unsqueeze(1)
# NxCxHW -> HWxNxC
context[i] = context[i].permute(2, 0, 1)
return context, pos_encs, context_sizes
def forward(self, scene_features, obj_features):
"""
Args:
scene_features: a dict containing multi-scale feature maps
from scene point cloud
obj_features: a dict containing multi-scale feature maps
from point cloud of object to be placed
"""
context, pos_encs, context_sizes = self.construct_context(
scene_features, self.scene_in_features, self.scene_feature_proj
)
mask_feat = scene_features["features"][self.mask_feature]
grasp_embed, place_embed = self.query_embed.weight.split(
[self.num_grasp_queries, self.num_place_queries]
)
embed, task_id = [], 0
if grasp_embed.shape[0] > 0:
if self.use_task_embed:
grasp_embed = grasp_embed + self.task_embed.weight[task_id]
embed.append(repeat_new_axis(grasp_embed, mask_feat.shape[0], dim=1))
task_id += 1
if place_embed.shape[0] > 0:
place_prompts = obj_features["features"][self.place_feature]
place_prompts = place_prompts.max(dim=-1)[0]
place_prompts = self.place_embed_proj(place_prompts)
if self.use_task_embed:
place_embed = place_embed + self.task_embed.weight[task_id]
embed.append(place_embed.unsqueeze(1) + place_prompts.unsqueeze(0))
embed = torch.cat(embed)
query_pos_enc = repeat_new_axis(
self.query_pos_enc.weight, mask_feat.shape[0], dim=1
)
# initial prediction with learnable query features only (no context)
prediction, _, attn_mask = self.predict(embed, mask_feat)
predictions = [prediction]
for i in range(self.num_layers):
j = i % self.num_scales
if self.use_attn_mask:
attn_mask = compute_attention_mask(
attn_mask,
scene_features["sample_ids"],
context_sizes[j],
self.num_heads,
)
else:
attn_mask = None
context_feat = context[j]
key_pos_enc = pos_encs[j]
embed = self.cross_attention_layers[i](
embed,
context_feat,
context_feat + key_pos_enc,
query_pos_enc,
key_pos_enc,
attn_mask,
)
embed = self.self_attention_layers[i](
embed, embed, embed + query_pos_enc, query_pos_enc, query_pos_enc
)
embed = self.ffn_layers[i](embed)
prediction, embedding, attn_mask = self.predict(embed, mask_feat)
predictions.append(prediction)
return embedding, predictions
================================================
FILE: grasp_gen/models/criterion.py
================================================
# 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.
#
# Author: Wentao Yuan
"""
Modules for computing ADD-S, mask and parameter losses.
"""
import torch
import torch.distributed as dist
import torch.nn as nn
from torch.nn.functional import binary_cross_entropy_with_logits as bce_loss
from torch.nn.functional import cross_entropy
from grasp_gen.models.model_utils import repeat_new_axis
from grasp_gen.robot import get_gripper_info, load_control_points
def dice_loss(pred: torch.Tensor, target: torch.Tensor, num_objs: torch.Tensor):
"""
Compute the DICE loss, similar to generalized IOU for masks
Args:
pred: A float tensor of arbitrary shape.
Predicted logits for each query matched with a target.
target: A float tensor with the same shape as pred.
Binary label for each element in pred.
num_objs: Average number of objects across the batch.
"""
pred = pred.sigmoid()
numerator = 2 * (pred * target).sum(-1)
denominator = pred.sum(-1) + target.sum(-1)
loss = 1 - (numerator + 1) / (denominator + 1)
return loss.sum() / num_objs
def adds(pred_grasps, confidence, target_grasps, ctr_pts):
"""Compute ADD-S loss
Params:
pred_grasps: [num_pred_grasps, 4, 4]
confidence: [num_pred_grasps]
target_grasps: [num_gt_grasps, 4, 4]
ctr_pts: [4, 5]
"""
# [N, 4, 4] x [4, 5] -> [N, 4, 5] -> [N, 5, 4] -> [N, 5, 3]
pred_pts = (pred_grasps @ ctr_pts).transpose(-2, -1)[..., :3]
gt_pts = (target_grasps @ ctr_pts).transpose(-2, -1)[..., :3]
# [N, 1, 5, 3] - [1, M, 5, 3] -> [N, M, 5, 3] -> [N, M]
dist_1 = (
torch.clip(((pred_pts.unsqueeze(1) - gt_pts.unsqueeze(0)) ** 2).sum(dim=-1), 0)
.sqrt()
.mean(dim=-1)
)
dist_2 = (
torch.clip(
((pred_pts.unsqueeze(1) - gt_pts.flip(1).unsqueeze(0)) ** 2).sum(dim=-1), 0
)
.sqrt()
.mean(dim=-1)
)
dist = torch.minimum(dist_1, dist_2)
pred2gt, _ = dist.min(dim=1)
gt2pred, gt2pred_idx = dist.min(dim=0)
adds_pred2gt = confidence * pred2gt
adds_gt2pred = confidence[gt2pred_idx] * gt2pred
return adds_pred2gt, adds_gt2pred
def average(inputs, num_objs, num_points, mask=False):
if mask:
inputs = (inputs > 0).float()
if num_points is not None:
inputs = [input.mean() for input in inputs.split(num_points)]
if len(inputs) > 0:
avg = torch.stack(inputs).nansum() / num_objs
else:
avg = torch.tensor(0.0).to(num_objs.device)
else:
avg = inputs.mean(dim=-1).sum() / max(num_objs, 1)
return avg
class MaskCriterion(nn.Module):
def __init__(self, loss_weights, top_k):
super(MaskCriterion, self).__init__()
self.loss_weights = loss_weights
self.top_k = top_k
@classmethod
def from_config(cls, cfg):
args = {}
args["loss_weights"] = {"bce": cfg.bce_weight, "dice": cfg.dice_weight}
args["top_k"] = cfg.bce_topk
return cls(**args)
def forward(self, key, out_mask, tgt_mask, loss_mask=None):
# Compute the average number of objects accross all nodes
num_objs = torch.tensor(max(out_mask.shape[0], 1)).to(out_mask.device)
if dist.is_available() and dist.is_initialized():
dist.all_reduce(num_objs)
num_objs = num_objs / dist.get_world_size()
losses = {"dice": dice_loss(out_mask, tgt_mask, num_objs)}
bce = bce_loss(out_mask, tgt_mask, reduction="none")
if loss_mask is not None:
bce = bce * loss_mask
bce_topk, topk_ids = bce.topk(self.top_k, dim=1)
out_mask = out_mask.gather(1, topk_ids)
tgt_mask = tgt_mask.gather(1, topk_ids)
losses["bce"] = torch.div(bce_topk.sum(), num_objs * bce_topk.shape[1])
stats = {
"topk_pred_pos_ratio": (out_mask > 0).float().mean(),
"topk_gt_pos_ratio": (tgt_mask > 0).float().mean(),
"topk_hard_neg_ratio": (((out_mask > 0) | (tgt_mask > 0)).float().mean()),
}
losses = {f"{key}_{k}": (self.loss_weights[k], v) for k, v in losses.items()}
stats = {f"{key}_{k}": v for k, v in stats.items()}
return losses, stats
class SetCriterion(nn.Module):
"""This class computes the Hungarian matching loss.
The process consists of two steps:
1) compute 1-1 assignments between outputs of the model and ground
truth targets (usually, there are more outputs than targets)
2) supervise each matched prediction with the corresponding target
"""
def __init__(
self,
matcher,
deep_supervision,
recompute_indices,
mask_criterion,
object_weight,
not_object_weight,
pseudo_ce_weight,
):
"""Create the criterion.
Parameters:
matcher: module to compute 1-1 matching between targets and outputs
sampler: sample a subset of points to compute mask loss
deep_supervision: whether to supervise intermediate layer outputs
recompute_indices: recompute matching for each intermediate layer
object_weight: weight of the objectness classification loss
not_object_weight: multiplier for the ce loss of unmatched outputs
instance_weights: weights of the instance mask loss
contact_weights: weights of the contact mask loss
pseudo_ce: use cross entropy with pseudo labels from matcher
"""
super(SetCriterion, self).__init__()
self.matcher = matcher
self.deep_supervision = deep_supervision
self.recompute_indices = recompute_indices
self.object_weight = object_weight
self.not_object_weight = not_object_weight
self.mask_criterion = mask_criterion
self.pseudo_ce_weight = pseudo_ce_weight
if pseudo_ce_weight > 0:
self.pseudo_ce_loss = nn.CrossEntropyLoss()
@classmethod
def from_config(cls, cfg, matcher):
args = {}
args["deep_supervision"] = cfg.deep_supervision
args["recompute_indices"] = cfg.recompute_indices
args["object_weight"] = cfg.object_weight
args["not_object_weight"] = cfg.not_object_weight
args["mask_criterion"] = MaskCriterion.from_config(cfg)
args["pseudo_ce_weight"] = cfg.pseudo_ce_weight
return cls(matcher, **args)
def get_pseudo_ce_loss(self, pred_masks, gt_masks, matched_idx):
B, N, H, W = pred_masks.shape
pseudo_label = torch.zeros(B, H, W).long()
pseudo_label = pseudo_label.to(pred_masks.device)
tgt_mask_any = []
for i, (tgt_mask, idx) in enumerate(zip(gt_masks, matched_idx)):
obj_id, y, x = torch.where(tgt_mask > 0)
pseudo_label[i, y, x] = idx[obj_id]
tgt_mask_any.append(tgt_mask.any(dim=0))
tgt_mask_any = torch.stack(tgt_mask_any)
loss = self.pseudo_ce_loss(
pred_masks.permute(0, 2, 3, 1)[tgt_mask_any], pseudo_label[tgt_mask_any]
)
return loss
def get_loss(self, pred, data, matched_idx, layer=None):
obj_label = torch.zeros_like(pred["objectness"])
for i, idx in enumerate(matched_idx):
obj_label[i][idx] = 1
pos_weight = torch.tensor(1 / self.not_object_weight).to(
pred["objectness"].device
)
loss_obj = (
bce_loss(
pred["objectness"], obj_label, pos_weight=pos_weight, reduction="none"
)
* self.not_object_weight
)
mask = data["task_is_pick"].unsqueeze(1).float()
loss_obj = (loss_obj * mask).mean(dim=1).sum()
loss_obj = loss_obj / torch.clamp(mask.sum(), 1)
losses = {"objectness": (self.object_weight, loss_obj)}
if self.pseudo_ce_weight > 0:
pseudo_ce = self.get_pseudo_ce_loss(
pred["grasping_masks"], data["grasping_masks"], matched_idx
)
losses["pseudo_ce"] = (self.pseudo_ce_weight, pseudo_ce)
matched_masks = [
mask[idx] for mask, idx in zip(pred["grasping_masks"], matched_idx)
]
outputs = {"matched_grasping_masks": matched_masks}
mask_loss, stats = self.mask_criterion(
"grasping", torch.cat(matched_masks), torch.cat(data["grasping_masks"])
)
losses.update(mask_loss)
outputs.update(stats)
if layer is not None:
losses = {f"layer{layer}/{key}": val for key, val in losses.items()}
return losses, outputs
def forward(self, pred, targets):
outputs = pred[-1]
# Compute matching between final prediction and the targets
output_idx, cost_matrices = self.matcher(pred[-1], targets)
outputs.update({"matched_idx": output_idx, "cost_matrices": cost_matrices})
# Compute losses for the final layer outputs
losses, stats = self.get_loss(pred[-1], targets, output_idx)
outputs.update(stats)
if self.deep_supervision and self.training:
# Compute losses for each intermediate layer outputs
for i, p in enumerate(pred[:-1]):
if self.recompute_indices:
output_idx, _ = self.matcher(p, targets)
l_dict, _ = self.get_loss(p, targets, output_idx, i + 1)
losses.update(l_dict)
return losses, outputs
class ADDSCriterion(nn.Module):
def __init__(self, pred2gt_weight, gt2pred_weight, adds_per_obj, gripper_name):
super().__init__()
self.gripper_name = gripper_name
self.loss_weights = {
"adds_pred2gt": pred2gt_weight,
"adds_gt2pred": gt2pred_weight,
}
self.adds_per_obj = adds_per_obj
self.control_points = load_control_points(self.gripper_name)
def forward(self, pred_grasps, confidence, gt_grasps, device):
"""Compute ADD-S loss for a batch of scenes
Params:
pred_grasps: a list of lists, each list contains num_objects
tensors of shape [num_pred_grasps, 4, 4]
confidence: a list of of lists, each list contains num_objects
tensors of shape [num_pred_grasps]
gt_grasps: a list of lists, each list contains num_objects
tensors of shape [num_gt_grasps, 4, 4]
"""
adds_pred2gt, adds_gt2pred = [], []
ctr_pts = self.control_points.to(device)
zero = torch.tensor(0.0).to(device)
for pred_grasp, conf, gt_grasp in zip(pred_grasps, confidence, gt_grasps):
if self.adds_per_obj:
for pred, c, gt in zip(pred_grasp, conf, gt_grasp):
if pred.shape[0] == 0 or gt.shape[0] == 0:
continue
pred2gt, gt2pred = adds(pred, c, gt, ctr_pts)
adds_pred2gt.append(pred2gt.mean())
adds_gt2pred.append(gt2pred.mean())
else:
if len(pred_grasp) == 0 or len(gt_grasp) == 0:
continue
num_grasps = [g.shape[0] for g in pred_grasp]
pred_grasp = torch.cat(pred_grasp)
conf = torch.cat(conf)
num_gt_grasps = [g.shape[0] for g in gt_grasp]
gt_grasp = torch.cat(gt_grasp)
if pred_grasp.shape[0] == 0 or gt_grasp.shape[0] == 0:
continue
pred2gt, gt2pred = adds(pred_grasp, conf, gt_grasp, ctr_pts)
pred2gt = [
val.mean() for val in pred2gt.split(num_grasps) if val.shape[0] > 0
]
gt2pred = [
val.mean()
for val in gt2pred.split(num_gt_grasps)
if val.shape[0] > 0
]
adds_pred2gt += pred2gt
adds_gt2pred += gt2pred
losses = {"adds_pred2gt": zero, "adds_gt2pred": zero}
if len(adds_pred2gt) > 0:
losses["adds_pred2gt"] = torch.stack(adds_pred2gt).mean()
if len(adds_gt2pred) > 0:
losses["adds_gt2pred"] = torch.stack(adds_gt2pred).mean()
losses = {key: (self.loss_weights[key], losses[key]) for key in losses}
return losses
class GraspCriterion(nn.Module):
def __init__(
self,
adds_criterion,
contact_dir_weight,
approach_dir_weight,
offset_weight,
bin_weights,
):
super(GraspCriterion, self).__init__()
self.adds_criterion = adds_criterion
self.loss_weights = {
"contact_dirs": contact_dir_weight,
"approach_dirs": approach_dir_weight,
"offset": offset_weight,
}
self.bin_weights = torch.tensor(bin_weights)
@classmethod
def from_config(cls, cfg):
args = {}
args["adds_criterion"] = ADDSCriterion(
cfg.adds_pred2gt, cfg.adds_gt2pred, cfg.adds_per_obj, cfg.gripper_name
)
args["contact_dir_weight"] = cfg.contact_dir
args["approach_dir_weight"] = cfg.approach_dir
gripper_info = get_gripper_info(cfg.gripper_name)
args["offset_weight"] = (
cfg.offset
) # NOTE: This is for the actual weight in the overall loss, not the bin weights
args["bin_weights"] = gripper_info.offset_bin_weights
return cls(**args)
def forward(self, pred, data):
losses = {}
for key in ["contact_dirs", "approach_dirs"]:
if self.loss_weights[key] == 0:
continue
losses[key] = 1 - (pred[key] * data[key]).sum(dim=1)
losses[key] = losses[key].sum() / max(losses[key].numel(), 1)
losses["offset"] = cross_entropy(
pred["offsets"],
data["offsets"],
self.bin_weights.to(pred["offsets"].device),
reduction="none",
)
losses["offset"] = losses["offset"].sum() / max(losses["offset"].numel(), 1)
losses = {key: (self.loss_weights[key], losses[key]) for key in losses}
losses.update(
self.adds_criterion(
pred["grasps"],
pred["grasp_confidence"],
data["grasps"],
data["inputs"].device,
)
)
return losses
class PlaceCriterion(nn.Module):
def __init__(self, mask_criterion, deep_supervision):
super(PlaceCriterion, self).__init__()
self.mask_criterion = mask_criterion
self.deep_supervision = deep_supervision
@classmethod
def from_config(cls, cfg):
args = {}
args["mask_criterion"] = MaskCriterion.from_config(cfg)
args["deep_supervision"] = cfg.deep_supervision
return cls(**args)
def forward(self, pred, data):
pred_masks = pred[-1]["placement_masks"][data["task_is_place"]]
target_masks = data["placement_masks"][data["task_is_place"]]
num_pred = (pred_masks > 0).sum()
num_gt = (target_masks > 0).sum()
num_scene = pred_masks.shape[0]
loss_masks = data["placement_region"][data["task_is_place"]]
loss_masks = repeat_new_axis(
loss_masks, target_masks.shape[1], dim=1
) # (B, H, W) -> (B, Q, H, W)
loss_masks = loss_masks.flatten(0, 1)
target_masks = target_masks.flatten(0, 1)
pred_masks = pred_masks.flatten(0, 1)
losses, stats = self.mask_criterion(
"placement", pred_masks, target_masks, loss_masks
)
stats.update(
{
"num_pred_placements": num_pred / max(num_scene, 1),
"num_gt_placements": num_gt / max(num_scene, 1),
}
)
if self.deep_supervision and self.training:
# Compute losses for each intermediate layer outputs
for i, p in enumerate(pred[:-1]):
pred_masks = p["placement_masks"][data["task_is_place"]]
pred_masks = pred_masks.flatten(0, 1)
mask_losses, _ = self.mask_criterion(
"placement", pred_masks, target_masks, loss_masks
)
mask_losses = {
f"layer{i+1}/{key}": val for key, val in mask_losses.items()
}
losses.update(mask_losses)
return losses, stats
================================================
FILE: grasp_gen/models/discriminator.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from grasp_gen.dataset.dataset import MAPPING_ID2NAME
from grasp_gen.utils.math_utils import matrix_to_rt
from grasp_gen.models.model_utils import (
PointNetPlusPlus,
SinusoidalPosEmb,
convert_to_ptv3_pc_format,
load_pretrained_checkpoint_to_dict,
offset2batch,
)
from grasp_gen.models.ptv3.ptv3 import PointTransformerV3
from grasp_gen.robot import get_gripper_info
from grasp_gen.utils.logging_config import get_logger
from grasp_gen.models.model_utils import load_pretrained_checkpoint_to_dict
logger = get_logger(__name__)
class GraspGenDiscriminator(nn.Module):
"""Neural network module for discriminating between good and bad grasps.
This class implements a discriminator that evaluates the quality of generated grasps
based on object geometry and grasp pose.
Args:
num_obs_dim (int): Dimension of observation features. Default: 512
obs_backbone (str): Type of observation encoder backbone. Default: 'vit'
grasp_repr (str): Grasp representation type. Default: 'r3_6d'
grid_size (float): Grid size for point cloud processing. Default: 0.01
sample_embed_dim (int): Dimension for grasp embeddings. Default: 512
pose_repr (str): Type of pose representation. Default: False
topk_ratio (float): Ratio of top grasps to consider. Default: 0.40
checkpoint_object_encoder_pretrained (str): Path to pretrained encoder. Default: None
kappa (float): Scale factor for noise. See calculate_dataset_kappa function in dataset.py for more details. Compute for each grasp dataset.
"""
def __init__(
self,
num_obs_dim: int = 512,
obs_backbone: str = "vit",
grasp_repr: str = "r3_6d",
grid_size: float = 0.01,
sample_embed_dim: int = 512,
pose_repr: str = False,
topk_ratio: float = 0.40,
checkpoint_object_encoder_pretrained: str = None,
kappa: float = 3.30,
gripper_name: str = "franka_panda",
):
super().__init__()
self.num_obs_dim = num_obs_dim
self.obs_backbone = obs_backbone
self.grasp_repr = grasp_repr
self.grid_size = grid_size
self.pose_repr = pose_repr
self.topk_ratio = topk_ratio
self.checkpoint_object_encoder_pretrained = checkpoint_object_encoder_pretrained
self.kappa = None if kappa <= 0 else kappa
self.gripper_name = gripper_name
if self.grasp_repr == "r3_6d":
self.output_dim = 9
elif self.grasp_repr in ["r3_so3", "r3_euler"]:
self.output_dim = 6
else:
raise NotImplementedError(
f"Rotation representation {grasp_repr} is not implemented!"
)
if self.obs_backbone == "pointnet":
self.object_encoder = PointNetPlusPlus(
output_embedding_dim=self.num_obs_dim,
feature_dim=1 if self.pose_repr == "pc_feature" else -1,
)
elif self.obs_backbone == "ptv3":
self.object_encoder = PointTransformerV3(
in_channels=3,
enable_flash=False,
cls_mode=True,
)
else:
raise NotImplementedError()
gripper_info = get_gripper_info(self.gripper_name)
self.ctr_pts = gripper_info.control_points
logger.info(f"Pose representation is {self.pose_repr}")
total_input_dim = sample_embed_dim + num_obs_dim
if self.pose_repr == "mlp":
self.sample_encoder = nn.Sequential(
nn.Linear(self.output_dim, sample_embed_dim),
nn.ReLU(),
nn.Linear(sample_embed_dim, sample_embed_dim),
)
elif self.pose_repr == "grasp_cloud":
num_input_dim = 3 * self.ctr_pts.shape[1]
self.sample_encoder = nn.Sequential(
nn.Linear(num_input_dim, sample_embed_dim),
nn.ReLU(),
nn.Linear(sample_embed_dim, sample_embed_dim),
)
elif self.pose_repr == "grasp_cloud_pe":
num_input_dim = 3 * self.ctr_pts.shape[1]
num_embed_dim_per_dim = int(sample_embed_dim / num_input_dim)
self.sample_encoder = nn.Sequential(
SinusoidalPosEmb(num_embed_dim_per_dim),
nn.Linear(num_embed_dim_per_dim * num_input_dim, sample_embed_dim),
nn.ReLU(),
nn.Linear(sample_embed_dim, sample_embed_dim),
nn.ReLU(),
nn.Linear(sample_embed_dim, sample_embed_dim),
)
elif self.pose_repr == "pe":
num_embed_dim_per_dim = int(sample_embed_dim / self.output_dim)
self.sample_encoder = nn.Sequential(
SinusoidalPosEmb(num_embed_dim_per_dim),
nn.Linear(num_embed_dim_per_dim * self.output_dim, sample_embed_dim),
nn.ReLU(),
nn.Linear(sample_embed_dim, sample_embed_dim),
nn.ReLU(),
nn.Linear(sample_embed_dim, sample_embed_dim),
)
elif self.pose_repr == "pc_feature":
total_input_dim = num_obs_dim
else:
raise NotImplementedError(
f"Pose input representation {self.pose_repr} is not implemented"
)
self.prediction_head = nn.Sequential(
nn.Linear(total_input_dim, total_input_dim // 2),
nn.ReLU(),
nn.Linear(total_input_dim // 2, total_input_dim // 4),
nn.ReLU(),
nn.Linear(total_input_dim // 4, 1),
)
if self.checkpoint_object_encoder_pretrained is not None:
if os.path.exists(self.checkpoint_object_encoder_pretrained):
model_state_dict_object_encoder = load_pretrained_checkpoint_to_dict(
self.checkpoint_object_encoder_pretrained, "object_encoder"
)
self.object_encoder.load_state_dict(model_state_dict_object_encoder)
for param in self.object_encoder.parameters():
param.requires_grad = False
logger.info("Using pretrained object encoder!")
else:
logger.info(
f"Object encoder checkpoints not found at location {self.checkpoint_object_encoder_pretrained}"
)
@classmethod
def from_config(cls, cfg):
"""Creates a GraspGenDiscriminator instance from a configuration object.
Args:
cfg: Configuration object containing model parameters
Returns:
GraspGenDiscriminator: Instantiated model
"""
args = {
"num_obs_dim": cfg.num_obs_dim,
"obs_backbone": cfg.obs_backbone,
"grasp_repr": cfg.grasp_repr,
"grid_size": cfg.ptv3.grid_size,
"sample_embed_dim": cfg.num_obs_dim,
"pose_repr": cfg.pose_repr,
"topk_ratio": cfg.topk_ratio,
"checkpoint_object_encoder_pretrained": cfg.checkpoint_object_encoder_pretrained,
"kappa": cfg.kappa,
"gripper_name": cfg.gripper_name,
}
return cls(**args)
def forward(self, data, cfg=None, eval=False):
"""Forward pass of the discriminator.
Args:
data: Input data dictionary containing point clouds and grasps
cfg: Optional configuration object
eval (bool): Whether to run in evaluation mode
Returns:
tuple: (outputs, losses, stats) containing grasp scores, losses and metrics
"""
device = data["points"].device
if "grasp_key" not in data:
grasp_key = "grasps"
else:
grasp_key = data["grasp_key"]
grasps = data[grasp_key]
num_objects_in_batch = len(data["points"])
num_grasps_per_batch = data[grasp_key][0].shape[0]
batch_size = num_objects_in_batch * num_grasps_per_batch
depth = data["points"]
num_points = depth.shape[-2]
depth = depth.reshape([-1, num_points, 3])
if type(grasps) == list:
grasps = torch.cat(grasps)
grasps = grasps.reshape([-1, 4, 4])
if self.kappa is not None:
depth = self.kappa * depth
if self.obs_backbone == "ptv3":
depth = convert_to_ptv3_pc_format(depth, grid_size=self.grid_size)
grasps_input = matrix_to_rt(grasps, self.grasp_repr, kappa=self.kappa)
offset = (
torch.tensor([num_grasps_per_batch])
.repeat(num_objects_in_batch)
.cumsum(dim=0)
.to(device)
)
mask_batch = offset2batch(offset)
if self.pose_repr in ["grasp_cloud", "grasp_cloud_pe", "pc_feature"]:
ctrl_pts = self.ctr_pts.to(device=device)
grasp_pc = (grasps @ ctrl_pts).transpose(-2, -1)[..., :3]
grasps_input = grasp_pc.reshape([batch_size, -1])
if self.pose_repr == "pc_feature":
depth_full = depth[mask_batch]
depth_full = torch.cat([depth_full, grasp_pc], dim=1)
pc_feature = torch.cat(
[
torch.zeros(
[num_grasps_per_batch * num_objects_in_batch, num_points, 1]
),
torch.ones(
[
num_grasps_per_batch * num_objects_in_batch,
grasp_pc.shape[1],
1,
]
),
],
dim=1,
).to(device=device)
total_embedding = torch.cat([depth_full, pc_feature], dim=-1)
total_embedding = self.object_encoder(total_embedding)
else:
sample_embedding = self.sample_encoder(grasps_input)
object_embedding = self.object_encoder(
depth
) # object_embedding size is [num_objects_in_batch, self.num_obs_dim]
object_embedding = object_embedding[
mask_batch
] # Redistribute object embeddings to full batch, result is [batch_size, self.num_obs_dim]
total_embedding = torch.cat([sample_embedding, object_embedding], axis=-1)
logits = self.prediction_head(total_embedding)
losses, outputs, stats = {}, {}, {}
outputs["logits"] = logits.reshape(
[num_objects_in_batch, num_grasps_per_batch, 1]
)
outputs["grasp_confidence"] = outputs["logits"].sigmoid()
if "labels" in data:
labels = data["labels"]
if type(labels) == list:
labels = torch.cat(labels)
bce_loss = F.binary_cross_entropy_with_logits(
input=logits, target=labels, reduction="none"
)
ratio_topk = self.topk_ratio
num_top_k = int(batch_size * ratio_topk)
bce_topk, mask = bce_loss.topk(num_top_k, dim=0)
losses["bce_topk"] = (1.0, bce_topk.mean())
pred = logits.sigmoid()
from sklearn.metrics import average_precision_score
labels = labels.squeeze(1).cpu().numpy()
score = pred.squeeze(1).detach().cpu().numpy()
ap = average_precision_score(labels, score)
stats["ap"] = torch.tensor(ap).to(device)
if "grasp_ids" in data:
grasp_ids = data["grasp_ids"]
if type(grasp_ids) == list:
grasp_ids = torch.cat(grasp_ids)
grasp_ids = grasp_ids.cpu().squeeze(1).numpy()
mask = mask.cpu().numpy()
grasp_ids_topk = grasp_ids[mask]
for grasp_id, grasp_name in MAPPING_ID2NAME.items():
mask = grasp_ids_topk == grasp_id
val = mask.sum() / num_top_k
key = f"topk_ratio_{grasp_name}"
stats[key] = torch.tensor(val).to(device)
mask = grasp_ids == grasp_id
# stats[f"ap_{grasp_name}"] = average_precision_score(labels[mask], score[mask])
key = f"loss_{grasp_name}"
val = bce_loss[mask].detach().cpu().numpy().mean()
stats[key] = torch.tensor(val).to(device)
return outputs, losses, stats
def infer(self, data):
"""Inference method for evaluating grasps.
Args:
data: Input data dictionary containing point clouds and grasps
Returns:
tuple: (outputs, losses, stats) containing grasp scores and metrics
"""
outputs, losses, stats = self.forward(data)
data.update(outputs)
return data, losses, stats
================================================
FILE: grasp_gen/models/generator.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
import os
import time
import numpy as np
import torch
import torch.nn as nn
from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
from scipy.spatial import KDTree
from grasp_gen.utils.math_utils import matrix_to_rt, rt_to_matrix
from grasp_gen.metrics import compute_metrics_given_two_sets_of_poses, compute_recall
from grasp_gen.models.model_utils import (
PointNetPlusPlus,
SinusoidalPosEmb,
compute_grasp_loss,
convert_to_ptv3_pc_format,
load_pretrained_checkpoint_to_dict,
offset2batch,
)
from grasp_gen.models.ptv3.ptv3 import PointTransformerV3
from grasp_gen.robot import get_gripper_info
from grasp_gen.utils.logging_config import get_logger
logger = get_logger(__name__)
class GraspGenGenerator(nn.Module):
"""GraspGen generator model for generating 6-DOF robotic grasps.
This class implements a diffusion model that generates robotic grasping poses from point cloud observations.
It uses a combination of object encoding and diffusion-based denoising to generate high-quality grasps.
Args:
num_embed_dim (int): Dimension of embedding vectors. Default: 256
num_obs_dim (int): Dimension of observation features. Default: 512
diffusion_embed_dim (int): Dimension of diffusion step embeddings. Default: 512
image_size (int): Size of input images if using vision backbone. Default: 256
num_diffusion_iters (int): Number of diffusion steps for training. Default: 100
num_diffusion_iters_eval (int): Number of diffusion steps for evaluation. Default: 100
obs_backbone (str): Type of observation encoder backbone ('vit', 'pointnet', 'ptv3'). Default: 'vit'
compositional_schedular (bool): Whether to use separate schedulers for position and rotation. Default: False
loss_pointmatching (bool): Whether to use point matching loss. Default: True
loss_l1_pos (bool): Whether to use L1 loss on positions. Default: False
loss_l1_rot (bool): Whether to use L1 loss on rotations. Default: False
grasp_repr (str): Grasp representation type ('r3_6d', 'r3_so3', 'r3_euler'). Default: 'r3_6d'
kappa (float): Scale factor for noise. Default: -1.0
clip_sample (bool): Whether to clip samples in diffusion process. Default: True
beta_schedule (str): Schedule for noise variance. Default: 'beta_schedule'
attention (str): Type of attention mechanism. Default: 'cat'
grid_size (float): Grid size for point cloud processing. Default: 0.02
gripper_name (str): Name of the gripper model. Default: 'franka_panda'
pose_repr (str): Type of pose representation. Default: 'mlp'
num_grasps_per_object (int): Number of grasps to generate per object. Default: 20
checkpoint_object_encoder_pretrained (str): Path to pretrained object encoder. Default: None
"""
def __init__(
self,
num_embed_dim: int = 256,
num_obs_dim: int = 512,
diffusion_embed_dim: int = 512,
image_size: int = 256,
num_diffusion_iters: int = 100,
num_diffusion_iters_eval: int = 100,
obs_backbone: str = "pointnet",
compositional_schedular: bool = False,
loss_pointmatching: bool = True,
loss_l1_pos: bool = False,
loss_l1_rot: bool = False,
grasp_repr: str = "r3_6d",
kappa: float = -1.0,
clip_sample: bool = True,
beta_schedule: str = "beta_schedule",
attention: str = "cat",
grid_size: float = 0.02,
gripper_name: str = "franka_panda",
pose_repr: str = "mlp",
num_grasps_per_object: int = 20,
checkpoint_object_encoder_pretrained: str = None,
):
super().__init__()
self.num_embed_dim = num_embed_dim
self.num_obs_dim = num_obs_dim
self.diffusion_embed_dim = diffusion_embed_dim
self.image_size = image_size
self.num_diffusion_iters = num_diffusion_iters
self.num_diffusion_iters_eval = num_diffusion_iters_eval
self.obs_backbone = obs_backbone
self.compositional_schedular = compositional_schedular
self.loss_pointmatching = loss_pointmatching
self.loss_l1_pos = loss_l1_pos
self.loss_l1_rot = loss_l1_rot
self.grasp_repr = grasp_repr
self.kappa = None if kappa <= 0 else kappa
self.clip_sample = clip_sample
self.beta_schedule = beta_schedule
self.attention = attention
self.grid_size = grid_size
self.gripper_name = gripper_name
self.pose_repr = pose_repr
self.num_grasps_per_object = num_grasps_per_object
self.checkpoint_object_encoder_pretrained = checkpoint_object_encoder_pretrained
if self.grasp_repr == "r3_6d":
self.output_dim = 9
elif self.grasp_repr in ["r3_so3", "r3_euler"]:
self.output_dim = 6
else:
raise NotImplementedError(
f"Rotation representation {grasp_repr} is not implemented!"
)
if obs_backbone == "vit":
from grasp_gen.models.vit import VisionTransformer
self.object_encoder = VisionTransformer(
img_size=self.image_size,
embed_dim=self.num_embed_dim,
num_classes=self.num_obs_dim,
patch_size=64,
depth=12,
num_heads=8,
)
elif obs_backbone == "pointnet":
self.object_encoder = PointNetPlusPlus(
output_embedding_dim=self.num_obs_dim,
feature_dim=1 if self.pose_repr == "pc_feature" else -1,
)
elif obs_backbone == "ptv3":
self.object_encoder = PointTransformerV3(
in_channels=3,
enable_flash=False,
cls_mode=True,
)
else:
raise NotImplementedError()
self.diffusion_head = DiffusionNoisePredictionNet(
diffusion_step_embed_dim=self.diffusion_embed_dim,
observation_embed_dim=self.num_obs_dim,
sample_embed_dim=self.diffusion_embed_dim,
sample_dim=self.output_dim,
attention=self.attention,
moreparams=False,
pose_repr=self.pose_repr,
)
if self.compositional_schedular:
self.noise_scheduler_pos = DDPMScheduler(
num_train_timesteps=self.num_diffusion_iters,
beta_schedule="scaled_linear",
clip_sample=True,
prediction_type="epsilon",
)
self.noise_scheduler_rot = DDPMScheduler(
num_train_timesteps=self.num_diffusion_iters,
beta_schedule="squaredcos_cap_v2",
clip_sample=True,
prediction_type="epsilon",
)
else:
logger.info(
f"DDPM parameters, num_diffusion_iters: {self.num_diffusion_iters}, beta_schedule: {self.beta_schedule}, clip_sample: {self.clip_sample}"
)
self.noise_scheduler = DDPMScheduler(
num_train_timesteps=self.num_diffusion_iters,
beta_schedule=self.beta_schedule, # TODO: Check this
clip_sample=self.clip_sample,
prediction_type="epsilon",
)
self.gripper_info = get_gripper_info(self.gripper_name)
self.gripper_mesh = self.gripper_info.collision_mesh
self.ctr_pts = self.gripper_info.control_points
if self.checkpoint_object_encoder_pretrained is not None:
if os.path.exists(self.checkpoint_object_encoder_pretrained):
model_state_dict_object_encoder = load_pretrained_checkpoint_to_dict(
self.checkpoint_object_encoder_pretrained, "object_encoder"
)
self.object_encoder.load_state_dict(model_state_dict_object_encoder)
for param in self.object_encoder.parameters():
param.requires_grad = False
logger.info("Using pretrained object encoder!")
else:
logger.info(
f"Object encoder checkpoints not found at location {self.checkpoint_object_encoder_pretrained}"
)
@classmethod
def from_config(cls, cfg):
"""Creates a GraspGenGenerator instance from a configuration object.
Args:
cfg: Configuration object containing model parameters
Returns:
GraspGenGenerator: Instantiated model
"""
args = {
"num_embed_dim": cfg.num_embed_dim,
"num_obs_dim": cfg.num_obs_dim,
"diffusion_embed_dim": cfg.diffusion_embed_dim,
"image_size": cfg.image_size,
"num_diffusion_iters": cfg.num_diffusion_iters,
"num_diffusion_iters_eval": cfg.num_diffusion_iters_eval,
"obs_backbone": cfg.obs_backbone,
"compositional_schedular": cfg.compositional_schedular,
"loss_pointmatching": cfg.loss_pointmatching,
"loss_l1_pos": cfg.loss_l1_pos,
"loss_l1_rot": cfg.loss_l1_rot,
"grasp_repr": cfg.grasp_repr,
"kappa": cfg.kappa,
"clip_sample": cfg.clip_sample,
"beta_schedule": cfg.beta_schedule,
"attention": cfg.attention,
"grid_size": cfg.ptv3.grid_size,
"gripper_name": cfg.gripper_name,
"pose_repr": cfg.pose_repr,
"num_grasps_per_object": cfg.num_grasps_per_object,
"checkpoint_object_encoder_pretrained": cfg.checkpoint_object_encoder_pretrained,
}
return cls(**args)
def forward(self, data, cfg=None, eval=False):
"""Forward pass of the model.
Args:
data: Input data dictionary containing point clouds and optionally ground truth grasps
cfg: Optional configuration object
eval (bool): Whether to run in evaluation mode
Returns:
tuple: (outputs, losses, stats) containing model predictions, losses and metrics
"""
if eval:
return self.forward_inference(data, return_metrics=True)
else:
return self.forward_train(data)
def infer(self, data, return_metrics=False):
"""Inference method for generating grasps.
Args:
data: Input data dictionary containing point clouds
return_metrics (bool): Whether to compute and return evaluation metrics
Returns:
tuple: (outputs, losses, stats) containing generated grasps and optional metrics
"""
return self.forward_inference(data, return_metrics=return_metrics)
def forward_train(self, data):
"""Training forward pass implementing the diffusion process.
Args:
data: Input data dictionary containing point clouds and ground truth grasps
Returns:
tuple: (outputs, losses, stats) containing predictions, training losses and metrics
"""
device = data["points"].device
num_objects_in_batch = len(data["points"])
num_grasps_per_batch = data["grasps"][0].shape[0]
batch_size = num_objects_in_batch * num_grasps_per_batch
depth = data["points"]
grasps = data["grasps"]
num_points = depth.shape[-2]
depth = depth.reshape([-1, num_points, 3])
grasps_init_size = [num_objects_in_batch, num_grasps_per_batch, 4, 4]
if type(grasps) == list:
grasps = torch.cat(grasps)
grasps = grasps.reshape([-1, 4, 4])
if self.kappa is not None:
depth = self.kappa * depth
if self.obs_backbone == "ptv3":
depth = convert_to_ptv3_pc_format(depth, grid_size=self.grid_size)
grasps_gt = matrix_to_rt(grasps, self.grasp_repr, kappa=self.kappa)
noise = torch.randn([batch_size, self.output_dim], device=device).float()
timesteps = torch.randint(
0, self.num_diffusion_iters, (batch_size,), device=device
).long()
offset = (
torch.tensor([num_grasps_per_batch])
.repeat(num_objects_in_batch)
.cumsum(dim=0)
.to(device)
)
mask_batch = offset2batch(offset)
if self.pose_repr in ["grasp_cloud", "grasp_cloud_pe", "pc_feature"]:
ctrl_pts = self.ctr_pts.to(device=device)
grasp_pc = (grasps @ ctrl_pts).transpose(-2, -1)[..., :3]
if self.pose_repr == "pc_feature":
depth_full = depth[mask_batch]
depth_full = torch.cat([depth_full, grasp_pc], dim=1)
pc_feature = torch.cat(
[
torch.zeros(
[num_grasps_per_batch * num_objects_in_batch, num_points, 1]
),
torch.ones(
[
num_grasps_per_batch * num_objects_in_batch,
grasp_pc.shape[1],
1,
]
),
],
dim=1,
).to(device=device)
object_embedding = torch.cat([depth_full, pc_feature], dim=-1)
object_embedding = self.object_encoder(object_embedding)
elif self.pose_repr == "mlp":
object_embedding = self.object_encoder(
depth
) # object_embedding size is [num_objects_in_batch, self.num_obs_dim]
object_embedding = object_embedding[
mask_batch
] # Redistribute object embeddings to full batch, result is [batch_size, self.num_obs_dim]
else:
raise NotImplementedError(f"Pose repr {self.pose_repr} not implemented!")
if self.compositional_schedular:
noisy_grasps_pos = self.noise_scheduler_pos.add_noise(
grasps_gt[..., :3], noise[..., :3], timesteps
)
noisy_grasps_rot = self.noise_scheduler_rot.add_noise(
grasps_gt[..., 3 : self.output_dim],
noise[..., 3 : self.output_dim],
timesteps,
)
noisy_grasps = torch.hstack([noisy_grasps_pos, noisy_grasps_rot])
else:
noisy_grasps = self.noise_scheduler.add_noise(grasps_gt, noise, timesteps)
samples = noisy_grasps if self.pose_repr == "mlp" else None
noise_pred = self.diffusion_head(object_embedding, timesteps, samples)
pred_noise_pts_mat = rt_to_matrix(noise_pred, self.grasp_repr, self.kappa)
actual_noise_pts_mat = rt_to_matrix(noise, self.grasp_repr, self.kappa)
noisy_grasps_mat = rt_to_matrix(noisy_grasps, self.grasp_repr, self.kappa)
grasps_gt_mat = rt_to_matrix(grasps_gt, self.grasp_repr, self.kappa)
stats = compute_metrics_given_two_sets_of_poses(
actual_noise_pts_mat, pred_noise_pts_mat, self.gripper_info
)
losses = {}
if self.loss_pointmatching:
point_matching_loss = compute_grasp_loss(
actual_noise_pts_mat, pred_noise_pts_mat, self.ctr_pts
)
losses["noise_pred"] = (2.0, point_matching_loss)
if self.loss_l1_pos:
position_loss = torch.linalg.norm(
noise[..., :3] - noise_pred[..., :3], dim=-1
)
position_loss = torch.mean(position_loss) # across the batch
losses["position_loss"] = (1.0, position_loss)
if self.loss_l1_rot:
rotation_loss = torch.linalg.norm(
noise[..., 3 : self.output_dim] - noise_pred[..., 3 : self.output_dim],
dim=-1,
)
rotation_loss = torch.mean(rotation_loss)
losses["rotation_loss"] = (1.0, rotation_loss)
outputs = {}
outputs["actual_noise_pts_mat"] = actual_noise_pts_mat.reshape(grasps_init_size)
outputs["pred_noise_pts_mat"] = pred_noise_pts_mat.reshape(grasps_init_size)
outputs["noisy_grasps_mat"] = noisy_grasps_mat.reshape(grasps_init_size)
outputs["grasps_gt_mat"] = grasps_gt_mat.reshape(grasps_init_size)
return outputs, losses, stats
def forward_inference(self, data, return_metrics=False):
"""Inference forward pass implementing the reverse diffusion process.
Args:
data: Input data dictionary containing point clouds
return_metrics (bool): Whether to compute evaluation metrics
Returns:
tuple: (outputs, losses, stats) containing generated grasps and optional metrics
"""
device = data["points"].device
num_objects_in_batch = len(data["points"])
if "grasps" in data:
if type(data["grasps"][0]) == list:
data["grasps"][0] = np.array(data["grasps"][0])
num_grasps_per_batch = data["grasps"][0].shape[0]
else:
num_grasps_per_batch = self.num_grasps_per_object
return_metrics = False
batch_size = data["points"].shape[0] * num_grasps_per_batch
depth = data["points"]
num_points = depth.shape[-2]
depth = depth.reshape([-1, num_points, 3])
depth = depth.to(device)
grasps_init_size = [num_objects_in_batch, num_grasps_per_batch, 4, 4]
if self.kappa is not None:
depth = self.kappa * depth
if self.obs_backbone == "ptv3":
depth = convert_to_ptv3_pc_format(depth, grid_size=self.grid_size)
# num_grasps_per_batch = data['grasps'].shape[1]
grasps_per_iteration = torch.zeros(
[
num_objects_in_batch,
self.num_diffusion_iters_eval,
num_grasps_per_batch,
4,
4,
]
)
with torch.no_grad():
noisy_init = torch.randn([batch_size, self.output_dim], device=device)
noisy_grasps = noisy_init
# Initialize likelihood scores
likelihood = torch.zeros((batch_size, 1), device=device)
offset = (
torch.tensor([num_grasps_per_batch])
.repeat(num_objects_in_batch)
.cumsum(dim=0)
.to(device)
)
mask_batch = offset2batch(offset)
if self.pose_repr == "mlp":
object_embedding = self.object_encoder(
depth
) # object_embedding size is [num_objects_in_batch, self.num_obs_dim]
object_embedding = object_embedding[
mask_batch
] # Redistribute object embeddings to full batch, result is [batch_size, self.num_obs_dim]
if self.compositional_schedular:
self.noise_scheduler_pos.set_timesteps(self.num_diffusion_iters_eval)
timesteps = self.noise_scheduler_pos.timesteps
self.noise_scheduler_rot.set_timesteps(self.num_diffusion_iters_eval)
else:
self.noise_scheduler.set_timesteps(self.num_diffusion_iters_eval)
timesteps = self.noise_scheduler.timesteps
for k in timesteps:
samples = noisy_grasps if self.pose_repr == "mlp" else None
if self.pose_repr in ["grasp_cloud", "grasp_cloud_pe", "pc_feature"]:
ctrl_pts = self.ctr_pts.to(device=device)
noisy_grasps_mat = rt_to_matrix(
noisy_grasps, self.grasp_repr, self.kappa
)
grasp_pc = (noisy_grasps_mat @ ctrl_pts).transpose(-2, -1)[..., :3]
if self.pose_repr == "pc_feature":
depth_full = depth[mask_batch]
depth_full = torch.cat([depth_full, grasp_pc], dim=1)
pc_feature = torch.cat(
[
torch.zeros(
[
num_grasps_per_batch * num_objects_in_batch,
num_points,
1,
]
),
torch.ones(
[
num_grasps_per_batch * num_objects_in_batch,
grasp_pc.shape[1],
1,
]
),
],
dim=1,
).to(device=device)
object_embedding = torch.cat([depth_full, pc_feature], dim=-1)
object_embedding = self.object_encoder(object_embedding)
# Forward: Predict noise
noise_pred = self.diffusion_head(object_embedding, k, samples)
if self.compositional_schedular:
# Handle compositional case
res_pos = self.noise_scheduler_pos.step(
model_output=noise_pred[..., :3],
timestep=k,
sample=noisy_grasps[..., :3],
)
res_rot = self.noise_scheduler_rot.step(
model_output=noise_pred[..., 3 : self.output_dim],
timestep=k,
sample=noisy_grasps[..., 3 : self.output_dim],
)
# Compute likelihood contributions
if k > 0: # Skip first step
alpha_pos = self.noise_scheduler_pos.alphas[k]
beta_pos = self.noise_scheduler_pos.betas[k]
var_pos = beta_pos
likelihood_pos = (
torch.distributions.Normal(
res_pos.pred_original_sample,
torch.sqrt(torch.tensor(var_pos, device=device)),
)
.log_prob(noisy_grasps[..., :3])
.sum(-1, keepdim=True)
)
alpha_rot = self.noise_scheduler_rot.alphas[k]
beta_rot = self.noise_scheduler_rot.betas[k]
var_rot = beta_rot
likelihood_rot = (
torch.distributions.Normal(
res_rot.pred_original_sample,
torch.sqrt(torch.tensor(var_rot, device=device)),
)
.log_prob(noisy_grasps[..., 3 : self.output_dim])
.sum(-1, keepdim=True)
)
likelihood += likelihood_pos + likelihood_rot
noisy_grasps = torch.hstack(
[res_pos.prev_sample, res_rot.prev_sample]
)
else:
# Handle standard case
res = self.noise_scheduler.step(
model_output=noise_pred, timestep=k, sample=noisy_grasps
)
# Compute likelihood contribution
if k > 0: # Skip first step
alpha = self.noise_scheduler.alphas[k]
beta = self.noise_scheduler.betas[k]
var = beta
likelihood += (
torch.distributions.Normal(
res.pred_original_sample,
torch.sqrt(torch.tensor(var, device=device)),
)
.log_prob(noisy_grasps)
.sum(-1, keepdim=True)
)
noisy_grasps = res.prev_sample
pred_grasps = rt_to_matrix(noisy_grasps, self.grasp_repr, self.kappa)
grasps_pred = pred_grasps.reshape(grasps_init_size)
grasps_per_iteration[:, k, :, ::] = grasps_pred
grasps_pred = pred_grasps.reshape(grasps_init_size)
grasps_pred[:, :, 3, 3] = 1 # To make proper homogeneous matrix
stats_batch = []
if return_metrics:
all_stats = []
for i in range(num_objects_in_batch):
grasps_pred_i = grasps_pred[i].cpu().numpy()
grasps_gt_i = data["grasps_highres"][i].cpu().numpy()
tree = KDTree(grasps_gt_i[:, :3, 3])
dist, idx = tree.query(grasps_pred_i[:, :3, 3])
matched = dist < 4.0
idx = idx[matched]
grasps_pred_matched = grasps_pred_i[matched]
grasps_gt_for_pred = grasps_gt_i[idx]
grasps_pred_matched = torch.from_numpy(grasps_pred_matched)
grasps_gt_for_pred = torch.from_numpy(grasps_gt_for_pred)
stats = compute_metrics_given_two_sets_of_poses(
grasps_gt_for_pred,
grasps_pred_matched,
self.gripper_info,
consider_symmetry=True,
)
recall = compute_recall(grasps_gt_i, grasps_pred_i)
precision = compute_recall(grasps_pred_i, grasps_gt_i)
stats["recall"] = torch.tensor(recall).to(device)
stats["precision"] = torch.tensor(precision).to(device)
all_stats.append(stats)
stats_keys = all_stats[0].keys()
stats_batch = {}
for key in stats_keys:
stats_batch[key] = torch.mean(
torch.tensor([stats[key] for stats in all_stats]).to(device)
)
outputs = {
"grasps_pred": grasps_pred,
"grasps_per_iteration": grasps_per_iteration,
"grasp_confidence": torch.zeros(grasps_pred.shape[0]),
"grasping_masks": torch.zeros(grasps_pred.shape[0]),
"grasp_contacts": torch.zeros(grasps_pred.shape[0]),
"instance_masks": torch.zeros(grasps_pred.shape[0]),
"likelihood": likelihood.reshape(
num_objects_in_batch, num_grasps_per_batch, 1
),
}
return outputs, {}, stats_batch
class DiffusionNoisePredictionNet(nn.Module):
"""Neural network module implementing the diffusion model's denoising network.
This network predicts the noise in the diffused samples given the current noisy sample,
diffusion timestep, and object observation embedding.
Args:
diffusion_step_embed_dim (int): Dimension for diffusion step embeddings. Default: 512
observation_embed_dim (int): Dimension of object observation embeddings. Default: 512
sample_embed_dim (int): Dimension for sample embeddings. Default: 512
sample_dim (int): Dimension of the grasp samples. Default: 9
moreparams (bool): Whether to use additional parameters. Default: False
attention (bool): Whether to use attention mechanisms. Default: False
pose_repr (str): Type of pose representation. Default: 'mlp'
"""
def __init__(
self,
diffusion_step_embed_dim=512,
observation_embed_dim=512,
sample_embed_dim=512,
sample_dim=9,
moreparams=False,
attention=False,
pose_repr="mlp",
):
self.attention = attention
self.pose_repr = pose_repr
super().__init__()
self.diffusion_step_encoder = nn.Sequential(
SinusoidalPosEmb(diffusion_step_embed_dim),
nn.Linear(diffusion_step_embed_dim, diffusion_step_embed_dim * 4),
nn.Mish(),
nn.Linear(diffusion_step_embed_dim * 4, diffusion_step_embed_dim),
)
if self.pose_repr == "mlp":
self.sample_encoder = nn.Sequential(
nn.Linear(sample_dim, sample_embed_dim),
nn.ReLU(),
nn.Linear(sample_embed_dim, sample_embed_dim),
)
total_input_dim = (
sample_embed_dim + diffusion_step_embed_dim + observation_embed_dim
)
else:
total_input_dim = diffusion_step_embed_dim + observation_embed_dim
self.prediction_head = nn.Sequential(
nn.Linear(total_input_dim, total_input_dim // 2),
nn.ReLU(),
nn.Linear(total_input_dim // 2, total_input_dim // 4),
nn.ReLU(),
nn.Linear(total_input_dim // 4, sample_dim),
)
if self.attention.find("attn") > 0:
from grasp_gen.models.model_utils import AttentionLayer, FFNLayer
# transformer decoder
self.embed_dim = total_input_dim
self.num_heads = 8
self.num_layers = 3
self.feedforward_dim = 512
self.activation = "GELU"
num_grasp_queries = 1
if self.attention.find("cross") >= 0:
self.obs_pos_enc = nn.Embedding(1, observation_embed_dim)
self.sample_pos_enc = nn.Embedding(1, sample_embed_dim)
self.time_pos_enc = nn.Embedding(1, diffusion_step_embed_dim)
self.query_embed = nn.Embedding(1, self.embed_dim)
self.query_pos_enc = nn.Embedding(1, self.embed_dim)
self.self_attention_layers = nn.ModuleList()
self.cross_attention_layers = nn.ModuleList()
self.ffn_layers = nn.ModuleList()
for _ in range(self.num_layers):
self.self_attention_layers.append(
AttentionLayer(self.embed_dim, self.num_heads)
)
self.cross_attention_layers.append(
AttentionLayer(self.embed_dim, self.num_heads)
)
self.ffn_layers.append(
FFNLayer(self.embed_dim, self.feedforward_dim, self.activation)
)
else:
self.query_pos_enc = nn.Embedding(num_grasp_queries, self.embed_dim)
self.self_attention_layers = nn.ModuleList()
self.ffn_layers = nn.ModuleList()
for _ in range(self.num_layers):
self.self_attention_layers.append(
AttentionLayer(self.embed_dim, self.num_heads)
)
self.ffn_layers.append(
FFNLayer(self.embed_dim, self.feedforward_dim, self.activation)
)
def forward(
self,
observation_embedding: torch.Tensor,
timesteps: torch.Tensor,
sample: torch.Tensor = None,
):
"""Forward pass of the diffusion denoising network.
Args:
observation_embedding (torch.Tensor): Object observation embeddings
timesteps (torch.Tensor): Current diffusion timesteps
sample (torch.Tensor, optional): Current noisy samples
Returns:
torch.Tensor: Predicted noise in the samples
"""
device = observation_embedding.device
if torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
timesteps = timesteps[None].to(device)
timesteps = timesteps.expand(observation_embedding.shape[0])
timestep_embedding = self.diffusion_step_encoder(timesteps)
if self.pose_repr == "mlp":
sample_embedding = self.sample_encoder(sample)
if self.attention.find("attn") >= 0:
if self.attention.find("cross") >= 0:
from grasp_gen.models.model_utils import repeat_new_axis
# t0 = time.time()
batch_size = timestep_embedding.shape[0]
embed = repeat_new_axis(self.query_embed.weight, batch_size, dim=1)
query_pos_enc = repeat_new_axis(
self.query_pos_enc.weight, batch_size, dim=1
)
obs_pos_enc = repeat_new_axis(
self.obs_pos_enc.weight, batch_size, dim=1
)
time_pos_enc = repeat_new_axis(
self.time_pos_enc.weight, batch_size, dim=1
)
# TODO - Improve this...
sample_pos_enc = repeat_new_axis(
self.sample_pos_enc.weight, batch_size, dim=1
)
cross_embed = torch.cat(
[timestep_embedding, observation_embedding, sample_embedding],
axis=1,
).unsqueeze(0)
cross_embed_pos_enc = torch.cat(
[time_pos_enc, obs_pos_enc, sample_pos_enc], axis=2
)
for i in range(self.num_layers):
embed = self.cross_attention_layers[i](
embed,
cross_embed,
cross_embed + cross_embed_pos_enc,
query_pos_enc,
cross_embed_pos_enc,
)
embed = self.self_attention_layers[i](
embed,
embed,
embed + query_pos_enc,
query_pos_enc,
query_pos_enc,
)
embed = self.ffn_layers[i](embed)
else:
from grasp_gen.models.model_utils import repeat_new_axis
t0 = time.time()
if self.pose_repr == "mlp":
embed = torch.cat(
[sample_embedding, timestep_embedding, observation_embedding],
axis=-1,
)
# print(f"Concatenation took {time.time() - t0}s")
else:
embed = torch.cat(
[timestep_embedding, observation_embedding], axis=-1
)
embed = embed.unsqueeze(0)
batch_size = embed.shape[1]
query_pos_enc = repeat_new_axis(
self.query_pos_enc.weight, batch_size, dim=1
)
t0 = time.time()
for i in range(self.num_layers):
embed = self.self_attention_layers[i](
embed,
embed,
embed + query_pos_enc,
query_pos_enc,
query_pos_enc,
)
embed = self.ffn_layers[i](embed)
# print(f"Attention took {time.time() - t0}s")
embed = embed.squeeze(0)
else:
t0 = time.time()
if self.pose_repr == "mlp":
embed = torch.cat(
[sample_embedding, timestep_embedding, observation_embedding],
axis=-1,
)
else:
embed = torch.cat([timestep_embedding, observation_embedding], axis=-1)
return self.prediction_head(embed)
================================================
FILE: grasp_gen/models/grasp_gen.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
import torch
import torch.nn as nn
from omegaconf import DictConfig
from grasp_gen.models.discriminator import GraspGenDiscriminator
from grasp_gen.models.generator import GraspGenGenerator
from grasp_gen.utils.logging_config import get_logger
logger = get_logger(__name__)
class GraspGen(nn.Module):
"""Combined model that uses both diffusion-based generation and discriminative evaluation.
This class combines a GraspGenGenerator generator with a GraspGenDiscriminator to both
generate and evaluate grasps in a single pipeline.
Args:
grasp_generator_cfg (DictConfig): Configuration for the grasp generator
grasp_discriminator_cfg (DictConfig): Configuration for the grasp discriminator
"""
def __init__(
self, grasp_generator_cfg: DictConfig, grasp_discriminator_cfg: DictConfig
):
super(GraspGen, self).__init__()
self.grasp_generator = GraspGenGenerator.from_config(grasp_generator_cfg)
self.grasp_discriminator = GraspGenDiscriminator.from_config(
grasp_discriminator_cfg
)
def forward(self, data):
"""Forward pass combining generation and discrimination.
Args:
data: Input data dictionary containing point clouds
Returns:
tuple: (outputs, losses, stats) containing generated and scored grasps
"""
outputs, _, stats = self.grasp_generator.infer(data, return_metrics=True)
data.update(outputs)
data["grasp_key"] = (
"grasps_pred" # Override to run discriminator inference on grasps predicted from previous step.
)
outputs, _, _ = self.grasp_discriminator.infer(data)
return outputs, {}, stats
def infer(self, data, return_metrics=False):
"""Inference method for generating and evaluating grasps.
Args:
data: Input data dictionary containing point clouds
return_metrics (bool): Whether to compute evaluation metrics
Returns:
tuple: (outputs, losses, stats) containing generated and scored grasps with metrics
"""
return self.forward(data)
@classmethod
def from_config(
cls, grasp_generator_cfg: DictConfig, grasp_discriminator_cfg: DictConfig
):
"""Creates a GraspGen instance from configuration objects.
Args:
grasp_generator_cfg (DictConfig): Configuration for the grasp generator
grasp_discriminator_cfg (DictConfig): Configuration for the grasp discriminator
Returns:
GraspGen: Instantiated model
"""
return GraspGen(grasp_generator_cfg, grasp_discriminator_cfg)
def load_state_dict(
self, grasp_generator_ckpt_filepath: str, grasp_discriminator_ckpt_filepath: str
):
"""Loads pretrained weights for both generator and discriminator.
Args:
grasp_generator_ckpt_filepath (str): Path to generator checkpoint
grasp_discriminator_ckpt_filepath (str): Path to discriminator checkpoint
"""
logger.info(
f"Loading generator checkpoint from {grasp_generator_ckpt_filepath}"
)
ckpt = torch.load(grasp_generator_ckpt_filepath, map_location="cpu")
self.grasp_generator.load_state_dict(ckpt["model"])
logger.info(
f"Loading discriminator checkpoint from {grasp_discriminator_ckpt_filepath}"
)
ckpt = torch.load(grasp_discriminator_ckpt_filepath, map_location="cpu")
self.grasp_discriminator.load_state_dict(ckpt["model"])
================================================
FILE: grasp_gen/models/m2t2.py
================================================
# 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.
#
# Author: Wentao Yuan
"""
Top-level M2T2 network.
"""
import torch
import torch.nn as nn
from grasp_gen.models.action_decoder import ActionDecoder, infer_placements
from grasp_gen.models.contact_decoder import ContactDecoder
from grasp_gen.models.criterion import GraspCriterion, PlaceCriterion, SetCriterion
from grasp_gen.models.matcher import HungarianMatcher
from grasp_gen.models.model_utils import repeat_new_axis
from grasp_gen.models.pointnet.pointnet2 import PointNet2MSG, PointNet2MSGCls
class M2T2(nn.Module):
def __init__(
self,
scene_encoder: nn.Module,
contact_decoder: nn.Module,
object_encoder: nn.Module = None,
grasp_mlp: nn.Module = None,
set_criterion: nn.Module = None,
grasp_criterion: nn.Module = None,
place_criterion: nn.Module = None,
):
super(M2T2, self).__init__()
self.scene_encoder = scene_encoder
self.object_encoder = object_encoder
self.contact_decoder = contact_decoder
self.grasp_mlp = grasp_mlp
self.set_criterion = set_criterion
self.grasp_criterion = grasp_criterion
self.place_criterion = place_criterion
@classmethod
def from_config(cls, cfg):
args = {}
args["scene_encoder"] = PointNet2MSG.from_config(cfg.scene_encoder)
channels = args["scene_encoder"].out_channels
obj_channels = None
if cfg.contact_decoder.num_place_queries > 0:
args["object_encoder"] = PointNet2MSGCls.from_config(cfg.object_encoder)
obj_channels = args["object_encoder"].out_channels
args["place_criterion"] = PlaceCriterion.from_config(cfg.place_loss)
args["contact_decoder"] = ContactDecoder.from_config(
cfg.contact_decoder, channels, obj_channels
)
if cfg.contact_decoder.num_grasp_queries > 0:
args["grasp_mlp"] = ActionDecoder.from_config(
cfg.action_decoder, args["contact_decoder"]
)
matcher = HungarianMatcher.from_config(cfg.matcher)
args["set_criterion"] = SetCriterion.from_config(cfg.grasp_loss, matcher)
args["grasp_criterion"] = GraspCriterion.from_config(cfg.grasp_loss)
return cls(**args)
def forward(self, data, cfg):
scene_feat = self.scene_encoder(data["inputs"])
object_feat = {"features": {}}
if self.object_encoder is not None:
object_feat = self.object_encoder(data["object_inputs"])
if "task_is_place" in data:
for key, val in object_feat["features"].items():
object_feat["features"][key] = val * data["task_is_place"].view(
data["task_is_place"].shape[0], 1, 1
)
embedding, outputs = self.contact_decoder(scene_feat, object_feat)
losses = {}
if self.place_criterion is not None:
losses, stats = self.place_criterion(outputs, data)
outputs[-1].update(stats)
if self.set_criterion is not None:
set_losses, outputs = self.set_criterion(outputs, data)
losses.update(set_losses)
else:
outputs = outputs[-1]
mask_features = scene_feat["features"][self.contact_decoder.mask_feature]
if self.set_criterion is not None:
obj_embedding = [
emb[idx] for emb, idx in zip(embedding["grasp"], outputs["matched_idx"])
]
else:
# There is only one token
obj_embedding = [emb[0] for emb in embedding["grasp"]]
confidence = [mask.sigmoid() for mask in outputs["matched_grasping_masks"]]
if self.grasp_criterion is not None:
grasp_outputs = self.grasp_mlp(
data["points"],
mask_features,
confidence,
cfg.mask_thresh,
obj_embedding,
data["grasping_masks"],
)
outputs.update(grasp_outputs)
contact_losses = self.grasp_criterion(outputs, data)
losses.update(contact_losses)
return outputs, losses
def infer(self, data, cfg):
scene_feat = self.scene_encoder(data["inputs"])
object_feat = {"features": {}}
if self.object_encoder is not None:
object_feat = self.object_encoder(data["object_inputs"])
embedding, outputs = self.contact_decoder(scene_feat, object_feat)
outputs = outputs[-1]
if cfg.task == "place":
if cfg.cam_coord:
cam_pose = data["cam_pose"]
else:
cam_pose = repeat_new_axis(
torch.eye(4).to(data["inputs"].device),
data["inputs"].shape[0],
dim=0,
)
placement_outputs = infer_placements(
data["points"],
outputs["placement_masks"],
data["bottom_center"],
data["ee_pose"],
cam_pose,
cfg.mask_thresh,
cfg.placement_height,
)
outputs.update(placement_outputs)
outputs["placement_masks"] = (
outputs["placement_masks"].sigmoid() > cfg.mask_thresh
)
if cfg.task == "pick":
objectness = outputs["objectness"].sigmoid()
confidence = outputs["grasping_masks"].sigmoid()
masks = confidence > cfg.mask_thresh
object_ids = [
torch.where((score > cfg.object_thresh) & (mask.sum(dim=1) > 0))[0]
for score, mask in zip(objectness, masks)
]
outputs["objectness"] = [
score[idx] for score, idx in zip(objectness, object_ids)
]
outputs["grasping_masks"] = [
mask[idx] for mask, idx in zip(masks, object_ids)
]
confidence = [conf[idx] for conf, idx in zip(confidence, object_ids)]
mask_features = scene_feat["features"][self.contact_decoder.mask_feature]
obj_embedding = [
emb[idx] for emb, idx in zip(embedding["grasp"], object_ids)
]
if self.grasp_mlp is not None:
grasp_outputs = self.grasp_mlp(
data["points"],
mask_features,
confidence,
cfg.mask_thresh,
obj_embedding,
)
outputs.update(grasp_outputs)
else:
xyz = data["points"]
mask_feats = mask_features.moveaxis(1, -1) # [B, H, W, mask_dim]
contacts, conf_all, inputs, num_grasps = [], [], [], []
total_grasps, num_objs = 0, 0
mask_thresh = cfg.mask_thresh
for i, (pts, feat, emb, conf_scene) in enumerate(
zip(xyz, mask_feats, embedding, confidence)
):
masks = conf_scene > mask_thresh
conf_list, num = [], []
for e, m, conf in zip(emb, masks, conf_scene):
f, p, c = feat[m], pts[m], conf[m]
inputs.append(f)
conf_list.append(c)
contacts.append(p)
num.append(f.shape[0])
total_grasps += f.shape[0]
conf_all.append(conf_list)
contacts = torch.cat(contacts)
outputs["grasp_contacts"] = contacts
outputs["grasp_confidence"] = conf_all
return outputs
================================================
FILE: grasp_gen/models/matcher.py
================================================
# 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.
#
# Author: Wentao Yuan
"""
Modules to compute the matching cost and solve the corresponding LSAP.
"""
import numpy as np
import torch
import torch.nn.functional as F
from scipy.optimize import linear_sum_assignment
from torch.cuda.amp import autocast
def dice_loss_matrix(inputs: torch.Tensor, targets: torch.Tensor):
"""
Compute the DICE loss, similar to generalized IOU for masks
Args:
inputs: A float tensor with shape [N, ...].
Predicted logits for each query.
targets: A float tensor with shape [M, ...].
Ground truth binary mask for each object.
Returns:
loss matrix of shape [N, M], averaged across all pixels/points
"""
inputs = inputs.sigmoid()
numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets)
denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :]
loss = 1 - (numerator + 1) / (denominator + 1)
return loss
def bce_loss_matrix(inputs: torch.Tensor, targets: torch.Tensor):
"""
Args:
inputs: A float tensor with shape [N, ...].
Predicted logits for each query.
targets: A float tensor with shape [M, ...].
Ground truth binary mask for each object.
Returns:
loss matrix of shape [N, M], averaged across all pixels/points
"""
pos = F.binary_cross_entropy_with_logits(
inputs, torch.ones_like(inputs), reduction="none"
)
neg = F.binary_cross_entropy_with_logits(
inputs, torch.zeros_like(inputs), reduction="none"
)
num_points = inputs.shape[1]
with autocast(enabled=False):
loss = torch.einsum("nc,mc->nm", pos.float(), targets) + torch.einsum(
"nc,mc->nm", neg.float(), (1 - targets)
)
return loss / num_points
class HungarianMatcher(torch.nn.Module):
"""This class computes a 1-to-1 assignment between the targets and the
network's predictions. The targets only include objects, so in general,
there are more predictions than targets. The un-matched predictions are
treated as non-objects).
"""
def __init__(self, object_weight, bce_weight, dice_weight):
super(HungarianMatcher, self).__init__()
self.object_weight = object_weight
self.bce_weight = bce_weight
self.dice_weight = dice_weight
@classmethod
def from_config(cls, cfg):
args = {}
args["object_weight"] = cfg.object_weight
args["bce_weight"] = cfg.bce_weight
args["dice_weight"] = cfg.dice_weight
return cls(**args)
@torch.no_grad()
def forward(self, outputs, data):
"""Performs the matching
Params:
outputs: a dict that contains these entries:
"objectness": dim [batch_size, num_queries]
logits for the objectness score
"instance_masks": dim [batch_size, num_queries, ...]
predicted object instance masks
"contact_masks": dim [batch_size, num_queries, ...]
predicted grasp contact masks
targets: a dict that contains these entries:
"instance_masks": a list of batch_size tensors
ground truth object instance masks
"contact_masks": a list of batch_size tensors
ground truth grasp contact masks
Returns:
indices: a list of length batch_size, containing indices of the
predictions that match the best with each target
"""
indices, cost_matrices = [], []
for i in range(len(outputs["objectness"])):
# We approximate objectness NLL loss with 1 - prob.
# The 1 is a constant that can be ommitted.
cost = (
self.object_weight * (-outputs["objectness"][i : i + 1].T.sigmoid())
+ self.bce_weight
* bce_loss_matrix(
outputs["grasping_masks"][i], data["grasping_masks"][i]
)
+ self.dice_weight
* dice_loss_matrix(
outputs["grasping_masks"][i], data["grasping_masks"][i]
)
)
output_idx, target_idx = linear_sum_assignment(cost.cpu().numpy())
output_idx = output_idx[np.argsort(target_idx)]
indices.append(torch.from_numpy(output_idx).long().to(cost.device))
cost_matrices.append(cost)
return indices, cost_matrices
================================================
FILE: grasp_gen/models/model_utils.py
================================================
# Copyright (c) 2024, 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.
#
# Author: Wentao Yuan, Adithya Murali
"""
Utility functions for network.
"""
import collections
import math
import numpy as np
import torch
import torch.nn as nn
from grasp_gen.models.pointnet.pointnet2_modules import PointnetSAModule
from grasp_gen.utils.logging_config import get_logger
logger = get_logger(__name__)
def load_pretrained_checkpoint_to_dict(checkpoint_path, model_name):
"""Loads and processes a pretrained model checkpoint.
This function loads a checkpoint file and extracts the weights for a specific model
component, modifying the state dict keys to match the target model's structure.
Args:
checkpoint_path (str): Path to the checkpoint file
model_name (str): Name of the model component to extract weights for
Returns:
collections.OrderedDict: Processed state dictionary containing model weights
"""
ckpt = torch.load(checkpoint_path)["model"]
logger.info(
f"Loading pretrained {model_name} checkpoint from file {checkpoint_path}"
)
new_ckpt_dict = collections.OrderedDict()
for key in ckpt:
if key.find(model_name) >= 0:
key_modified = key[key.find(".") + 1 :]
new_ckpt_dict[key_modified] = ckpt[key]
return new_ckpt_dict
def convert_to_ptv3_pc_format(point_cloud: torch.Tensor, grid_size: float = 0.01):
device = point_cloud.device
batch_size = point_cloud.shape[0]
num_points = point_cloud.shape[1]
data_dict = dict()
data_dict["coord"] = point_cloud.reshape([-1, 3]).to(device)
data_dict["grid_size"] = grid_size
data_dict["feat"] = point_cloud.reshape([-1, 3]).to(device)
data_dict["offset"] = (
torch.tensor([num_points]).repeat(batch_size).cumsum(dim=0).to(device)
)
return data_dict
def repeat_new_axis(tensor, rep, dim):
reps = [1] * len(tensor.shape)
reps.insert(dim, rep)
return tensor.unsqueeze(dim).repeat(*reps)
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
batch_size = x.shape[0]
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
if len(x.shape) == 1:
emb = x[:, None] * emb[None, :]
else:
emb = x[:, :, None] * emb[None, None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
emb = emb.reshape([batch_size, -1])
return emb
def break_up_pc(pc):
xyz = pc[..., 0:3].contiguous()
features = pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None
return xyz, features
def offset2bincount(offset):
return torch.diff(
offset, prepend=torch.tensor([0], device=offset.device, dtype=torch.long)
)
def offset2batch(offset):
bincount = offset2bincount(offset)
return torch.arange(
len(bincount), device=offset.device, dtype=torch.long
).repeat_interleave(bincount)
def compute_grasp_loss(target_grasps, pred_grasps, ctr_pts):
ctr_pts = ctr_pts.to(device=target_grasps.device)
pred_pts = (pred_grasps @ ctr_pts).transpose(-2, -1)[..., :3]
gt_pts = (target_grasps @ ctr_pts).transpose(-2, -1)[..., :3]
loss = torch.sum(torch.abs(pred_pts - gt_pts), -1) # Sum across xyz dimension
loss = torch.mean(loss, -1) # Mean over control points
return torch.mean(loss) # Mean over batch
def get_activation_fn(activation):
return getattr(nn, activation)()
OBJ_NPOINTS = [256, 64, None]
OBJ_RADII = [0.02, 0.04, None]
OBJ_NSAMPLES = [64, 128, None]
OBJ_MLPS = [[0, 64, 128], [128, 128, 256], [256, 256, 512]]
SCENE_PT_MLP = [3, 128, 256]
SCENE_VOX_MLP = [256, 512, 1024, 512]
CLS_FC = [2057, 1024, 256]
class PointNetPlusPlus(nn.Module):
def __init__(
self, activation="relu", bn=False, output_embedding_dim=512, feature_dim=-1
):
mlp = []
for elem in OBJ_MLPS:
mlp.append(elem.copy())
if feature_dim > 0:
mlp[0][0] = feature_dim
super().__init__()
self.activation = activation
self.output_embedding_dim = output_embedding_dim
self.obj_SA_modules = nn.ModuleList()
for k in range(OBJ_NPOINTS.__len__()):
self.obj_SA_modules.append(
PointnetSAModule(
npoint=OBJ_NPOINTS[k],
radius=OBJ_RADII[k],
nsample=OBJ_NSAMPLES[k],
mlp=mlp[k],
use_xyz=True,
)
)
self.prediction_head = nn.Sequential(
nn.Linear(OBJ_MLPS[-1][-1], 1024),
nn.ReLU(),
nn.Linear(1024, 1024),
nn.ReLU(),
nn.Linear(1024, self.output_embedding_dim),
)
def forward(self, pc):
xyz, features = break_up_pc(pc)
for i in range(len(self.obj_SA_modules)):
# new_xyz, new_xyz_idx, features, sample_ids
xyz, _, features, _ = self.obj_SA_modules[i](xyz, features)
features = self.prediction_head(features.squeeze(axis=-1))
return features
class MLP(nn.Module):
def __init__(
self,
input_dim,
hidden_dim,
output_dim,
num_layers,
activation="ReLU",
dropout=0.0,
):
super().__init__()
h = [hidden_dim] * (num_layers - 1)
layers = []
for m, n in zip([input_dim] + h[:-1], h):
layers.extend([nn.Linear(m, n), get_activation_fn(activation)])
if dropout > 0:
layers.append(nn.Dropout(dropout))
layers.append(nn.Linear(hidden_dim, output_dim))
self.mlp = nn.Sequential(*layers)
def forward(self, x):
return self.mlp(x)
class AttentionLayer(nn.Module):
def __init__(self, embed_dim, num_heads):
super().__init__()
self.attn = nn.MultiheadAttention(embed_dim, num_heads)
self.norm = nn.LayerNorm(embed_dim)
def forward(self, query, key, value, query_pos_enc, key_pos_enc, attn_mask=None):
output, attn = self.attn(
query + query_pos_enc, key + key_pos_enc, value, attn_mask=attn_mask
)
return self.norm(query + output)
class FFNLayer(nn.Module):
def __init__(self, embed_dim, hidden_dim, activation="ReLU"):
super().__init__()
self.ff = nn.Sequential(
nn.Linear(embed_dim, hidden_dim),
get_activation_fn(activation),
nn.Linear(hidden_dim, embed_dim),
)
self.norm = nn.LayerNorm(embed_dim)
def forward(self, x):
return self.norm(x + self.ff(x))
class PositionEncoding3D(nn.Module):
"""
Generate sinusoidal positional encoding
f(p) = (sin(2^0 pi p), cos(2^0 pi p), ..., sin(2^L pi pi), cos(2^L pi p))
"""
def __init__(self, enc_dim, scale=np.pi, temperature=10000):
super(PositionEncoding3D, self).__init__()
self.enc_dim = enc_dim
self.freq = np.ceil(enc_dim / 6)
self.scale = scale
self.temperature = temperature
def forward(self, pos):
pos_min = pos.min(dim=1, keepdim=True)[0]
pos_max = pos.max(dim=1, keepdim=True)[0]
pos = ((pos - pos_min) / (pos_max - pos_min) - 0.5) * 2 * np.pi
dim_t = torch.arange(self.freq, dtype=torch.float32, device=pos.device)
dim_t = self.temperature ** (dim_t / self.freq)
pos = pos[..., None] * self.scale / dim_t # (B, N, 3, F)
pos = torch.stack([pos.sin(), pos.cos()], dim=-1).flatten(start_dim=2)
pos = pos[..., : self.enc_dim].transpose(1, 2)
return pos.detach()
class PositionEncodingOld3D(nn.Module):
"""
Generate sinusoidal positional encoding
f(p) = (sin(2^0 pi p), cos(2^0 pi p), ..., sin(2^L pi pi), cos(2^L pi p))
"""
def __init__(self, enc_dim, scale=np.pi, temperature=10000):
super(PositionEncodingOld3D, self).__init__()
self.enc_dim = enc_dim
self.freq = np.ceil(enc_dim / 6)
self.scale = scale
self.temperature = temperature
def forward(self, pos):
pos_min = pos.min(dim=-1, keepdim=True)[0]
pos_max = pos.max(dim=-1, keepdim=True)[0]
pos = ((pos - pos_min) / (pos_max - pos_min) - 0.5) * 2 * np.pi
dim_t = torch.arange(self.freq, dtype=torch.float32, device=pos.device)
dim_t = self.temperature ** (dim_t / self.freq)
pos = pos[..., None] * self.scale / dim_t # (B, N, 3, F)
pos = torch.stack([pos.sin(), pos.cos()], dim=-1).flatten(start_dim=2)
pos = pos[..., : self.enc_dim].transpose(1, 2)
return pos.detach()
================================================
FILE: grasp_gen/models/pointnet/pointnet2.py
================================================
# 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.
#
# Author: Wentao Yuan
"""
Network modules for PointNet++.
"""
import torch.nn as nn
from grasp_gen.models.pointnet.pointnet2_modules import (
PointnetFPModule,
PointnetSAModule,
PointnetSAModuleMSG,
)
class PointNet2Base(nn.Module):
def __init__(self):
super(PointNet2Base, self).__init__()
self.SA_modules = nn.ModuleList()
self.FP_modules = nn.ModuleList()
def _break_up_pc(self, pc):
xyz = pc[..., :3].contiguous()
features = None
if pc.shape[-1] > 3 and self.use_rgb:
features = pc[..., 3:].transpose(1, 2).contiguous()
return xyz, features
def forward(self, pointcloud):
r"""
Forward pass of the network
Parameters
----------
pointcloud: Variable(torch.cuda.FloatTensor)
(B, N, 3 + input_channels) tensor
Point cloud to run predicts on
Each point in the point-cloud MUST
be formated as (x, y, z, features...)
"""
xyz, features = self._break_up_pc(pointcloud)
l_xyz, l_features, sample_ids = [xyz], [features], []
for i in range(len(self.SA_modules)):
li_xyz, _, li_features, sample_idx = self.SA_modules[i](
l_xyz[i], l_features[i]
)
l_xyz.append(li_xyz)
l_features.append(li_features)
if sample_idx[0] is not None:
sample_ids.append(sample_idx[0])
for i in range(-1, -(len(self.FP_modules) + 1), -1):
l_features[i - 1] = self.FP_modules[i](
l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i]
)
l_features = {
f"res{i}": feat for i, feat in enumerate(l_features) if feat is not None
}
l_xyz = {f"res{i}": xyz for i, xyz in enumerate(l_xyz) if xyz is not None}
outputs = {
"features": l_features,
"context_pos": l_xyz,
"sample_ids": sample_ids,
}
return outputs
class PointNet2MSG(PointNet2Base):
def __init__(
self, num_points, downsample, radius, radius_mult, use_rgb=True, norm="BN"
):
super(PointNet2MSG, self).__init__()
self.use_rgb = use_rgb
c_in = 3 if use_rgb else 0
num_points = num_points // downsample
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=num_points,
radii=[radius, radius * radius_mult],
nsamples=[16, 32],
mlps=[[c_in, 32, 32, 64], [c_in, 32, 32, 64]],
norm=norm,
)
)
c_out_0 = 64 + 64
radius = radius * radius_mult
num_points = num_points // downsample
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=num_points,
radii=[radius, radius * radius_mult],
nsamples=[16, 32],
mlps=[[c_out_0, 64, 64, 128], [c_out_0, 64, 64, 128]],
norm=norm,
)
)
c_out_1 = 128 + 128
radius = radius * radius_mult
num_points = num_points // downsample
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=num_points,
radii=[radius, radius * radius_mult],
nsamples=[16, 32],
mlps=[[c_out_1, 128, 128, 256], [c_out_1, 128, 128, 256]],
norm=norm,
)
)
c_out_2 = 256 + 256
radius = radius * radius_mult
num_points = num_points // downsample
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=num_points,
radii=[radius, radius * radius_mult],
nsamples=[16, 32],
mlps=[[c_out_2, 256, 256, 512], [c_out_2, 256, 256, 512]],
norm=norm,
)
)
c_out_3 = 512 + 512
self.FP_modules.append(PointnetFPModule(mlp=[256 + c_in, 128, 128]))
self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_0, 256, 256]))
self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_1, 512, 512]))
self.FP_modules.append(PointnetFPModule(mlp=[c_out_3 + c_out_2, 512, 512]))
self.out_channels = {
"res0": 128,
"res1": 256,
"res2": 512,
"res3": 512,
"res4": 1024,
}
@classmethod
def from_config(cls, cfg):
args = {}
args["num_points"] = cfg.num_points
args["downsample"] = cfg.downsample
args["radius"] = cfg.radius
args["radius_mult"] = cfg.radius_mult
args["use_rgb"] = cfg.use_rgb
return cls(**args)
class PointNet2MSGCls(PointNet2Base):
def __init__(
self, num_points, downsample, radius, radius_mult, use_rgb=True, norm="BN"
):
super(PointNet2MSGCls, self).__init__()
self.use_rgb = use_rgb
c_in = 3 if use_rgb else 0
num_points = num_points // downsample
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=num_points,
radii=[radius, radius * radius_mult],
nsamples=[16, 32],
mlps=[[c_in, 32, 32, 64], [c_in, 32, 32, 64]],
norm=norm,
)
)
c_out_0 = 64 + 64
radius = radius * radius_mult
num_points = num_points // downsample
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=num_points,
radii=[radius, radius * radius_mult],
nsamples=[16, 32],
mlps=[[c_out_0, 64, 64, 128], [c_out_0, 64, 64, 128]],
norm=norm,
)
)
c_out_1 = 128 + 128
radius = radius * radius_mult
num_points = num_points // downsample
self.SA_modules.append(
PointnetSAModuleMSG(
npoint=num_points,
radii=[radius, radius * radius_mult],
nsamples=[16, 32],
mlps=[[c_out_1, 128, 128, 256], [c_out_1, 128, 128, 256]],
norm=norm,
)
)
c_out_2 = 256 + 256
self.SA_modules.append(
PointnetSAModule(mlp=[c_out_2, 256, 256, 512], norm=norm)
)
self.out_channels = {
"res0": c_in,
"res1": 128,
"res2": 256,
"res3": 512,
"res4": 512,
}
@classmethod
def from_config(cls, cfg):
args = {}
args["num_points"] = cfg.num_points
args["downsample"] = cfg.downsample
args["radius"] = cfg.radius
args["radius_mult"] = cfg.radius_mult
args["use_rgb"] = cfg.use_rgb
return cls(**args)
================================================
FILE: grasp_gen/models/pointnet/pointnet2_modules.py
================================================
# 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.
#
# Author: Wentao Yuan
"""
Modules for PointNet++.
"""
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from grasp_gen.models.pointnet.pointnet2_utils import (
GroupAll,
QueryAndGroup,
furthest_point_sample,
gather_operation,
three_interpolate,
three_nn,
)
def _get_norm(norm, dim):
if norm == "BN":
return nn.BatchNorm2d(dim)
if norm == "GN":
return nn.GroupNorm(16, dim)
def build_shared_mlp(mlp_spec: List[int], norm: str):
layers = []
for i in range(1, len(mlp_spec)):
layers.append(
nn.Conv2d(mlp_spec[i - 1], mlp_spec[i], kernel_size=1, bias=norm == "")
)
norm_layer = _get_norm(norm, mlp_spec[i])
if norm_layer is not None:
layers.append(norm_layer)
layers.append(nn.ReLU(True))
return nn.Sequential(*layers)
class _PointnetSAModuleBase(nn.Module):
def __init__(self):
super(_PointnetSAModuleBase, self).__init__()
self.npoint = None
self.groupers = None
self.mlps = None
def forward(
self, xyz: torch.Tensor, features: Optional[torch.Tensor]
) -> Tuple[torch.Tensor, torch.Tensor]:
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the features
features : torch.Tensor
(B, C, N) tensor of the descriptors of the the features
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new features' xyz
new_features : torch.Tensor
(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
sample_ids : torch.Tensor
list of (B, npoint, nsample) points indices from ball queries
"""
if self.npoint is not None:
new_xyz_idx = furthest_point_sample(xyz, self.npoint)
new_xyz = (
gather_operation(xyz.transpose(1, 2).contiguous(), new_xyz_idx)
.transpose(1, 2)
.contiguous()
)
else:
new_xyz_idx = torch.zeros_like(xyz[:, :1, 0]).long()
new_xyz = torch.zeros_like(xyz[:, :1])
new_features_list, sample_ids = [], []
for i in range(len(self.groupers)):
new_features, sample_idx = self.groupers[i](
xyz, new_xyz, features
) # (B, C, npoint, nsample)
new_features = self.mlps[i](new_features) # (B, mlp[-1], npoint, nsample)
new_features = new_features.max(dim=-1)[0] # (B, mlp[-1], npoint)
new_features_list.append(new_features)
sample_ids.append(sample_idx)
features = torch.cat(new_features_list, dim=1)
return new_xyz, new_xyz_idx, features, sample_ids
class _PointnetSAModuleVarNPts(nn.Module):
def __init__(self):
super(_PointnetSAModuleVarNPts, self).__init__()
self.npoint = None
self.groupers = None
self.mlps = None
def forward(
self, xyz: torch.Tensor, features: Optional[torch.Tensor], num_points: List[int]
) -> Tuple[torch.Tensor, torch.Tensor]:
r"""
Parameters
----------
xyz : torch.Tensor
(N, 3) tensor of the xyz coordinates of the features
features : torch.Tensor
(C, N) tensor of the descriptors of the the features
num_points : list of int
(B, ) number of points in each example
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new features' xyz
new_features : torch.Tensor
(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
sample_ids : torch.Tensor
list of (B, npoint, nsample) points indices from ball queries
"""
all_new_features, all_sample_ids = [], []
all_new_xyz, all_new_xyz_idx = [], []
if features is not None:
features = features.split(num_points, dim=-1)
for i, xyz in enumerate(xyz.split(num_points)):
xyz = xyz.unsqueeze(0)
feat = None
if features is not None:
feat = features[i].unsqueeze(0)
if self.npoint is not None:
new_xyz_idx = furthest_point_sample(xyz, self.npoint)
new_xyz = (
gather_operation(xyz.transpose(1, 2).contiguous(), new_xyz_idx)
.transpose(1, 2)
.contiguous()
)
all_new_xyz.append(new_xyz)
all_new_xyz_idx.append(new_xyz_idx)
else:
new_xyz_idx = torch.zeros_like(xyz[:, :1, 0]).long()
new_xyz = torch.zeros_like(xyz[:, :1])
new_features_list, sample_ids = [], []
for j in range(len(self.groupers)):
new_features, sample_idx = self.groupers[j](
xyz, new_xyz, feat
) # (1, C, npoint, nsample)
new_features_list.append(new_features)
sample_ids.append(sample_idx)
all_new_features.append(new_features_list)
all_sample_ids.append(sample_ids)
if self.npoint is not None:
new_xyz = torch.cat(all_new_xyz)
new_xyz_idx = torch.cat(all_new_xyz_idx)
new_features_list, sample_ids = [], []
for i in range(len(self.groupers)):
inputs = torch.cat([features[i] for features in all_new_features])
new_features = self.mlps[i](inputs) # (B, mlp[-1], npoint, nsample)
new_features = new_features.max(dim=-1)[0] # (B, mlp[-1], npoint)
new_features_list.append(new_features)
sample_ids.append(torch.cat([sample_id[i] for sample_id in all_sample_ids]))
features = torch.cat(new_features_list, dim=1)
return new_xyz, new_xyz_idx, features, sample_ids
class PointnetSAModuleMSG(_PointnetSAModuleBase):
r"""Pointnet set abstrction layer with multiscale grouping
Parameters
----------
npoint : int
Number of features
radii : list of float32
list of radii to group with
nsamples : list of int32
Number of samples in each ball query
mlps : list of list of int32
Spec of the pointnet before the global max_pool for each scale
norm : str
Type of normalization layer (BN/GN)
"""
def __init__(self, npoint, radii, nsamples, mlps, norm="BN", use_xyz=True):
super(PointnetSAModuleMSG, self).__init__()
assert len(radii) == len(nsamples) == len(mlps)
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
for i in range(len(radii)):
radius = radii[i]
nsample = nsamples[i]
self.groupers.append(
QueryAndGroup(radius, nsample, use_xyz=use_xyz)
if npoint is not None
else GroupAll(use_xyz)
)
mlp_spec = mlps[i]
if use_xyz:
mlp_spec[0] += 3
self.mlps.append(build_shared_mlp(mlp_spec, norm))
class PointnetSAModuleMSGVarNPts(_PointnetSAModuleVarNPts):
r"""Pointnet set abstrction layer with multiscale grouping
Parameters
----------
npoint : int
Number of features
radii : list of float32
list of radii to group with
nsamples : list of int32
Number of samples in each ball query
mlps : list of list of int32
Spec of the pointnet before the global max_pool for each scale
norm : str
Type of normalization layer (BN/GN)
"""
def __init__(self, npoint, radii, nsamples, mlps, norm="BN", use_xyz=True):
super(PointnetSAModuleMSGVarNPts, self).__init__()
assert len(radii) == len(nsamples) == len(mlps)
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
for i in range(len(radii)):
radius = radii[i]
nsample = nsamples[i]
self.groupers.append(
QueryAndGroup(radius, nsample, use_xyz=use_xyz)
if npoint is not None
else GroupAll(use_xyz)
)
mlp_spec = mlps[i]
if use_xyz:
mlp_spec[0] += 3
self.mlps.append(build_shared_mlp(mlp_spec, norm))
class PointnetSAModule(PointnetSAModuleMSG):
r"""Pointnet set abstrction layer
Parameters
----------
npoint : int
Number of features
radius : float
Radius of ball
nsample : int
Number of samples in the ball query
mlp : list
Spec of the pointnet before the global max_pool
norm : str
Type of normalization layer (BN/GN)
"""
def __init__(
self, mlp, npoint=None, radius=None, nsample=None, norm="BN", use_xyz=True
):
super(PointnetSAModule, self).__init__(
mlps=[mlp],
npoint=npoint,
radii=[radius],
nsamples=[nsample],
norm=norm,
use_xyz=use_xyz,
)
class PointnetFPModule(nn.Module):
r"""Propigates the features of one set to another
Parameters
----------
mlp : list
Pointnet module parameters
norm : str
Type of normalization layer (BN/GN)
"""
def __init__(self, mlp, norm="BN"):
# type: (PointnetFPModule, List[int], bool) -> None
super(PointnetFPModule, self).__init__()
self.mlp = build_shared_mlp(mlp, norm)
def forward(self, unknown, known, unknow_feats, known_feats):
r"""
Parameters
----------
unknown : torch.Tensor
(B, n, 3) tensor of the xyz positions of the unknown features
known : torch.Tensor
(B, m, 3) tensor of the xyz positions of the known features
unknow_feats : torch.Tensor
(B, C1, n) tensor of the features to be propigated to
known_feats : torch.Tensor
(B, C2, m) tensor of features to be propigated
Returns
-------
new_features : torch.Tensor
(B, mlp[-1], n) tensor of the features of the unknown features
"""
dist, idx = three_nn(unknown, known)
dist_recip = 1.0 / (dist + 1e-8)
weight = dist_recip / torch.sum(dist_recip, dim=2, keepdim=True)
new_features = three_interpolate(known_feats.float(), idx, weight)
if unknow_feats is not None:
new_features = torch.cat(
[new_features, unknow_feats], dim=1
) # (B, C2 + C1, n)
new_features = self.mlp(new_features.unsqueeze(-1))
return new_features.squeeze(-1)
================================================
FILE: grasp_gen/models/pointnet/pointnet2_utils.py
================================================
# 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.
#
# Author: Wentao Yuan
"""
Utility functions for PointNet++.
"""
import warnings
import torch
import torch.nn as nn
from torch.autograd import Function
try:
from pointnet2_ops import _ext as ops
except ImportError:
import glob
import os.path as osp
from torch.utils.cpp_extension import load
warnings.warn("Unable to load pointnet2_ops cpp extension. JIT Compiling.")
_ext_src_root = osp.join(osp.dirname(__file__), "_ext-src")
_ext_sources = glob.glob(osp.join(_ext_src_root, "src", "*.cpp")) + glob.glob(
osp.join(_ext_src_root, "src", "*.cu")
)
# os.environ["TORCH_CUDA_ARCH_LIST"] = "3.7+PTX;5.0;6.0;6.1;6.2;7.0;7.5"
ops = load(
"_ext",
sources=_ext_sources,
extra_include_paths=[osp.join(_ext_src_root, "include")],
extra_cflags=["-O3"],
extra_cuda_cflags=["-O3", "-Xfatbin", "-compress-all"],
with_cuda=True,
)
class FurthestPointSampling(Function):
@staticmethod
def forward(ctx, xyz, npoint):
# type: (Any, torch.Tensor, int) -> torch.Tensor
r"""
Uses iterative furthest point sampling to select a set of npoint features that have the largest
minimum distance
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor where N > npoint
npoint : int32
number of features in the sampled set
Returns
-------
torch.Tensor
(B, npoint) tensor containing the set
"""
out = ops.furthest_point_sampling(xyz, npoint)
ctx.mark_non_differentiable(out)
return out
@staticmethod
def backward(ctx, grad_out):
return ()
furthest_point_sample = FurthestPointSampling.apply
class GatherOperation(Function):
@staticmethod
def forward(ctx, features, idx):
# type: (Any, torch.Tensor, torch.Tensor) -> torch.Tensor
r"""
Parameters
----------
features : torch.Tensor
(B, C, N) tensor
idx : torch.Tensor
(B, npoint) tensor of the features to gather
Returns
-------
torch.Tensor
(B, C, npoint) tensor
"""
ctx.save_for_backward(idx, features)
output = ops.gather_points(features, idx)
return output
@staticmethod
def backward(ctx, grad_out):
idx, features = ctx.saved_tensors
N = features.size(2)
grad_features = ops.gather_points_grad(grad_out.contiguous(), idx, N)
return grad_features, None
gather_operation = GatherOperation.apply
class ThreeNN(Function):
@staticmethod
def forward(ctx, unknown, known):
# type: (Any, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
r"""
Find the three nearest neighbors of unknown in known
Parameters
----------
unknown : torch.Tensor
(B, n, 3) tensor of unknown features
known : torch.Tensor
(B, m, 3) tensor of known features
Returns
-------
dist : torch.Tensor
(B, n, 3) l2 distance to the three nearest neighbors
idx : torch.Tensor
(B, n, 3) index of 3 nearest neighbors
"""
dist2, idx = ops.three_nn(unknown, known)
dist = torch.sqrt(dist2)
ctx.mark_non_differentiable(dist, idx)
return dist, idx
@staticmethod
def backward(ctx, grad_dist, grad_idx):
return ()
three_nn = ThreeNN.apply
class ThreeInterpolate(Function):
@staticmethod
def forward(ctx, features, idx, weight):
# type(Any, torch.Tensor, torch.Tensor, torch.Tensor) -> Torch.Tensor
r"""
Performs weight linear interpolation on 3 features
Parameters
----------
features : torch.Tensor
(B, c, m) Features descriptors to be interpolated from
idx : torch.Tensor
(B, n, 3) three nearest neighbors of the target features in features
weight : torch.Tensor
(B, n, 3) weights
Returns
-------
torch.Tensor
(B, c, n) tensor of the interpolated features
"""
ctx.save_for_backward(idx, weight, features)
return ops.three_interpolate(features, idx, weight)
@staticmethod
def backward(ctx, grad_out):
# type: (Any, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
r"""
Parameters
----------
grad_out : torch.Tensor
(B, c, n) tensor with gradients of ouputs
Returns
-------
grad_features : torch.Tensor
(B, c, m) tensor with gradients of features
None
None
"""
idx, weight, features = ctx.saved_tensors
m = features.size(2)
grad_features = ops.three_interpolate_grad(
grad_out.contiguous(), idx, weight, m
)
return grad_features, torch.zeros_like(idx), torch.zeros_like(weight)
three_interpolate = ThreeInterpolate.apply
class GroupingOperation(Function):
@staticmethod
def forward(ctx, features, idx):
# type: (Any, torch.Tensor, torch.Tensor) -> torch.Tensor
r"""
Parameters
----------
features : torch.Tensor
(B, C, N) tensor of features to group
idx : torch.Tensor
(B, npoint, nsample) tensor containing the indicies of features to group with
Returns
-------
torch.Tensor
(B, C, npoint, nsample) tensor
"""
ctx.save_for_backward(idx, features)
return ops.group_points(features, idx)
@staticmethod
def backward(ctx, grad_out):
# type: (Any, torch.tensor) -> Tuple[torch.Tensor, torch.Tensor]
r"""
Parameters
----------
grad_out : torch.Tensor
(B, C, npoint, nsample) tensor of the gradients of the output from forward
Returns
-------
torch.Tensor
(B, C, N) gradient of the features
None
"""
idx, features = ctx.saved_tensors
N = features.size(2)
grad_features = ops.group_points_grad(grad_out.contiguous(), idx, N)
return grad_features, torch.zeros_like(idx)
grouping_operation = GroupingOperation.apply
class BallQuery(Function):
@staticmethod
def forward(ctx, radius, nsample, xyz, new_xyz):
# type: (Any, float, int, torch.Tensor, torch.Tensor) -> torch.Tensor
r"""
Parameters
----------
radius : float
radius of the balls
nsample : int
maximum number of features in the balls
xyz : torch.Tensor
(B, N, 3) xyz coordinates of the features
new_xyz : torch.Tensor
(B, npoint, 3) centers of the ball query
Returns
-------
torch.Tensor
(B, npoint, nsample) tensor with the indicies of the features that form the query balls
"""
output = ops.ball_query(new_xyz, xyz, radius, nsample)
ctx.mark_non_differentiable(output)
return output
@staticmethod
def backward(ctx, grad_out):
return ()
ball_query = BallQuery.apply
class QueryAndGroup(nn.Module):
r"""
Groups with a ball query of radius
Parameters
----------
radius : float32
Radius of ball
nsample : int32
Maximum number of features to gather in the ball
"""
def __init__(self, radius, nsample, use_xyz=True):
# type: (QueryAndGroup, float, int, bool) -> None
super(QueryAndGroup, self).__init__()
self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz
def forward(self, xyz, new_xyz, features=None):
# type: (QueryAndGroup, torch.Tensor. torch.Tensor, torch.Tensor) -> Tuple[Torch.Tensor]
r"""
Parameters
----------
xyz : torch.Tensor
xyz coordinates of the features (B, N, 3)
new_xyz : torch.Tensor
centriods (B, npoint, 3)
features : torch.Tensor
Descriptors of the features (B, C, N)
Returns
-------
new_features : torch.Tensor
(B, 3 + C, npoint, nsample) grouped features from sampled points
idx : torch.Tensor
(B, npoint, nsample) indices of sampled points within query balls
"""
# num_in_ball = ((
# new_xyz.unsqueeze(2) - xyz.unsqueeze(1)).norm(dim=-1) < self.radius
# ).sum(dim=-1).float().mean()
# print(xyz.shape, new_xyz.shape, self.nsample, num_in_ball)
idx = ball_query(self.radius, self.nsample, xyz, new_xyz)
xyz_trans = xyz.transpose(1, 2).contiguous()
grouped_xyz = grouping_operation(xyz_trans, idx) # (B, 3, npoint, nsample)
grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1)
if features is not None:
grouped_features = grouping_operation(features.float(), idx)
if self.use_xyz:
new_features = torch.cat(
[grouped_xyz, grouped_features], dim=1
) # (B, C + 3, npoint, nsample)
else:
new_features = grouped_features
else:
assert (
self.use_xyz
), "Cannot have not features and not use xyz as a feature!"
new_features = grouped_xyz
return new_features, idx
class GroupAll(nn.Module):
r"""
Groups all features
Parameters
---------
"""
def __init__(self, use_xyz=True):
# type: (GroupAll, bool) -> None
super(GroupAll, self).__init__()
self.use_xyz = use_xyz
def forward(self, xyz, new_xyz, features=None):
# type: (GroupAll, torch.Tensor, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor]
r"""
Parameters
----------
xyz : torch.Tensor
xyz coordinates of the features (B, N, 3)
new_xyz : torch.Tensor
Ignored
features : torch.Tensor
Descriptors of the features (B, C, N)
Returns
-------
new_features : torch.Tensor
(B, C + 3, 1, N) tensor
None
"""
grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
if features is not None:
grouped_features = features.unsqueeze(2)
if self.use_xyz:
new_features = torch.cat(
[grouped_xyz, grouped_features], dim=1
) # (B, 3 + C, 1, N)
else:
new_features = grouped_features
else:
new_features = grouped_xyz
idx = torch.arange(xyz.shape[1]).to(xyz.device).long()
idx = idx.unsqueeze(0).repeat(xyz.shape[0], 1).unsqueeze(1)
return new_features, idx
================================================
FILE: grasp_gen/models/ptv3/ptv3.py
================================================
# Copyright (c) 2025, 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.
"""
Point Transformer - V3 Mode1
Pointcept detached version
Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
Please cite our work if the code is helpful to you.
"""
from timm.models.layers import DropPath
import math
import sys
from collections import OrderedDict
from functools import partial
import spconv.pytorch as spconv
import torch
import torch.nn as nn
import torch_scatter
from addict import Dict
try:
import flash_attn
except ImportError:
flash_attn = None
from grasp_gen.models.ptv3.serialization.default import encode
@torch.inference_mode()
def offset2bincount(offset):
return torch.diff(
offset, prepend=torch.tensor([0], device=offset.device, dtype=torch.long)
)
@torch.inference_mode()
def offset2batch(offset):
bincount = offset2bincount(offset)
return torch.arange(
len(bincount), device=offset.device, dtype=torch.long
).repeat_interleave(bincount)
@torch.inference_mode()
def batch2offset(batch):
return torch.cumsum(batch.bincount(), dim=0).long()
class Point(Dict):
"""
Point Structure of Pointcept
A Point (point cloud) in Pointcept is a dictionary that contains various properties of
a batched point cloud. The property with the following names have a specific definition
as follows:
- "coord": original coordinate of point cloud;
- "grid_coord": grid coordinate for specific grid size (related to GridSampling);
Point also support the following optional attributes:
- "offset": if not exist, initialized as batch size is 1;
- "batch": if not exist, initialized as batch size is 1;
- "feat": feature of point cloud, default input of model;
- "grid_size": Grid size of point cloud (related to GridSampling);
(related to Serialization)
- "serialized_depth": depth of serialization, 2 ** depth * grid_size describe the maximum of point cloud range;
- "serialized_code": a list of serialization codes;
- "serialized_order": a list of serialization order determined by code;
- "serialized_inverse": a list of inverse mapping determined by code;
(related to Sparsify: SpConv)
- "sparse_shape": Sparse shape for Sparse Conv Tensor;
- "sparse_conv_feat": SparseConvTensor init with information provide by Point;
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# If one of "offset" or "batch" do not exist, generate by the existing one
if "batch" not in self.keys() and "offset" in self.keys():
self["batch"] = offset2batch(self.offset)
elif "offset" not in self.keys() and "batch" in self.keys():
self["offset"] = batch2offset(self.batch)
def serialization(self, order="z", depth=None, shuffle_orders=False):
"""
Point Cloud Serialization
relay on ["grid_coord" or "coord" + "grid_size", "batch", "feat"]
"""
assert "batch" in self.keys()
if "grid_coord" not in self.keys():
# if you don't want to operate GridSampling in data augmentation,
# please add the following augmentation into your pipline:
# dict(type="Copy", keys_dict={"grid_size": 0.01}),
# (adjust `grid_size` to what your want)
assert {"grid_size", "coord"}.issubset(self.keys())
self["grid_coord"] = torch.div(
self.coord - self.coord.min(0)[0], self.grid_size, rounding_mode="trunc"
).int()
if depth is None:
# Adaptive measure the depth of serialization cube (length = 2 ^ depth)
depth = int(self.grid_coord.max()).bit_length()
self["serialized_depth"] = depth
# Maximum bit length for serialization code is 63 (int64)
assert depth * 3 + len(self.offset).bit_length() <= 63
# Here we follow OCNN and set the depth limitation to 16 (48bit) for the point position.
# Although depth is limited to less than 16, we can encode a 655.36^3 (2^16 * 0.01) meter^3
# cube with a grid size of 0.01 meter. We consider it is enough for the current stage.
# We can unlock the limitation by optimizing the z-order encoding function if necessary.
assert depth <= 16
# The serialization codes are arranged as following structures:
# [Order1 ([n]),
# Order2 ([n]),
# ...
# OrderN ([n])] (k, n)
code = [
encode(self.grid_coord, self.batch, depth, order=order_) for order_ in order
]
code = torch.stack(code)
order = torch.argsort(code)
inverse = torch.zeros_like(order).scatter_(
dim=1,
index=order,
src=torch.arange(0, code.shape[1], device=order.device).repeat(
code.shape[0], 1
),
)
if shuffle_orders:
perm = torch.randperm(code.shape[0])
code = code[perm]
order = order[perm]
inverse = inverse[perm]
self["serialized_code"] = code
self["serialized_order"] = order
self["serialized_inverse"] = inverse
def sparsify(self, pad=96):
"""
Point Cloud Serialization
Point cloud is sparse, here we use "sparsify" to specifically refer to
preparing "spconv.SparseConvTensor" for SpConv.
relay on ["grid_coord" or "coord" + "grid_size", "batch", "feat"]
pad: padding sparse for sparse shape.
"""
assert {"feat", "batch"}.issubset(self.keys())
if "grid_coord" not in self.keys():
# if you don't want to operate GridSampling in data augmentation,
# please add the following augmentation into your pipline:
# dict(type="Copy", keys_dict={"grid_size": 0.01}),
# (adjust `grid_size` to what your want)
assert {"grid_size", "coord"}.issubset(self.keys())
self["grid_coord"] = torch.div(
self.coord - self.coord.min(0)[0], self.grid_size, rounding_mode="trunc"
).int()
if "sparse_shape" in self.keys():
sparse_shape = self.sparse_shape
else:
sparse_shape = torch.add(
torch.max(self.grid_coord, dim=0).values, pad
).tolist()
sparse_conv_feat = spconv.SparseConvTensor(
features=self.feat,
indices=torch.cat(
[self.batch.unsqueeze(-1).int(), self.grid_coord.int()], dim=1
).contiguous(),
spatial_shape=sparse_shape,
batch_size=self.batch[-1].tolist() + 1,
)
self["sparse_shape"] = sparse_shape
self["sparse_conv_feat"] = sparse_conv_feat
class PointModule(nn.Module):
r"""PointModule
placeholder, all module subclass from this will take Point in PointSequential.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
class PointSequential(PointModule):
r"""A sequential container.
Modules will be added to it in the order they are passed in the constructor.
Alternatively, an ordered dict of modules can also be passed in.
"""
def __init__(self, *args, **kwargs):
super().__init__()
if len(args) == 1 and isinstance(args[0], OrderedDict):
for key, module in args[0].items():
self.add_module(key, module)
else:
for idx, module in enumerate(args):
self.add_module(str(idx), module)
for name, module in kwargs.items():
if sys.version_info < (3, 6):
raise ValueError("kwargs only supported in py36+")
if name in self._modules:
raise ValueError("name exists.")
self.add_module(name, module)
def __getitem__(self, idx):
if not (-len(self) <= idx < len(self)):
raise IndexError("index {} is out of range".format(idx))
if idx < 0:
idx += len(self)
it = iter(self._modules.values())
for i in range(idx):
next(it)
return next(it)
def __len__(self):
return len(self._modules)
def add(self, module, name=None):
if name is None:
name = str(len(self._modules))
if name in self._modules:
raise KeyError("name exists")
self.add_module(name, module)
def forward(self, input):
for k, module in self._modules.items():
# Point module
if isinstance(module, PointModule):
input = module(input)
# Spconv module
elif spconv.modules.is_spconv_module(module):
if isinstance(input, Point):
input.sparse_conv_feat = module(input.sparse_conv_feat)
input.feat = input.sparse_conv_feat.features
else:
input = module(input)
# PyTorch module
else:
if isinstance(input, Point):
input.feat = module(input.feat)
if "sparse_conv_feat" in input.keys():
input.sparse_conv_feat = input.sparse_conv_feat.replace_feature(
input.feat
)
elif isinstance(input, spconv.SparseConvTensor):
if input.indices.shape[0] != 0:
input = input.replace_feature(module(input.features))
else:
input = module(input)
return input
class PDNorm(PointModule):
def __init__(
self,
num_features,
norm_layer,
context_channels=256,
conditions=("ScanNet", "S3DIS", "Structured3D"),
decouple=True,
adaptive=False,
):
super().__init__()
self.conditions = conditions
self.decouple = decouple
self.adaptive = adaptive
if self.decouple:
self.norm = nn.ModuleList([norm_layer(num_features) for _ in conditions])
else:
self.norm = norm_layer
if self.adaptive:
self.modulation = nn.Sequential(
nn.SiLU(), nn.Linear(context_channels, 2 * num_features, bias=True)
)
def forward(self, point):
assert {"feat", "condition"}.issubset(point.keys())
if isinstance(point.condition, str):
condition = point.condition
else:
condition = point.condition[0]
if self.decouple:
assert condition in self.conditions
norm = self.norm[self.conditions.index(condition)]
else:
norm = self.norm
point.feat = norm(point.feat)
if self.adaptive:
assert "context" in point.keys()
shift, scale = self.modulation(point.context).chunk(2, dim=1)
point.feat = point.feat * (1.0 + scale) + shift
return point
class RPE(torch.nn.Module):
def __init__(self, patch_size, num_heads):
super().__init__()
self.patch_size = patch_size
self.num_heads = num_heads
self.pos_bnd = int((4 * patch_size) ** (1 / 3) * 2)
self.rpe_num = 2 * self.pos_bnd + 1
self.rpe_table = torch.nn.Parameter(torch.zeros(3 * self.rpe_num, num_heads))
torch.nn.init.trunc_normal_(self.rpe_table, std=0.02)
def forward(self, coord):
idx = (
coord.clamp(-self.pos_bnd, self.pos_bnd) # clamp into bnd
+ self.pos_bnd # relative position to positive index
+ torch.arange(3, device=coord.device) * self.rpe_num # x, y, z stride
)
out = self.rpe_table.index_select(0, idx.reshape(-1))
out = out.view(idx.shape + (-1,)).sum(3)
out = out.permute(0, 3, 1, 2) # (N, K, K, H) -> (N, H, K, K)
return out
class SerializedAttention(PointModule):
def __init__(
self,
channels,
num_heads,
patch_size,
qkv_bias=True,
qk_scale=None,
attn_drop=0.0,
proj_drop=0.0,
order_index=0,
enable_rpe=False,
enable_flash=True,
upcast_attention=True,
upcast_softmax=True,
):
super().__init__()
assert channels % num_heads == 0
self.channels = channels
self.num_heads = num_heads
self.scale = qk_scale or (channels // num_heads) ** -0.5
self.order_index = order_index
self.upcast_attention = upcast_attention
self.upcast_softmax = upcast_softmax
self.enable_rpe = enable_rpe
self.enable_flash = enable_flash
if enable_flash:
assert (
enable_rpe is False
), "Set enable_rpe to False when enable Flash Attention"
assert (
upcast_attention is False
), "Set upcast_attention to False when enable Flash Attention"
assert (
upcast_softmax is False
), "Set upcast_softmax to False when enable Flash Attention"
assert flash_attn is not None, "Make sure flash_attn is installed."
self.patch_size = patch_size
self.attn_drop = attn_drop
else:
# when disable flash attention, we still don't want to use mask
# consequently, patch size will auto set to the
# min number of patch_size_max and number of points
self.patch_size_max = patch_size
self.patch_size = 0
self.attn_drop = torch.nn.Dropout(attn_drop)
self.qkv = torch.nn.Linear(channels, channels * 3, bias=qkv_bias)
self.proj = torch.nn.Linear(channels, channels)
self.proj_drop = torch.nn.Dropout(proj_drop)
self.softmax = torch.nn.Softmax(dim=-1)
self.rpe = RPE(patch_size, num_heads) if self.enable_rpe else None
@torch.no_grad()
def get_rel_pos(self, point, order):
K = self.patch_size
rel_pos_key = f"rel_pos_{self.order_index}"
if rel_pos_key not in point.keys():
grid_coord = point.grid_coord[order]
grid_coord = grid_coord.reshape(-1, K, 3)
point[rel_pos_key] = grid_coord.unsqueeze(2) - grid_coord.unsqueeze(1)
return point[rel_pos_key]
@torch.no_grad()
def get_padding_and_inverse(self, point):
pad_key = "pad"
unpad_key = "unpad"
cu_seqlens_key = "cu_seqlens_key"
if (
pad_key not in point.keys()
or unpad_key not in point.keys()
or cu_seqlens_key not in point.keys()
):
offset = point.offset
bincount = offset2bincount(offset)
bincount_pad = (
torch.div(
bincount + self.patch_size - 1,
self.patch_size,
rounding_mode="trunc",
)
* self.patch_size
)
# only pad point when num of points larger than patch_size
mask_pad = bincount > self.patch_size
bincount_pad = ~mask_pad * bincount + mask_pad * bincount_pad
_offset = nn.functional.pad(offset, (1, 0))
_offset_pad = nn.functional.pad(torch.cumsum(bincount_pad, dim=0), (1, 0))
pad = torch.arange(_offset_pad[-1], device=offset.device)
unpad = torch.arange(_offset[-1], device=offset.device)
cu_seqlens = []
for i in range(len(offset)):
unpad[_offset[i] : _offset[i + 1]] += _offset_pad[i] - _offset[i]
if bincount[i] != bincount_pad[i]:
pad[
_offset_pad[i + 1]
- self.patch_size
+ (bincount[i] % self.patch_size) : _offset_pad[i + 1]
] = pad[
_offset_pad[i + 1]
- 2 * self.patch_size
+ (bincount[i] % self.patch_size) : _offset_pad[i + 1]
- self.patch_size
]
pad[_offset_pad[i] : _offset_pad[i + 1]] -= _offset_pad[i] - _offset[i]
cu_seqlens.append(
torch.arange(
_offset_pad[i],
_offset_pad[i + 1],
step=self.patch_size,
dtype=torch.int32,
device=offset.device,
)
)
point[pad_key] = pad
point[unpad_key] = unpad
point[cu_seqlens_key] = nn.functional.pad(
torch.concat(cu_seqlens), (0, 1), value=_offset_pad[-1]
)
return point[pad_key], point[unpad_key], point[cu_seqlens_key]
def forward(self, point):
if not self.enable_flash:
self.patch_size = min(
offset2bincount(point.offset).min().tolist(), self.patch_size_max
)
H = self.num_heads
K = self.patch_size
C = self.channels
pad, unpad, cu_seqlens = self.get_padding_and_inverse(point)
order = point.serialized_order[self.order_index][pad]
inverse = unpad[point.serialized_inverse[self.order_index]]
# padding and reshape feat and batch for serialized point patch
qkv = self.qkv(point.feat)[order]
if not self.enable_flash:
# encode and reshape qkv: (N', K, 3, H, C') => (3, N', H, K, C')
q, k, v = (
qkv.reshape(-1, K, 3, H, C // H).permute(2, 0, 3, 1, 4).unbind(dim=0)
)
# attn
if self.upcast_attention:
q = q.float()
k = k.float()
attn = (q * self.scale) @ k.transpose(-2, -1) # (N', H, K, K)
if self.enable_rpe:
attn = attn + self.rpe(self.get_rel_pos(point, order))
if self.upcast_softmax:
attn = attn.float()
attn = self.softmax(attn)
attn = self.attn_drop(attn).to(qkv.dtype)
feat = (attn @ v).transpose(1, 2).reshape(-1, C)
else:
feat = flash_attn.flash_attn_varlen_qkvpacked_func(
qkv.half().reshape(-1, 3, H, C // H),
cu_seqlens,
max_seqlen=self.patch_size,
dropout_p=self.attn_drop if self.training else 0,
softmax_scale=self.scale,
).reshape(-1, C)
feat = feat.to(qkv.dtype)
feat = feat[inverse]
# ffn
feat = self.proj(feat)
feat = self.proj_drop(feat)
point.feat = feat
return point
class MLP(nn.Module):
def __init__(
self,
in_channels,
hidden_channels=None,
out_channels=None,
act_layer=nn.GELU,
drop=0.0,
):
super().__init__()
out_channels = out_channels or in_channels
hidden_channels = hidden_channels or in_channels
self.fc1 = nn.Linear(in_channels, hidden_channels)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_channels, out_channels)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Block(PointModule):
def __init__(
self,
channels,
num_heads,
patch_size=48,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
attn_drop=0.0,
proj_drop=0.0,
drop_path=0.0,
norm_layer=nn.LayerNorm,
act_layer=nn.GELU,
pre_norm=True,
order_index=0,
cpe_indice_key=None,
enable_rpe=False,
enable_flash=True,
upcast_attention=True,
upcast_softmax=True,
):
super().__init__()
self.channels = channels
self.pre_norm = pre_norm
self.cpe = PointSequential(
spconv.SubMConv3d(
channels,
channels,
kernel_size=3,
bias=True,
indice_key=cpe_indice_key,
),
nn.Linear(channels, channels),
norm_layer(channels),
)
self.norm1 = PointSequential(norm_layer(channels))
self.attn = SerializedAttention(
channels=channels,
patch_size=patch_size,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=proj_drop,
order_index=order_index,
enable_rpe=enable_rpe,
enable_flash=enable_flash,
upcast_attention=upcast_attention,
upcast_softmax=upcast_softmax,
)
self.norm2 = PointSequential(norm_layer(channels))
self.mlp = PointSequential(
MLP(
in_channels=channels,
hidden_channels=int(channels * mlp_ratio),
out_channels=channels,
act_layer=act_layer,
drop=proj_drop,
)
)
self.drop_path = PointSequential(
DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
)
def forward(self, point: Point):
shortcut = point.feat
point = self.cpe(point)
point.feat = shortcut + point.feat
shortcut = point.feat
if self.pre_norm:
point = self.norm1(point)
point = self.drop_path(self.attn(point))
point.feat = shortcut + point.feat
if not self.pre_norm:
point = self.norm1(point)
shortcut = point.feat
if self.pre_norm:
point = self.norm2(point)
point = self.drop_path(self.mlp(point))
point.feat = shortcut + point.feat
if not self.pre_norm:
point = self.norm2(point)
point.sparse_conv_feat = point.sparse_conv_feat.replace_feature(point.feat)
return point
class SerializedPooling(PointModule):
def __init__(
self,
in_channels,
out_channels,
stride=2,
norm_layer=None,
act_layer=None,
reduce="max",
shuffle_orders=True,
traceable=True, # record parent and cluster
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
assert stride == 2 ** (math.ceil(stride) - 1).bit_length() # 2, 4, 8
# TODO: add support to grid pool (any stride)
self.stride = stride
assert reduce in ["sum", "mean", "min", "max"]
self.reduce = reduce
self.shuffle_orders = shuffle_orders
self.traceable = traceable
self.proj = nn.Linear(in_channels, out_channels)
if norm_layer is not None:
self.norm = PointSequential(norm_layer(out_channels))
if act_layer is not None:
self.act = PointSequential(act_layer())
def forward(self, point: Point):
pooling_depth = (math.ceil(self.stride) - 1).bit_length()
if pooling_depth > point.serialized_depth:
pooling_depth = 0
assert {
"serialized_code",
"serialized_order",
"serialized_inverse",
"serialized_depth",
}.issubset(
point.keys()
), "Run point.serialization() point cloud before SerializedPooling"
code = point.serialized_code >> pooling_depth * 3
code_, cluster, counts = torch.unique(
code[0],
sorted=True,
return_inverse=True,
return_counts=True,
)
# indices of point sorted by cluster, for torch_scatter.segment_csr
_, indices = torch.sort(cluster)
# index pointer for sorted point, for torch_scatter.segment_csr
idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
# head_indices of each cluster, for reduce attr e.g. code, batch
head_indices = indices[idx_ptr[:-1]]
# generate down code, order, inverse
code = code[:, head_indices]
order = torch.argsort(code)
inverse = torch.zeros_like(order).scatter_(
dim=1,
index=order,
src=torch.arange(0, code.shape[1], device=order.device).repeat(
code.shape[0], 1
),
)
if self.shuffle_orders:
perm = torch.randperm(code.shape[0])
code = code[perm]
order = order[perm]
inverse = inverse[perm]
# collect information
point_dict = Dict(
feat=torch_scatter.segment_csr(
self.proj(point.feat)[indices], idx_ptr, reduce=self.reduce
),
coord=torch_scatter.segment_csr(
point.coord[indices], idx_ptr, reduce="mean"
),
grid_coord=point.grid_coord[head_indices] >> pooling_depth,
serialized_code=code,
serialized_order=order,
serialized_inverse=inverse,
serialized_depth=point.serialized_depth - pooling_depth,
batch=point.batch[head_indices],
)
if "condition" in point.keys():
point_dict["condition"] = point.condition
if "context" in point.keys():
point_dict["context"] = point.context
if self.traceable:
point_dict["pooling_inverse"] = cluster
point_dict["pooling_parent"] = point
point = Point(point_dict)
if self.norm is not None:
point = self.norm(point)
if self.act is not None:
point = self.act(point)
point.sparsify()
return point
class SerializedUnpooling(PointModule):
def __init__(
self,
in_channels,
skip_channels,
out_channels,
norm_layer=None,
act_layer=None,
traceable=False, # record parent and cluster
):
super().__init__()
self.proj = PointSequential(nn.Linear(in_channels, out_channels))
self.proj_skip = PointSequential(nn.Linear(skip_channels, out_channels))
if norm_layer is not None:
self.proj.add(norm_layer(out_channels))
self.proj_skip.add(norm_layer(out_channels))
if act_layer is not None:
self.proj.add(act_layer())
self.proj_skip.add(act_layer())
self.traceable = traceable
def forward(self, point):
assert "pooling_parent" in point.keys()
assert "pooling_inverse" in point.keys()
parent = point.pop("pooling_parent")
inverse = point.pop("pooling_inverse")
point = self.proj(point)
parent = self.proj_skip(parent)
parent.feat = parent.feat + point.feat[inverse]
if self.traceable:
parent["unpooling_parent"] = point
return parent
class Embedding(PointModule):
def __init__(
self,
in_channels,
embed_channels,
norm_layer=None,
act_layer=None,
):
super().__init__()
self.in_channels = in_channels
self.embed_channels = embed_channels
# TODO: check remove spconv
self.stem = PointSequential(
conv=spconv.SubMConv3d(
in_channels,
embed_channels,
kernel_size=5,
padding=1,
bias=False,
indice_key="stem",
)
)
if norm_layer is not None:
self.stem.add(norm_layer(embed_channels), name="norm")
if act_layer is not None:
self.stem.add(act_layer(), name="act")
def forward(self, point: Point):
point = self.stem(point)
return point
class PointTransformerV3(PointModule):
def __init__(
self,
in_channels=6,
order=("z", "z-trans", "hilbert", "hilbert-trans"),
stride=(2, 2, 2, 2),
enc_depths=(2, 2, 2, 6, 2),
enc_channels=(32, 64, 128, 256, 512),
enc_num_head=(2, 4, 8, 16, 32),
enc_patch_size=(1024, 1024, 1024, 1024, 1024),
dec_depths=(2, 2, 2, 2),
dec_channels=(64, 64, 128, 256),
dec_num_head=(4, 4, 8, 16),
dec_patch_size=(1024, 1024, 1024, 1024),
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
attn_drop=0.0,
proj_drop=0.0,
drop_path=0.3,
pre_norm=True,
shuffle_orders=True,
enable_rpe=False,
enable_flash=True,
upcast_attention=False,
upcast_softmax=False,
cls_mode=False,
pdnorm_bn=False,
pdnorm_ln=False,
pdnorm_decouple=True,
pdnorm_adaptive=False,
pdnorm_affine=True,
pdnorm_conditions=("ScanNet", "S3DIS", "Structured3D"),
):
super().__init__()
self.num_stages = len(enc_depths)
self.order = [order] if isinstance(order, str) else order
self.cls_mode = cls_mode
self.shuffle_orders = shuffle_orders
assert self.num_stages == len(stride) + 1
assert self.num_stages == len(enc_depths)
assert self.num_stages == len(enc_channels)
assert self.num_stages == len(enc_num_head)
assert self.num_stages == len(enc_patch_size)
assert self.cls_mode or self.num_stages == len(dec_depths) + 1
assert self.cls_mode or self.num_stages == len(dec_channels) + 1
assert self.cls_mode or self.num_stages == len(dec_num_head) + 1
assert self.cls_mode or self.num_stages == len(dec_patch_size) + 1
# norm layers
if pdnorm_bn:
bn_layer = partial(
PDNorm,
norm_layer=partial(
nn.BatchNorm1d, eps=1e-3, momentum=0.01, affine=pdnorm_affine
),
conditions=pdnorm_conditions,
decouple=pdnorm_decouple,
adaptive=pdnorm_adaptive,
)
else:
bn_layer = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
if pdnorm_ln:
ln_layer = partial(
PDNorm,
norm_layer=partial(nn.LayerNorm, elementwise_affine=pdnorm_affine),
conditions=pdnorm_conditions,
decouple=pdnorm_decouple,
adaptive=pdnorm_adaptive,
)
else:
ln_layer = nn.LayerNorm
# activation layers
act_layer = nn.GELU
self.embedding = Embedding(
in_channels=in_channels,
embed_channels=enc_channels[0],
norm_layer=bn_layer,
act_layer=act_layer,
)
# encoder
enc_drop_path = [
x.item() for x in torch.linspace(0, drop_path, sum(enc_depths))
]
self.enc = PointSequential()
for s in range(self.num_stages):
enc_drop_path_ = enc_drop_path[
sum(enc_depths[:s]) : sum(enc_depths[: s + 1])
]
enc = PointSequential()
if s > 0:
enc.add(
SerializedPooling(
in_channels=enc_channels[s - 1],
out_channels=enc_channels[s],
stride=stride[s - 1],
norm_layer=bn_layer,
act_layer=act_layer,
),
name="down",
)
for i in range(enc_depths[s]):
enc.add(
Block(
channels=enc_channels[s],
num_heads=enc_num_head[s],
patch_size=enc_patch_size[s],
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=proj_drop,
drop_path=enc_drop_path_[i],
norm_layer=ln_layer,
act_layer=act_layer,
pre_norm=pre_norm,
order_index=i % len(self.order),
cpe_indice_key=f"stage{s}",
enable_rpe=enable_rpe,
enable_flash=enable_flash,
upcast_attention=upcast_attention,
upcast_softmax=upcast_softmax,
),
name=f"block{i}",
)
if len(enc) != 0:
self.enc.add(module=enc, name=f"enc{s}")
# decoder
if not self.cls_mode:
dec_drop_path = [
x.item() for x in torch.linspace(0, drop_path, sum(dec_depths))
]
self.dec = PointSequential()
dec_channels = list(dec_channels) + [enc_channels[-1]]
for s in reversed(range(self.num_stages - 1)):
dec_drop_path_ = dec_drop_path[
sum(dec_depths[:s]) : sum(dec_depths[: s + 1])
]
dec_drop_path_.reverse()
dec = PointSequential()
dec.add(
SerializedUnpooling(
in_channels=dec_channels[s + 1],
skip_channels=enc_channels[s],
out_channels=dec_channels[s],
norm_layer=bn_layer,
act_layer=act_layer,
),
name="up",
)
for i in range(dec_depths[s]):
dec.add(
Block(
channels=dec_channels[s],
num_heads=dec_num_head[s],
patch_size=dec_patch_size[s],
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=proj_drop,
drop_path=dec_drop_path_[i],
norm_layer=ln_layer,
act_layer=act_layer,
pre_norm=pre_norm,
order_index=i % len(self.order),
cpe_indice_key=f"stage{s}",
enable_rpe=enable_rpe,
enable_flash=enable_flash,
upcast_attention=upcast_attention,
upcast_softmax=upcast_softmax,
),
name=f"block{i}",
)
self.dec.add(module=dec, name=f"dec{s}")
def forward(self, data_dict):
"""
A data_dict is a dictionary containing properties of a batched point cloud.
It should contain the following properties for PTv3:
1. "feat": feature of point cloud
2. "grid_coord": discrete coordinate after grid sampling (voxelization) or "coord" + "grid_size"
3. "offset" or "batch": https://github.com/Pointcept/Pointcept?tab=readme-ov-file#offset
"""
point = Point(data_dict)
point.serialization(order=self.order, shuffle_orders=self.shuffle_orders)
point.sparsify()
point = self.embedding(point)
point = self.enc(point)
if not self.cls_mode:
point = self.dec(point)
else:
point.feat = torch_scatter.segment_csr(
src=point.feat,
indptr=nn.functional.pad(point.offset, (1, 0)),
reduce="mean",
)
point = point.feat
return point
================================================
FILE: grasp_gen/models/ptv3/serialization/__init__.py
================================================
# Copyright (c) 2025, 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.
from .default import (
decode,
encode,
hilbert_decode,
hilbert_encode,
z_order_decode,
z_order_encode,
)
================================================
FILE: grasp_gen/models/ptv3/serialization/default.py
================================================
import torch
from grasp_gen.models.ptv3.serialization.hilbert import decode as hilbert_decode_
from grasp_gen.models.ptv3.serialization.hilbert import encode as hilbert_encode_
from grasp_gen.models.ptv3.serialization.z_order import key2xyz as z_order_decode_
from grasp_gen.models.ptv3.serialization.z_order import xyz2key as z_order_encode_
@torch.inference_mode()
def encode(grid_coord, batch=None, depth=16, order="z"):
assert order in {"z", "z-trans", "hilbert", "hilbert-trans"}
if order == "z":
code = z_order_encode(grid_coord, depth=depth)
elif order == "z-trans":
code = z_order_encode(grid_coord[:, [1, 0, 2]], depth=depth)
elif order == "hilbert":
code = hilbert_encode(grid_coord, depth=depth)
elif order == "hilbert-trans":
code = hilbert_encode(grid_coord[:, [1, 0, 2]], depth=depth)
else:
raise NotImplementedError
if batch is not None:
batch = batch.long()
code = batch << depth * 3 | code
return code
@torch.inference_mode()
def decode(code, depth=16, order="z"):
assert order in {"z", "hilbert"}
batch = code >> depth * 3
code = code & ((1 << depth * 3) - 1)
if order == "z":
grid_coord = z_order_decode(code, depth=depth)
elif order == "hilbert":
grid_coord = hilbert_decode(code, depth=depth)
else:
raise NotImplementedError
return grid_coord, batch
def z_order_encode(grid_coord: torch.Tensor, depth: int = 16):
x, y, z = grid_coord[:, 0].long(), grid_coord[:, 1].long(), grid_coord[:, 2].long()
# we block the support to batch, maintain batched code in Point class
code = z_order_encode_(x, y, z, b=None, depth=depth)
return code
def z_order_decode(code: torch.Tensor, depth):
x, y, z = z_order_decode_(code, depth=depth)
grid_coord = torch.stack([x, y, z], dim=-1) # (N, 3)
return grid_coord
def hilbert_encode(grid_coord: torch.Tensor, depth: int = 16):
return hilbert_encode_(grid_coord, num_dims=3, num_bits=depth)
def hilbert_decode(code: torch.Tensor, depth: int = 16):
return hilbert_decode_(code, num_dims=3, num_bits=depth)
================================================
FILE: grasp_gen/models/ptv3/serialization/hilbert.py
================================================
"""
Hilbert Order
Modified from https://github.com/PrincetonLIPS/numpy-hilbert-curve
Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com), Kaixin Xu
Please cite our work if the code is helpful to you.
"""
import torch
def right_shift(binary, k=1, axis=-1):
"""Right shift an array of binary values.
Parameters:
-----------
binary: An ndarray of binary values.
k: The number of bits to shift. Default 1.
axis: The axis along which to shift. Default -1.
Returns:
--------
Returns an ndarray with zero prepended and the ends truncated, along
whatever axis was specified."""
# If we're shifting the whole thing, just return zeros.
if binary.shape[axis] <= k:
return torch.zeros_like(binary)
# Determine the padding pattern.
# padding = [(0,0)] * len(binary.shape)
# padding[axis] = (k,0)
# Determine the slicing pattern to eliminate just the last one.
slicing = [slice(None)] * len(binary.shape)
slicing[axis] = slice(None, -k)
shifted = torch.nn.functional.pad(
binary[tuple(slicing)], (k, 0), mode="constant", value=0
)
return shifted
def binary2gray(binary, axis=-1):
"""Convert an array of binary values into Gray codes.
This uses the classic X ^ (X >> 1) trick to compute the Gray code.
Parameters:
-----------
binary: An ndarray of binary values.
axis: The axis along which to compute the gray code. Default=-1.
Returns:
--------
Returns an ndarray of Gray codes.
"""
shifted = right_shift(binary, axis=axis)
# Do the X ^ (X >> 1) trick.
gray = torch.logical_xor(binary, shifted)
return gray
def gray2binary(gray, axis=-1):
"""Convert an array of Gray codes back into binary values.
Parameters:
-----------
gray: An ndarray of gray codes.
axis: The axis along which to perform Gray decoding. Default=-1.
Returns:
--------
Returns an ndarray of binary values.
"""
# Loop the log2(bits) number of times necessary, with shift and xor.
shift = 2 ** (torch.Tensor([gray.shape[axis]]).log2().ceil().int() - 1)
while shift > 0:
gray = torch.logical_xor(gray, right_shift(gray, shift))
shift = torch.div(shift, 2, rounding_mode="floor")
return gray
def encode(locs, num_dims, num_bits):
"""Decode an array of locations in a hypercube into a Hilbert integer.
This is a vectorized-ish version of the Hilbert curve implementation by John
Skilling as described in:
Skilling, J. (2004, April). Programming the Hilbert curve. In AIP Conference
Proceedings (Vol. 707, No. 1, pp. 381-387). American Institute of Physics.
Params:
-------
locs - An ndarray of locations in a hypercube of num_dims dimensions, in
which each dimension runs from 0 to 2**num_bits-1. The shape can
be arbitrary, as long as the last dimension of the same has size
num_dims.
num_dims - The dimensionality of the hypercube. Integer.
num_bits - The number of bits for each dimension. Integer.
Returns:
--------
The output is an ndarray of uint64 integers with the same shape as the
input, excluding the last dimension, which needs to be num_dims.
"""
# Keep around the original shape for later.
orig_shape = locs.shape
bitpack_mask = 1 << torch.arange(0, 8).to(locs.device)
bitpack_mask_rev = bitpack_mask.flip(-1)
if orig_shape[-1] != num_dims:
raise ValueError(
"""
The shape of locs was surprising in that the last dimension was of size
%d, but num_dims=%d. These need to be equal.
"""
% (orig_shape[-1], num_dims)
)
if num_dims * num_bits > 63:
raise ValueError(
"""
num_dims=%d and num_bits=%d for %d bits total, which can't be encoded
into a int64. Are you sure you need that many points on your Hilbert
curve?
"""
% (num_dims, num_bits, num_dims * num_bits)
)
# Treat the location integers as 64-bit unsigned and then split them up into
# a sequence of uint8s. Preserve the association by dimension.
locs_uint8 = locs.long().view(torch.uint8).reshape((-1, num_dims, 8)).flip(-1)
# Now turn these into bits and truncate to num_bits.
gray = (
locs_uint8.unsqueeze(-1)
.bitwise_and(bitpack_mask_rev)
.ne(0)
.byte()
.flatten(-2, -1)[..., -num_bits:]
)
# Run the decoding process the other way.
# Iterate forwards through the bits.
for bit in range(0, num_bits):
# Iterate forwards through the dimensions.
for dim in range(0, num_dims):
# Identify which ones have this bit active.
mask = gray[:, dim, bit]
# Where this bit is on, invert the 0 dimension for lower bits.
gray[:, 0, bit + 1 :] = torch.logical_xor(
gray[:, 0, bit + 1 :], mask[:, None]
)
# Where the bit is off, exchange the lower bits with the 0 dimension.
to_flip = torch.logical_and(
torch.logical_not(mask[:, None]).repeat(1, gray.shape[2] - bit - 1),
torch.logical_xor(gray[:, 0, bit + 1 :], gray[:, dim, bit + 1 :]),
)
gray[:, dim, bit + 1 :] = torch.logical_xor(
gray[:, dim, bit + 1 :], to_flip
)
gray[:, 0, bit + 1 :] = torch.logical_xor(gray[:, 0, bit + 1 :], to_flip)
# Now flatten out.
gray = gray.swapaxes(1, 2).reshape((-1, num_bits * num_dims))
# Convert Gray back to binary.
hh_bin = gray2binary(gray)
# Pad back out to 64 bits.
extra_dims = 64 - num_bits * num_dims
padded = torch.nn.functional.pad(hh_bin, (extra_dims, 0), "constant", 0)
# Convert binary values into uint8s.
hh_uint8 = (
(padded.flip(-1).reshape((-1, 8, 8)) * bitpack_mask)
.sum(2)
.squeeze()
.type(torch.uint8)
)
# Convert uint8s into uint64s.
hh_uint64 = hh_uint8.view(torch.int64).squeeze()
return hh_uint64
def decode(hilberts, num_dims, num_bits):
"""Decode an array of Hilbert integers into locations in a hypercube.
This is a vectorized-ish version of the Hilbert curve implementation by John
Skilling as described in:
Skilling, J. (2004, April). Programming the Hilbert curve. In AIP Conference
Proceedings (Vol. 707, No. 1, pp. 381-387). American Institute of Physics.
Params:
-------
hilberts - An ndarray of Hilbert integers. Must be an integer dtype and
cannot have fewer bits than num_dims * num_bits.
num_dims - The dimensionality of the hypercube. Integer.
num_bits - The number of bits for each dimension. Integer.
Returns:
--------
The output is an ndarray of unsigned integers with the same shape as hilberts
but with an additional dimension of size num_dims.
"""
if num_dims * num_bits > 64:
raise ValueError(
"""
num_dims=%d and num_bits=%d for %d bits total, which can't be encoded
into a uint64. Are you sure you need that many points on your Hilbert
curve?
"""
% (num_dims, num_bits)
)
# Handle the case where we got handed a naked integer.
hilberts = torch.atleast_1d(hilberts)
# Keep around the shape for later.
orig_shape = hilberts.shape
bitpack_mask = 2 ** torch.arange(0, 8).to(hilberts.device)
bitpack_mask_rev = bitpack_mask.flip(-1)
# Treat each of the hilberts as a s equence of eight uint8.
# This treats all of the inputs as uint64 and makes things uniform.
hh_uint8 = (
hilberts.ravel().type(torch.int64).view(torch.uint8).reshape((-1, 8)).flip(-1)
)
# Turn these lists of uints into lists of bits and then truncate to the size
# we actually need for using Skilling's procedure.
hh_bits = (
hh_uint8.unsqueeze(-1)
.bitwise_and(bitpack_mask_rev)
.ne(0)
.byte()
.flatten(-2, -1)[:, -num_dims * num_bits :]
)
# Take the sequence of bits and Gray-code it.
gray = binary2gray(hh_bits)
# There has got to be a better way to do this.
# I could index them differently, but the eventual packbits likes it this way.
gray = gray.reshape((-1, num_bits, num_dims)).swapaxes(1, 2)
# Iterate backwards through the bits.
for bit in range(num_bits - 1, -1, -1):
# Iterate backwards through the dimensions.
for dim in range(num_dims - 1, -1, -1):
# Identify which ones have this bit active.
mask = gray[:, dim, bit]
# Where this bit is on, invert the 0 dimension for lower bits.
gray[:, 0, bit + 1 :] = torch.logical_xor(
gray[:, 0, bit + 1 :], mask[:, None]
)
# Where the bit is off, exchange the lower bits with the 0 dimension.
to_flip = torch.logical_and(
torch.logical_not(mask[:, None]),
torch.logical_xor(gray[:, 0, bit + 1 :], gray[:, dim, bit + 1 :]),
)
gray[:, dim, bit + 1 :] = torch.logical_xor(
gray[:, dim, bit + 1 :], to_flip
)
gray[:, 0, bit + 1 :] = torch.logical_xor(gray[:, 0, bit + 1 :], to_flip)
# Pad back out to 64 bits.
extra_dims = 64 - num_bits
padded = torch.nn.functional.pad(gray, (extra_dims, 0), "constant", 0)
# Now chop these up into blocks of 8.
locs_chopped = padded.flip(-1).reshape((-1, num_dims, 8, 8))
# Take those blocks and turn them unto uint8s.
# from IPython import embed; embed()
locs_uint8 = (locs_chopped * bitpack_mask).sum(3).squeeze().type(torch.uint8)
# Finally, treat these as uint64s.
flat_locs = locs_uint8.view(torch.int64)
# Return them in the expected shape.
return flat_locs.reshape((*orig_shape, num_dims))
================================================
FILE: grasp_gen/models/ptv3/serialization/z_order.py
================================================
# --------------------------------------------------------
# Octree-based Sparse Convolutional Neural Networks
# Copyright (c) 2022 Peng-Shuai Wang
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng-Shuai Wang
# --------------------------------------------------------
from typing import Optional, Union
import torch
class KeyLUT:
def __init__(self):
r256 = torch.arange(256, dtype=torch.int64)
r512 = torch.arange(512, dtype=torch.int64)
zero = torch.zeros(256, dtype=torch.int64)
device = torch.device("cpu")
self._encode = {
device: (
self.xyz2key(r256, zero, zero, 8),
self.xyz2key(zero, r256, zero, 8),
self.xyz2key(zero, zero, r256, 8),
)
}
self._decode = {device: self.key2xyz(r512, 9)}
def encode_lut(self, device=torch.device("cpu")):
if device not in self._encode:
cpu = torch.device("cpu")
self._encode[device] = tuple(e.to(device) for e in self._encode[cpu])
return self._encode[device]
def decode_lut(self, device=torch.device("cpu")):
if device not in self._decode:
cpu = torch.device("cpu")
self._decode[device] = tuple(e.to(device) for e in self._decode[cpu])
return self._decode[device]
def xyz2key(self, x, y, z, depth):
key = torch.zeros_like(x)
for i in range(depth):
mask = 1 << i
key = (
key
| ((x & mask) << (2 * i + 2))
| ((y & mask) << (2 * i + 1))
| ((z & mask) << (2 * i + 0))
)
return key
def key2xyz(self, key, depth):
x = torch.zeros_like(key)
y = torch.zeros_like(key)
z = torch.zeros_like(key)
for i in range(depth):
x = x | ((key & (1 << (3 * i + 2))) >> (2 * i + 2))
y = y | ((key & (1 << (3 * i + 1))) >> (2 * i + 1))
z = z | ((key & (1 << (3 * i + 0))) >> (2 * i + 0))
return x, y, z
_key_lut = KeyLUT()
def xyz2key(
x: torch.Tensor,
y: torch.Tensor,
z: torch.Tensor,
b: Optional[Union[torch.Tensor, int]] = None,
depth: int = 16,
):
r"""Encodes :attr:`x`, :attr:`y`, :attr:`z` coordinates to the shuffled keys
based on pre-computed look up tables. The speed of this function is much
faster than the method based on for-loop.
Args:
x (torch.Tensor): The x coordinate.
y (torch.Tensor): The y coordinate.
z (torch.Tensor): The z coordinate.
b (torch.Tensor or int): The batch index of the coordinates, and should be
smaller than 32768. If :attr:`b` is :obj:`torch.Tensor`, the size of
:attr:`b` must be the same as :attr:`x`, :attr:`y`, and :attr:`z`.
depth (int): The depth of the shuffled key, and must be smaller than 17 (< 17).
"""
EX, EY, EZ = _key_lut.encode_lut(x.device)
x, y, z = x.long(), y.long(), z.long()
mask = 255 if depth > 8 else (1 << depth) - 1
key = EX[x & mask] | EY[y & mask] | EZ[z & mask]
if depth > 8:
mask = (1 << (depth - 8)) - 1
key16 = EX[(x >> 8) & mask] | EY[(y >> 8) & mask] | EZ[(z >> 8) & mask]
key = key16 << 24 | key
if b is not None:
b = b.long()
key = b << 48 | key
return key
def key2xyz(key: torch.Tensor, depth: int = 16):
r"""Decodes the shuffled key to :attr:`x`, :attr:`y`, :attr:`z` coordinates
and the batch index based on pre-computed look up tables.
Args:
key (torch.Tensor): The shuffled key.
depth (int): The depth of the shuffled key, and must be smaller than 17 (< 17).
"""
DX, DY, DZ = _key_lut.decode_lut(key.device)
x, y, z = torch.zeros_like(key), torch.zeros_like(key), torch.zeros_like(key)
b = key >> 48
key = key & ((1 << 48) - 1)
n = (depth + 2) // 3
for i in range(n):
k = key >> (i * 9) & 511
x = x | (DX[k] << (i * 3))
y = y | (DY[k] << (i * 3))
z = z | (DZ[k] << (i * 3))
return x, y, z, b
================================================
FILE: grasp_gen/models/vit.py
================================================
# Copyright (c) 2025, 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.
"""Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
- https://arxiv.org/abs/2106.10270
`FlexiViT: One Model for All Patch Sizes`
- https://arxiv.org/abs/2212.08013
The official jax code is released and available at
* https://github.com/google-research/vision_transformer
* https://github.com/google-research/big_vision
Acknowledgments:
* The paper authors for releasing code and weights, thanks!
* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch
* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
Hacked together by / Copyright 2020, Ross Wightman
"""
import logging
import math
from collections import OrderedDict
from functools import partial
from typing import Callable, List, Optional, Sequence, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from timm.layers import (
AttentionPoolLatent,
DropPath,
Mlp,
PatchDropout,
PatchEmbed,
RmsNorm,
SwiGLUPacked,
lecun_normal_,
resample_abs_pos_embed,
resample_patch_embed,
trunc_normal_,
use_fused_attn,
)
from timm.models._manipulate import adapt_input_conv, checkpoint_seq, named_apply
from torch.jit import Final
_logger = logging.getLogger(__name__)
from timm.layers.weight_init import trunc_normal_tf_
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class Attention(nn.Module):
fused_attn: Final[bool]
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
qk_norm=False,
attn_drop=0.0,
proj_drop=0.0,
norm_layer=nn.LayerNorm,
):
super().__init__()
assert dim % num_heads == 0, "dim should be divisible by num_heads"
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim**-0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B, N, 3, self.num_heads, self.head_dim)
.permute(2, 0, 3, 1, 4)
)
q, k, v = qkv.unbind(0)
q, k = self.q_norm(q), self.k_norm(k)
if self.fused_attn:
x = F.scaled_dot_product_attention(
q,
k,
v,
dropout_p=self.attn_drop.p if self.training else 0.0,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
return x.mul_(self.gamma) if self.inplace else x * self.gamma
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.0,
qkv_bias=False,
qk_norm=False,
proj_drop=0.0,
attn_drop=0.0,
init_values=None,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
mlp_layer=Mlp,
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
attn_drop=attn_drop,
proj_drop=proj_drop,
norm_layer=norm_layer,
)
self.ls1 = (
LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
)
self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = mlp_layer(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.ls2 = (
LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
)
self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
def forward(self, x):
x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
class VisionTransformer(nn.Module):
"""Vision Transformer
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
- https://arxiv.org/abs/2010.11929
"""
dynamic_img_size: Final[bool]
def __init__(
self,
img_size: Union[int, Tuple[int, int]] = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: str = "token",
# embed_dim: int = 768,
embed_dim: int = 640,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
qk_norm: bool = False,
init_values: Optional[float] = None,
class_token: bool = True,
no_embed_class: bool = False,
reg_tokens: int = 0,
pre_norm: bool = False,
fc_norm: Optional[bool] = None,
dynamic_img_size: bool = False,
dynamic_img_pad: bool = False,
drop_rate: float = 0.0,
pos_drop_rate: float = 0.0,
patch_drop_rate: float = 0.0,
proj_drop_rate: float = 0.0,
attn_drop_rate: float = 0.0,
drop_path_rate: float = 0.0,
weight_init: str = "",
embed_layer: Callable = PatchEmbed,
norm_layer: Optional[Callable] = None,
act_layer: Optional[Callable] = None,
block_fn: Callable = Block,
mlp_layer: Callable = Mlp,
):
"""
Args:
img_size: Input image size.
patch_size: Patch size.
in_chans: Number of image input channels.
num_classes: Mumber of classes for classification head.
global_pool: Type of global pooling for final sequence (default: 'token').
embed_dim: Transformer embedding dimension.
depth: Depth of transformer.
num_heads: Number of attention heads.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: Enable bias for qkv projections if True.
init_values: Layer-scale init values (layer-scale enabled if not None).
class_token: Use class token.
no_embed_class: Don't include position embeddings for class (or reg) tokens.
reg_tokens: Number of register tokens.
fc_norm: Pre head norm after pool (instead of before), if None, enabled when global_pool == 'avg'.
drop_rate: Head dropout rate.
pos_drop_rate: Position embedding dropout rate.
attn_drop_rate: Attention dropout rate.
drop_path_rate: Stochastic depth rate.
weight_init: Weight initialization scheme.
embed_layer: Patch embedding layer.
norm_layer: Normalization layer.
act_layer: MLP activation layer.
block_fn: Transformer block layer.
"""
super().__init__()
assert global_pool in ("", "avg", "token", "map")
assert class_token or global_pool != "token"
use_fc_norm = global_pool == "avg" if fc_norm is None else fc_norm
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
act_layer = act_layer or nn.GELU
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.embed_dim = (
embed_dim # num_features for consistency with other models
)
self.num_prefix_tokens = 1 if class_token else 0
self.num_prefix_tokens += reg_tokens
self.num_reg_tokens = reg_tokens
self.has_class_token = class_token
self.no_embed_class = (
no_embed_class # don't embed prefix positions (includes reg)
)
self.dynamic_img_size = dynamic_img_size
self.grad_checkpointing = False
embed_args = {}
if dynamic_img_size:
# flatten deferred until after pos embed
embed_args.update(dict(strict_img_size=False, output_fmt="NHWC"))
self.patch_embed = embed_layer(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
bias=not pre_norm, # disable bias if pre-norm is used (e.g. CLIP)
dynamic_img_pad=dynamic_img_pad,
**embed_args,
)
num_patches = self.patch_embed.num_patches
self.cls_token = (
nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
)
self.reg_token = (
nn.Parameter(torch.zeros(1, reg_tokens, embed_dim)) if reg_tokens else None
)
embed_len = (
num_patches if no_embed_class else num_patches + self.num_prefix_tokens
)
self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * 0.02)
self.pos_drop = nn.Dropout(p=pos_drop_rate)
if patch_drop_rate > 0:
self.patch_drop = PatchDropout(
patch_drop_rate,
num_prefix_tokens=self.num_prefix_tokens,
)
else:
self.patch_drop = nn.Identity()
self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
dpr = [
x.item() for x in torch.linspace(0, drop_path_rate, depth)
] # stochastic depth decay rule
self.blocks = nn.Sequential(
*[
block_fn(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
init_values=init_values,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=act_layer,
mlp_layer=mlp_layer,
)
for i in range(depth)
]
)
self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
# Classifier Head
if global_pool == "map":
self.attn_pool = AttentionPoolLatent(
embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
norm_layer=norm_layer,
)
else:
self.attn_pool = None
self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
self.head_drop = nn.Dropout(drop_rate)
self.head = (
nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
)
self.attention_pool = None
if weight_init != "skip":
self.init_weights(weight_init)
def init_weights(self, mode=""):
assert mode in ("jax", "jax_nlhb", "moco", "")
head_bias = -math.log(self.num_classes) if "nlhb" in mode else 0.0
trunc_normal_(self.pos_embed, std=0.02)
if self.cls_token is not None:
nn.init.normal_(self.cls_token, std=1e-6)
named_apply(get_init_weights_vit(mode, head_bias), self)
def _init_weights(self, m):
# this fn left here for compat with downstream users
init_weights_vit_timm(m)
@torch.jit.ignore()
def load_pretrained(self, checkpoint_path, prefix=""):
_load_weights(self, checkpoint_path, prefix)
@torch.jit.ignore
def no_weight_decay(self):
return {"pos_embed", "cls_token", "dist_token"}
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r"^cls_token|pos_embed|patch_embed", # stem and embed
blocks=[(r"^blocks\.(\d+)", None), (r"^norm", (99999,))],
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes: int, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ("", "avg", "token")
self.global_pool = global_pool
self.head = (
nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
)
def _pos_embed(self, x):
if self.dynamic_img_size:
B, H, W, C = x.shape
pos_embed = resample_abs_pos_embed(
self.pos_embed,
(H, W),
num_prefix_tokens=0 if self.no_embed_class else self.num_prefix_tokens,
)
x = x.view(B, -1, C)
else:
pos_embed = self.pos_embed
to_cat = []
if self.cls_token is not None:
to_cat.append(self.cls_token.expand(x.shape[0], -1, -1))
if self.reg_token is not None:
to_cat.append(self.reg_token.expand(x.shape[0], -1, -1))
if self.no_embed_class:
# deit-3, updated JAX (big vision)
# position embedding does not overlap with class token, add then concat
x = x + pos_embed
if to_cat:
x = torch.cat(to_cat + [x], dim=1)
else:
# original timm, JAX, and deit vit impl
# pos_embed has entry for class token, concat then add
if to_cat:
x = torch.cat(to_cat + [x], dim=1)
x = x + pos_embed
return self.pos_drop(x)
def _intermediate_layers(
self,
x: torch.Tensor,
n: Union[int, Sequence] = 1,
):
outputs, num_blocks = [], len(self.blocks)
take_indices = set(
range(num_blocks - n, num_blocks) if isinstance(n, int) else n
)
# forward pass
x = self.patch_embed(x)
x = self._pos_embed(x)
x = self.patch_drop(x)
x = self.norm_pre(x)
for i, blk in enumerate(self.blocks):
x = blk(x)
if i in take_indices:
outputs.append(x)
return outputs
def get_intermediate_layers(
self,
x: torch.Tensor,
n: Union[int, Sequence] = 1,
reshape: bool = False,
return_prefix_tokens: bool = False,
norm: bool = False,
) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
"""Intermediate layer accessor (NOTE: This is a WIP experiment).
Inspired by DINO / DINOv2 interface
"""
# take last n blocks if n is an int, if in is a sequence, select by matching indices
outputs = self._intermediate_layers(x, n)
if norm:
outputs = [self.norm(out) for out in outputs]
prefix_tokens = [out[:, 0 : self.num_prefix_tokens] for out in outputs]
outputs = [out[:, self.num_prefix_tokens :] for out in outputs]
if reshape:
grid_size = self.patch_embed.grid_size
outputs = [
out.reshape(x.shape[0], grid_size[0], grid_size[1], -1)
.permute(0, 3, 1, 2)
.contiguous()
for out in outputs
]
if return_prefix_tokens:
return tuple(zip(outputs, prefix_tokens))
return tuple(outputs)
def forward_features(self, x):
# x: img B ,S, H , W , C
x = x.permute(0, 1, 4, 2, 3)
B, S = x.shape[0], x.shape[1]
x = x.flatten(0, 1) # bs, c, h, w
x = self.patch_embed(x) # bs, num_patch, embed_dim
x = self._pos_embed(x)
x = x.reshape(B, S, x.shape[1], x.shape[2]).reshape(B, -1, x.shape[-1])
x = self.patch_drop(x)
x = self.norm_pre(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.attn_pool is not None:
x = self.attn_pool(x)
elif self.attention_pool is not None:
x = self.attention_pool(x)
elif self.global_pool == "avg":
x = x[:, self.num_prefix_tokens :].mean(dim=1)
elif self.global_pool:
x = x[:, 0] # class token
x = self.fc_norm(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def init_weights_vit_timm(module: nn.Module, name: str = ""):
"""ViT weight initialization, original timm impl (for reproducibility)"""
if isinstance(module, nn.Linear):
trunc_normal_(module.weight, std=0.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif hasattr(module, "init_weights"):
module.init_weights()
def init_weights_vit_jax(module: nn.Module, name: str = "", head_bias: float = 0.0):
"""ViT weight initialization, matching JAX (Flax) impl"""
if isinstance(module, nn.Linear):
if name.startswith("head"):
nn.init.zeros_(module.weight)
nn.init.constant_(module.bias, head_bias)
else:
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
(
nn.init.normal_(module.bias, std=1e-6)
if "mlp" in name
else nn.init.zeros_(module.bias)
)
elif isinstance(module, nn.Conv2d):
lecun_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif hasattr(module, "init_weights"):
module.init_weights()
def init_weights_vit_moco(module: nn.Module, name: str = ""):
"""ViT weight initialization, matching moco-v3 impl minus fixed PatchEmbed"""
if isinstance(module, nn.Linear):
if "qkv" in name:
# treat the weights of Q, K, V separately
val = math.sqrt(
6.0 / float(module.weight.shape[0] // 3 + module.weight.shape[1])
)
nn.init.uniform_(module.weight, -val, val)
else:
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif hasattr(module, "init_weights"):
module.init_weights()
def get_init_weights_vit(mode="jax", head_bias: float = 0.0):
if "jax" in mode:
return partial(init_weights_vit_jax, head_bias=head_bias)
elif "moco" in mode:
return init_weights_vit_moco
else:
return init_weights_vit_timm
def resize_pos_embed(
posemb,
posemb_new,
num_prefix_tokens=1,
gs_new=(),
interpolation="bicubic",
antialias=False,
):
"""Rescale the grid of position embeddings when loading from state_dict.
*DEPRECATED* This function is being deprecated in favour of resample_abs_pos_embed
Adapted from:
https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
"""
ntok_new = posemb_new.shape[1]
if num_prefix_tokens:
posemb_prefix, posemb_grid = (
posemb[:, :num_prefix_tokens],
posemb[0, num_prefix_tokens:],
)
ntok_new -= num_prefix_tokens
else:
posemb_prefix, posemb_grid = posemb[:, :0], posemb[0]
gs_old = int(math.sqrt(len(posemb_grid)))
if not len(gs_new): # backwards compatibility
gs_new = [int(math.sqrt(ntok_new))] * 2
assert len(gs_new) >= 2
_logger.info(
f"Resized position embedding: {posemb.shape} ({[gs_old, gs_old]}) to {posemb_new.shape} ({gs_new})."
)
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(
posemb_grid,
size=gs_new,
mode=interpolation,
antialias=antialias,
align_corners=False,
)
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
posemb = torch.cat([posemb_prefix, posemb_grid], dim=1)
return posemb
@torch.no_grad()
def _load_weights(model: nn.Module, checkpoint_path: str, prefix: str = ""):
"""Load weights from .npz checkpoints for official Google Brain Flax implementation"""
import numpy as np
def _n2p(w, t=True):
if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
w = w.flatten()
if t:
if w.ndim == 4:
w = w.transpose([3, 2, 0, 1])
elif w.ndim == 3:
w = w.transpose([2, 0, 1])
elif w.ndim == 2:
w = w.transpose([1, 0])
return torch.from_numpy(w)
w = np.load(checkpoint_path)
interpolation = "bilinear"
antialias = False
big_vision = False
if not prefix:
if "opt/target/embedding/kernel" in w:
prefix = "opt/target/"
elif "params/embedding/kernel" in w:
prefix = "params/"
big_vision = True
elif "params/img/embedding/kernel" in w:
prefix = "params/img/"
big_vision = True
if hasattr(model.patch_embed, "backbone"):
# hybrid
backbone = model.patch_embed.backbone
stem_only = not hasattr(backbone, "stem")
stem = backbone if stem_only else backbone.stem
stem.conv.weight.copy_(
adapt_input_conv(
stem.conv.weight.shape[1], _n2p(w[f"{prefix}conv_root/kernel"])
)
)
stem.norm.weight.copy_(_n2p(w[f"{prefix}gn_root/scale"]))
stem.norm.bias.copy_(_n2p(w[f"{prefix}gn_root/bias"]))
if not stem_only:
for i, stage in enumerate(backbone.stages):
for j, block in enumerate(stage.blocks):
bp = f"{prefix}block{i + 1}/unit{j + 1}/"
for r in range(3):
getattr(block, f"conv{r + 1}").weight.copy_(
_n2p(w[f"{bp}conv{r + 1}/kernel"])
)
getattr(block, f"norm{r + 1}").weight.copy_(
_n2p(w[f"{bp}gn{r + 1}/scale"])
)
getattr(block, f"norm{r + 1}").bias.copy_(
_n2p(w[f"{bp}gn{r + 1}/bias"])
)
if block.downsample is not None:
block.downsample.conv.weight.copy_(
_n2p(w[f"{bp}conv_proj/kernel"])
)
block.downsample.norm.weight.copy_(
_n2p(w[f"{bp}gn_proj/scale"])
)
block.downsample.norm.bias.copy_(_n2p(w[f"{bp}gn_proj/bias"]))
embed_conv_w = _n2p(w[f"{prefix}embedding/kernel"])
else:
embed_conv_w = adapt_input_conv(
model.patch_embed.proj.weight.shape[1], _n2p(w[f"{prefix}embedding/kernel"])
)
if embed_conv_w.shape[-2:] != model.patch_embed.proj.weight.shape[-2:]:
embed_conv_w = resample_patch_embed(
embed_conv_w,
model.patch_embed.proj.weight.shape[-2:],
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
model.patch_embed.proj.weight.copy_(embed_conv_w)
model.patch_embed.proj.bias.copy_(_n2p(w[f"{prefix}embedding/bias"]))
if model.cls_token is not None:
model.cls_token.copy_(_n2p(w[f"{prefix}cls"], t=False))
if big_vision:
pos_embed_w = _n2p(w[f"{prefix}pos_embedding"], t=False)
else:
pos_embed_w = _n2p(
w[f"{prefix}Transformer/posembed_input/pos_embedding"], t=False
)
if pos_embed_w.shape != model.pos_embed.shape:
old_shape = pos_embed_w.shape
num_prefix_tokens = (
0
if getattr(model, "no_embed_class", False)
else getattr(model, "num_prefix_tokens", 1)
)
pos_embed_w = resample_abs_pos_embed( # resize pos embedding when different size from pretrained weights
pos_embed_w,
new_size=model.patch_embed.grid_size,
num_prefix_tokens=num_prefix_tokens,
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
model.pos_embed.copy_(pos_embed_w)
model.norm.weight.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/scale"]))
model.norm.bias.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/bias"]))
if (
isinstance(model.head, nn.Linear)
and f"{prefix}head/bias" in w
and model.head.bias.shape[0] == w[f"{prefix}head/bias"].shape[-1]
):
model.head.weight.copy_(_n2p(w[f"{prefix}head/kernel"]))
model.head.bias.copy_(_n2p(w[f"{prefix}head/bias"]))
# NOTE representation layer has been removed, not used in latest 21k/1k pretrained weights
# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
if model.attn_pool is not None:
block_prefix = f"{prefix}MAPHead_0/"
mha_prefix = block_prefix + f"MultiHeadDotProductAttention_0/"
model.attn_pool.latent.copy_(_n2p(w[f"{block_prefix}probe"], t=False))
model.attn_pool.kv.weight.copy_(
torch.cat(
[
_n2p(w[f"{mha_prefix}{n}/kernel"], t=False).flatten(1).T
for n in ("key", "value")
]
)
)
model.attn_pool.kv.bias.copy_(
torch.cat(
[
_n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1)
for n in ("key", "value")
]
)
)
model.attn_pool.q.weight.copy_(
_n2p(w[f"{mha_prefix}query/kernel"], t=False).flatten(1).T
)
model.attn_pool.q.bias.copy_(
_n2p(w[f"{mha_prefix}query/bias"], t=False).reshape(-1)
)
model.attn_pool.proj.weight.copy_(_n2p(w[f"{mha_prefix}out/kernel"]).flatten(1))
model.attn_pool.proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"]))
model.attn_pool.norm.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/scale"]))
model.attn_pool.norm.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/bias"]))
for r in range(2):
getattr(model.attn_pool.mlp, f"fc{r + 1}").weight.copy_(
_n2p(w[f"{block_prefix}MlpBlock_0/Dense_{r}/kernel"])
)
getattr(model.attn_pool.mlp, f"fc{r + 1}").bias.copy_(
_n2p(w[f"{block_prefix}MlpBlock_0/Dense_{r}/bias"])
)
mha_sub, b_sub, ln1_sub = (0, 0, 1) if big_vision else (1, 3, 2)
for i, block in enumerate(model.blocks.children()):
block_prefix = f"{prefix}Transformer/encoderblock_{i}/"
mha_prefix = block_prefix + f"MultiHeadDotProductAttention_{mha_sub}/"
block.norm1.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/scale"]))
block.norm1.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/bias"]))
block.attn.qkv.weight.copy_(
torch.cat(
[
_n2p(w[f"{mha_prefix}{n}/kernel"], t=False).flatten(1).T
for n in ("query", "key", "value")
]
)
)
block.attn.qkv.bias.copy_(
torch.cat(
[
_n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1)
for n in ("query", "key", "value")
]
)
)
block.attn.proj.weight.copy_(_n2p(w[f"{mha_prefix}out/kernel"]).flatten(1))
block.attn.proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"]))
block.norm2.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_{ln1_sub}/scale"]))
block.norm2.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_{ln1_sub}/bias"]))
for r in range(2):
getattr(block.mlp, f"fc{r + 1}").weight.copy_(
_n2p(w[f"{block_prefix}MlpBlock_{b_sub}/Dense_{r}/kernel"])
)
getattr(block.mlp, f"fc{r + 1}").bias.copy_(
_n2p(w[f"{block_prefix}MlpBlock_{b_sub}/Dense_{r}/bias"])
)
def _convert_openai_clip(state_dict, model):
out_dict = {}
swaps = [
("visual.", ""),
("conv1", "patch_embed.proj"),
("positional_embedding", "pos_embed"),
("transformer.resblocks.", "blocks."),
("ln_pre", "norm_pre"),
("ln_post", "norm"),
("ln_", "norm"),
("in_proj_", "qkv."),
("out_proj", "proj"),
("mlp.c_fc", "mlp.fc1"),
("mlp.c_proj", "mlp.fc2"),
]
for k, v in state_dict.items():
if not k.startswith("visual."):
continue
for sp in swaps:
k = k.replace(sp[0], sp[1])
if k == "proj":
k = "head.weight"
v = v.transpose(0, 1)
out_dict["head.bias"] = torch.zeros(v.shape[0])
elif k == "class_embedding":
k = "cls_token"
v = v.unsqueeze(0).unsqueeze(1)
elif k == "pos_embed":
v = v.unsqueeze(0)
if v.shape[1] != model.pos_embed.shape[1]:
# To resize pos embedding when using model at different size from pretrained weights
v = resize_pos_embed(
v,
model.pos_embed,
(
0
if getattr(model, "no_embed_class")
else getattr(model, "num_prefix_tokens", 1)
),
model.patch_embed.grid_size,
)
out_dict[k] = v
return out_dict
def _convert_dinov2(state_dict, model):
import re
out_dict = {}
for k, v in state_dict.items():
if k == "mask_token":
continue
elif re.match(r"blocks\.(\d+)\.mlp\.w12\.(?:weight|bias)", k):
out_dict[k.replace("w12", "fc1")] = v
continue
elif re.match(r"blocks\.(\d+)\.mlp\.w3\.(?:weight|bias)", k):
out_dict[k.replace("w3", "fc2")] = v
continue
out_dict[k] = v
return out_dict
def checkpoint_filter_fn(
state_dict,
model,
adapt_layer_scale=False,
interpolation="bicubic",
antialias=True,
):
"""convert patch embedding weight from manual patchify + linear proj to conv"""
import re
out_dict = {}
state_dict = state_dict.get("model", state_dict)
state_dict = state_dict.get("state_dict", state_dict)
prefix = ""
if "visual.class_embedding" in state_dict:
return _convert_openai_clip(state_dict, model)
if "mask_token" in state_dict:
state_dict = _convert_dinov2(state_dict, model)
if "encoder" in state_dict:
state_dict = state_dict["encoder"]
prefix = "module."
if "visual.trunk.pos_embed" in state_dict:
# convert an OpenCLIP model with timm vision encoder
# FIXME remap final nn.Linear if it exists outside of the timm .trunk (ie in visual.head.proj)
prefix = "visual.trunk."
if prefix:
# filter on & remove prefix string from keys
state_dict = {
k[len(prefix) :]: v for k, v in state_dict.items() if k.startswith(prefix)
}
for k, v in state_dict.items():
if "patch_embed.proj.weight" in k:
O, I, H, W = model.patch_embed.proj.weight.shape
if len(v.shape) < 4:
# For old models that I trained prior to conv based patchification
O, I, H, W = model.patch_embed.proj.weight.shape
v = v.reshape(O, -1, H, W)
if v.shape[-1] != W or v.shape[-2] != H:
v = resample_patch_embed(
v,
(H, W),
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
elif k == "pos_embed" and v.shape[1] != model.pos_embed.shape[1]:
# To resize pos embedding when using model at different size from pretrained weights
num_prefix_tokens = (
0
if getattr(model, "no_embed_class", False)
else getattr(model, "num_prefix_tokens", 1)
)
v = resample_abs_pos_embed(
v,
new_size=model.patch_embed.grid_size,
num_prefix_tokens=num_prefix_tokens,
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
elif adapt_layer_scale and "gamma_" in k:
# remap layer-scale gamma into sub-module (deit3 models)
k = re.sub(r"gamma_([0-9])", r"ls\1.gamma", k)
elif "pre_logits" in k:
# NOTE representation layer removed as not used in latest 21k/1k pretrained weights
continue
out_dict[k] = v
return out_dict
================================================
FILE: grasp_gen/robot.py
================================================
# Copyright (c) 2025, 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.
#
# Author: Adithya Murali
"""
Modules to compute gripper poses from contact masks and parameters.
"""
import glob
import importlib.util
import os
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List
import numpy as np
import torch
import trimesh
import trimesh.transformations as tra
import yaml
from grasp_gen.dataset.eval_utils import load_urdf_scene
@dataclass
class GripperInfo:
"""A dataclass containing information about a gripper configuration.
This class stores all the necessary information about a gripper including its
meshes, transforms, control points, and other properties.
Attributes:
gripper_name (str): Name identifier for the gripper.
collision_mesh (trimesh.base.Trimesh): Mesh used for collision detection.
visual_mesh (trimesh.base.Trimesh): Mesh used for visualization.
offset_transform (trimesh.base.Trimesh): This is the transform offset from converting a gripper from its original axis to the GraspGen convention
offset_bins (np.ndarray, optional): Bins for offset calculations. Defaults to None. Used for M2T2 loss only.
offset_bin_weights (np.ndarray, optional): Weights for offset bins. Defaults to None. Used for M2T2 loss only.
depth (float, optional): Depth of the gripper. Defaults to None.
symmetric (float): Whether the gripper is symmetric. Defaults to False. For antipodal grippers only.
control_points (np.ndarray): Control points used for applying learning losses and computing metrics.
control_points_visualization (np.ndarray, optional): Points used for visualization. Defaults to None.
transform_from_base_link_to_tool_tcp (np.ndarray, optional): Transform from the gripper base link to the tool TCP (Tool Center Point). Defaults to None.
"""
gripper_name: str
collision_mesh: trimesh.base.Trimesh
visual_mesh: trimesh.base.Trimesh
offset_transform: trimesh.base.Trimesh
control_points: np.ndarray
offset_bins: np.ndarray = None
offset_bin_weights: np.ndarray = None
depth: float = None
symmetric: float = False
control_points: np.ndarray = None
control_points_visualization: np.ndarray = None
transform_from_base_link_to_tool_tcp: np.ndarray = None
def get_canonical_gripper_control_points(w, d):
"""Generate canonical control points for a gripper.
Convention assumes approach direction is z-positive and contact direction is along x.
The control points are arranged in a rectangular pattern.
Args:
w (float): Width of the gripper.
d (float): Depth of the gripper.
Returns:
np.ndarray: Array of shape (4, 3) containing the control points in 3D space.
Points are arranged as [right_front, left_front, right_back, left_back].
"""
control_points = np.array(
[[w / 2, -0, d / 2], [-w / 2, 0, d / 2], [w / 2, -0, d], [-w / 2, 0, d]]
)
return control_points
def get_gripper_depth(gripper_name) -> float:
"""Get the depth of a specified gripper.
This gives the distance from the gripper root/base link frame to the gripper tool tip frame.
Args:
gripper_name (str): Name of the gripper to get depth for.
Returns:
float: The depth of the gripper in meters.
"""
gripper_info = get_gripper_info(gripper_name)
return gripper_info.depth
def load_control_points_core(gripper_config: Dict):
"""Load control points from a gripper configuration.
This function handles loading control points either from explicit control points
in the config or by generating them from width and depth parameters.
Args:
gripper_config (Dict): Dictionary containing gripper configuration.
Returns:
np.ndarray: Array of control points for the gripper.
Raises:
NotImplementedError: If neither control_points nor width and depth are specified.
"""
if "control_points" in gripper_config:
control_points = gripper_config["control_points"]
elif "width" in gripper_config and "depth" in gripper_config:
width = gripper_config["width"]
depth = gripper_config["depth"]
control_points = get_canonical_gripper_control_points(width, depth)
else:
raise NotImplementedError(
f"Unable to load control points for gripper {gripper_name}. Neither control_points nor width and depth are specified."
)
return control_points
def load_control_points_for_visualization(gripper_name: str):
"""Load control points used for visualization of a gripper in meshcat.
Args:
gripper_name (str): Name of the gripper to load visualization points for.
Returns:
np.ndarray: Array of control points used for visualization.
"""
gripper_info = get_gripper_info(gripper_name)
control_points = gripper_info.control_points_visualization
return control_points
def load_control_points(gripper_name: str) -> torch.Tensor:
"""Load control points for a specified gripper.
Args:
gripper_name (str): Name of the gripper to load control points for.
Returns:
torch.Tensor: Tensor containing the control points for the gripper.
"""
gripper_info = get_gripper_info(gripper_name)
control_points = gripper_info.control_points
return control_points
def generate_circle_points(center, radius=0.007, N=30):
"""Generate a set of points that form a circle in 2D space.
This function creates N equally spaced points along the circumference of a circle
with the specified center and radius. The points are returned as a 2D array of
x,y coordinates.
Args:
center (np.ndarray or list): The [x, y] coordinates of the circle's center.
radius (float, optional): The radius of the circle in meters. Defaults to 0.007.
N (int, optional): Number of points to generate along the circle. Defaults to 30.
Returns:
np.ndarray: A 2D array of shape (N, 2) containing the x,y coordinates of the
generated points. Each row represents one point [x, y].
Example:
>>> center = [0.0, 0.0]
>>> points = generate_circle_points(center, radius=0.01, N=8)
>>> print(points.shape)
(8, 2)
"""
# Generate N equally spaced angles between 0 and 2π
angles = np.linspace(0, 2 * np.pi, N, endpoint=False)
# Calculate the x and y coordinates of the points
x_points = center[0] + radius * np.cos(angles)
y_points = center[1] + radius * np.sin(angles)
# Stack x and y points into a 2D array
points = np.stack((x_points, y_points), axis=1)
return points
def load_visualize_control_points_multi_suction(
list_of_suction_center_points=List[List[float]],
):
"""Generate visualization points for multiple suction cups.
This function creates visualization points for multiple suction cups by generating
circular patterns around each suction center point.
Args:
list_of_suction_center_points (List[List[float]]): List of [x, y, z] coordinates
for each suction cup center. The z-axis value is obtained from the first
suction center point.
Returns:
np.ndarray: Array of shape (N, M, 3) containing the x,y,z coordinates for
visualization points of all suction cups, where N is the number of
suction cups and M is the number of points per suction cup.
Note:
The z-axis value is obtained from the first suction center point and applied
to all points.
"""
assert len(list_of_suction_center_points) > 0
for pt in list_of_suction_center_points:
assert len(pt) == 3
z = list_of_suction_center_points[0][2]
xy = [
generate_circle_points(c[0:2], radius=0.005)
for c in list_of_suction_center_points
]
xy = np.stack(xy)
ptsz = z * np.ones([xy.shape[0], xy.shape[1], 1])
xyz = np.concatenate([xy, ptsz], axis=2)
return xyz
def load_gripper_yaml_file(yaml_path: Path):
"""Load gripper configuration from a YAML file.
Args:
yaml_path (Path): Path to the YAML configuration file.
Returns:
Dict: Dictionary containing the gripper configuration.
"""
with open(yaml_path, "r") as f:
config = yaml.safe_load(f)
return config
def import_module_from_path(path):
"""Import a Python module from a file path.
Args:
path (Path): Path to the Python module file.
Returns:
module: The imported Python module.
Raises:
ImportError: If the module cannot be imported.
"""
path = Path(path)
module_name = path.stem
spec = importlib.util.spec_from_file_location(module_name, str(path))
if spec is None:
raise ImportError(f"Could not create a spec for {path}")
module = importlib.util.module_from_spec(spec)
sys.modules[module_name] = module
spec.loader.exec_module(module)
return module
def load_default_gripper_config(gripper_name: str) -> Dict:
"""Load the default configuration for a specified gripper.
Args:
gripper_name (str): Name of the gripper to load configuration for.
Returns:
Dict: Dictionary containing the gripper's default configuration.
"""
conf_path = (
Path(__file__).parent.parent / "config" / "grippers" / f"{gripper_name}.yaml"
)
config = load_gripper_yaml_file(conf_path)
return config
def parse_offset_transform_from_yaml(offset_transform: List[List[float]]) -> np.ndarray:
"""Parse offset transform from YAML format to 4x4 transformation matrix.
Args:
offset_transform (List[List[float]]): List containing [translation (xyz), quaternion (xyzw)].
Returns:
np.ndarray: 4x4 transformation matrix.
Raises:
AssertionError: If the input format is incorrect.
"""
assert len(offset_transform) == 2
assert len(offset_transform[0]) == 3
assert (
len(offset_transform[1]) == 4
), f"Quaternion must be in xyzw format. Got {offset_transform[1]}"
trans = offset_transform[0]
quat = offset_transform[1]
offset_transform_mat = tra.quaternion_matrix(
[
quat[-1],
]
+ quat[:-1]
)
offset_transform_mat[:3, 3] = trans
return offset_transform_mat
def get_gripper_info(name: str) -> GripperInfo:
"""Get comprehensive information about a specified gripper.
This function orchestrates the loading of the gripper configuration, model,
and gripper-specific information from Python definitions.
Args:
name (str): Name of the gripper to get information for.
Returns:
GripperInfo: Object containing all information about the gripper.
Raises:
ValueError: If the gripper is not registered.
NotImplementedError: If required functions are not implemented in the gripper module.
"""
import glob
registered_grippers = glob.glob(
f"{Path(__file__).parent.parent}/config/grippers/*.yaml"
)
registered_grippers = [Path(gripper).stem for gripper in registered_grippers]
gripper_name = name
if gripper_name not in registered_grippers:
raise ValueError(
f"Gripper {gripper_name} not registered yet. Available grippers are: {registered_grippers}"
)
offset_bins, offset_bin_weights = None, None
gripper_module = import_module_from_path(
f"{Path(__file__).parent.parent}/config/grippers/{gripper_name}.py"
)
gripper_config = load_default_gripper_config(gripper_name)
gripper_model = gripper_module.GripperModel()
collision_mesh = gripper_model.get_gripper_collision_mesh()
visual_mesh = gripper_model.get_gripper_visual_mesh()
if hasattr(gripper_module, "get_gripper_offset_bins"):
offset_bins, offset_bin_weights = gripper_module.get_gripper_offset_bins()
if "transform_offset_from_asset_to_graspgen_convention" in gripper_config:
offset_transform = parse_offset_transform_from_yaml(
gripper_config["transform_offset_from_asset_to_graspgen_convention"]
)
else:
offset_transform = np.eye(4)
depth = gripper_config["depth"]
if "symmetric_antipodal" in gripper_config:
symmetric_antipodal = gripper_config["symmetric_antipodal"]
else:
symmetric_antipodal = False
if hasattr(gripper_module, "load_control_points"):
control_points = gripper_module.load_control_points()
else:
raise NotImplementedError(
f"Please implement a load_control_points function for gripper {gripper_name}."
)
if hasattr(gripper_module, "load_control_points_for_visualization"):
control_points_visualization = (
gripper_module.load_control_points_for_visualization()
)
else:
raise NotImplementedError(
f"Please implement a load_control_points_for_visualization function for gripper {gripper_name}."
)
if hasattr(gripper_module, "get_transform_from_base_link_to_tool_tcp"):
transform_from_base_link_to_tool_tcp = (
gripper_module.get_transform_from_base_link_to_tool_tcp()
)
else:
transform_from_base_link_to_tool_tcp = tra.translation_matrix(
[0, 0, np.abs(depth)]
)
collision_mesh.apply_transform(offset_transform)
visual_mesh.apply_transform(offset_transform)
gripper_info = GripperInfo(
gripper_name=gripper_name,
collision_mesh=collision_mesh,
visual_mesh=visual_mesh,
offset_transform=offset_transform,
offset_bins=offset_bins,
offset_bin_weights=offset_bin_weights,
depth=depth,
control_points=control_points,
control_points_visualization=control_points_visualization,
symmetric=symmetric_antipodal,
transform_from_base_link_to_tool_tcp=transform_from_base_link_to_tool_tcp,
)
return gripper_info
================================================
FILE: grasp_gen/serving/__init__.py
================================================
def __getattr__(name):
if name == "GraspGenZMQServer":
from grasp_gen.serving.zmq_server import GraspGenZMQServer
return GraspGenZMQServer
if name == "GraspGenClient":
from grasp_gen.serving.zmq_client import GraspGenClient
return GraspGenClient
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
__all__ = ["GraspGenZMQServer", "GraspGenClient"]
================================================
FILE: grasp_gen/serving/zmq_client.py
================================================
# Copyright (c) 2025, 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.
"""Lightweight ZMQ client for GraspGen server.
Only depends on pyzmq, msgpack, msgpack-numpy, and numpy — no torch / CUDA needed.
This makes it suitable for running on robot controllers or edge devices.
Usage:
from grasp_gen.serving.zmq_client import GraspGenClient
client = GraspGenClient("localhost", 5556)
grasps, confidences = client.infer(point_cloud)
"""
import logging
import time
from typing import Optional
import numpy as np
import zmq
import msgpack
import msgpack_numpy
msgpack_numpy.patch()
logger = logging.getLogger(__name__)
class GraspGenClient:
"""Client that connects to a GraspGen ZMQ server for remote grasp inference."""
def __init__(
self,
host: str = "localhost",
port: int = 5556,
timeout_ms: int = 60_000,
wait_for_server: bool = True,
retry_interval_s: float = 2.0,
) -> None:
self._addr = f"tcp://{host}:{port}"
self._timeout_ms = timeout_ms
self._ctx = zmq.Context()
self._socket: Optional[zmq.Socket] = None
self._server_metadata: Optional[dict] = None
if wait_for_server:
self._wait_for_server(retry_interval_s)
def _create_socket(self) -> zmq.Socket:
sock = self._ctx.socket(zmq.REQ)
sock.setsockopt(zmq.RCVTIMEO, self._timeout_ms)
sock.setsockopt(zmq.SNDTIMEO, self._timeout_ms)
sock.setsockopt(zmq.LINGER, 0)
sock.connect(self._addr)
return sock
def _wait_for_server(self, retry_interval_s: float) -> None:
logger.info("Waiting for GraspGen server at %s ...", self._addr)
while True:
try:
self._socket = self._create_socket()
self._server_metadata = self._request({"action": "metadata"})
logger.info(
"Connected to GraspGen server: %s", self._server_metadata
)
return
except (zmq.error.Again, zmq.error.ZMQError):
logger.info("Server not ready, retrying in %.1fs ...", retry_interval_s)
if self._socket is not None:
self._socket.close()
self._socket = None
time.sleep(retry_interval_s)
def _ensure_connected(self) -> None:
if self._socket is None:
self._socket = self._create_socket()
def _request(self, payload: dict) -> dict:
self._ensure_connected()
self._socket.send(msgpack.packb(payload, use_bin_type=True))
raw = self._socket.recv()
response = msgpack.unpackb(raw, raw=False)
if "error" in response:
raise RuntimeError(f"Server error: {response['error']}")
return response
@property
def server_metadata(self) -> Optional[dict]:
return self._server_metadata
def health_check(self) -> bool:
try:
resp = self._request({"action": "health"})
return resp.get("status") == "ok"
except Exception:
return False
def get_metadata(self) -> dict:
return self._request({"action": "metadata"})
def infer(
self,
point_cloud: np.ndarray,
*,
grasp_threshold: float = -1.0,
num_grasps: int = 200,
topk_num_grasps: int = -1,
min_grasps: int = 40,
max_tries: int = 6,
remove_outliers: bool = True,
) -> tuple[np.ndarray, np.ndarray]:
"""Send a point cloud to the server and receive grasp predictions.
Args:
point_cloud: (N, 3) float32 array of object points.
grasp_threshold: Min confidence to keep. -1.0 returns top-k instead.
num_grasps: Number of grasps the diffusion model should sample.
topk_num_grasps: Return only top-k grasps (-1 = use threshold).
min_grasps: Minimum grasps before retrying.
max_tries: Max inference retries on the server.
remove_outliers: Whether to filter point cloud outliers.
Returns:
grasps: (M, 4, 4) float32 array of 6-DOF grasp poses.
confidences: (M,) float32 array of grasp confidence scores.
"""
point_cloud = np.asarray(point_cloud, dtype=np.float32)
if point_cloud.ndim != 2 or point_cloud.shape[1] != 3:
raise ValueError(f"point_cloud must be (N, 3), got {point_cloud.shape}")
payload = {
"action": "infer",
"point_cloud": point_cloud,
"grasp_threshold": grasp_threshold,
"num_grasps": num_grasps,
"topk_num_grasps": topk_num_grasps,
"min_grasps": min_grasps,
"max_tries": max_tries,
"remove_outliers": remove_outliers,
}
response = self._request(payload)
grasps = np.asarray(response["grasps"], dtype=np.float32)
confidences = np.asarray(response["confidences"], dtype=np.float32)
return grasps, confidences
def close(self) -> None:
if self._socket is not None:
self._socket.close()
self._socket = None
self._ctx.term()
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
def __del__(self):
self.close()
================================================
FILE: grasp_gen/serving/zmq_server.py
================================================
# Copyright (c) 2025, 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 time
import logging
from typing import Optional
import numpy as np
import zmq
import msgpack
import msgpack_numpy
msgpack_numpy.patch()
from grasp_gen.grasp_server import GraspGenSampler, load_grasp_cfg
logger = logging.getLogger(__name__)
class GraspGenZMQServer:
"""ZMQ server that wraps GraspGenSampler for remote grasp inference.
Protocol (msgpack over ZMQ REP socket):
Request: {"action": "infer", "point_cloud": ndarray(N,3), ...params}
{"action": "metadata"}
{"action": "health"}
Response: msgpack-encoded dict with results or error.
"""
def __init__(
self,
gripper_config: str,
host: str = "0.0.0.0",
port: int = 5556,
) -> None:
self._host = host
self._port = port
self._gripper_config = gripper_config
logger.info("Loading gripper config from %s", gripper_config)
self._cfg = load_grasp_cfg(gripper_config)
self._gripper_name = self._cfg.data.gripper_name
self._model_name = self._cfg.eval.model_name
logger.info(
"Initializing GraspGenSampler (model=%s, gripper=%s)",
self._model_name,
self._gripper_name,
)
self._sampler = GraspGenSampler(self._cfg)
logger.info("Model loaded and ready for inference")
self._metadata = {
"gripper_name": self._gripper_name,
"model_name": self._model_name,
"gripper_config": gripper_config,
}
def serve_forever(self) -> None:
ctx = zmq.Context()
socket = ctx.socket(zmq.REP)
bind_addr = f"tcp://{self._host}:{self._port}"
socket.bind(bind_addr)
logger.info("GraspGen ZMQ server listening on %s", bind_addr)
try:
while True:
raw = socket.recv()
try:
request = msgpack.unpackb(raw, raw=False)
response = self._handle(request)
except Exception as exc:
logger.exception("Error handling request")
response = {"error": str(exc)}
socket.send(msgpack.packb(response, use_bin_type=True))
except KeyboardInterrupt:
logger.info("Shutting down server")
finally:
socket.close()
ctx.term()
def _handle(self, request: dict) -> dict:
action = request.get("action")
if action == "health":
return {"status": "ok"}
if action == "metadata":
return self._metadata
if action == "infer":
return self._handle_infer(request)
return {"error": f"Unknown action: {action}"}
def _handle_infer(self, request: dict) -> dict:
point_cloud = request.get("point_cloud")
if point_cloud is None:
return {"error": "Missing required field 'point_cloud'"}
point_cloud = np.asarray(point_cloud, dtype=np.float32)
if point_cloud.ndim != 2 or point_cloud.shape[1] != 3:
return {
"error": f"point_cloud must be (N, 3), got {point_cloud.shape}"
}
params = {
"grasp_threshold": float(request.get("grasp_threshold", -1.0)),
"num_grasps": int(request.get("num_grasps", 200)),
"topk_num_grasps": int(request.get("topk_num_grasps", -1)),
"min_grasps": int(request.get("min_grasps", 40)),
"max_tries": int(request.get("max_tries", 6)),
"remove_outliers": bool(request.get("remove_outliers", True)),
}
t0 = time.monotonic()
grasps, grasp_conf = GraspGenSampler.run_inference(
point_cloud, self._sampler, **params
)
infer_ms = (time.monotonic() - t0) * 1000
if len(grasps) == 0:
return {
"grasps": np.empty((0, 4, 4), dtype=np.float32),
"confidences": np.empty((0,), dtype=np.float32),
"num_grasps": 0,
"timing": {"infer_ms": infer_ms},
}
grasps_np = grasps.cpu().numpy().astype(np.float32)
conf_np = grasp_conf.cpu().numpy().astype(np.float32)
logger.info(
"Inferred %d grasps in %.1f ms (conf range %.3f - %.3f)",
len(grasps_np),
infer_ms,
conf_np.min(),
conf_np.max(),
)
return {
"grasps": grasps_np,
"confidences": conf_np,
"num_grasps": len(grasps_np),
"timing": {"infer_ms": infer_ms},
}
================================================
FILE: grasp_gen/utils/logging_config.py
================================================
import logging
import sys
# Global flag to track if logging is initialized
_logging_initialized = False
def setup_logging():
"""
Set up basic logging configuration for all scripts.
Uses INFO level and outputs to both console and file.
"""
global _logging_initialized
# Only initialize once
if _logging_initialized:
return
# Configure logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
handlers=[logging.StreamHandler(sys.stdout)],
)
_logging_initialized = True
def get_logger(name):
"""
Get a logger instance for a specific module.
Args:
name: The name of the module (usually __name__)
Returns:
A logger instance configured for the module
"""
# Ensure logging is initialized before getting a logger
setup_logging()
return logging.getLogger(name)
# Initialize logging when this module is imported
setup_logging()
================================================
FILE: grasp_gen/utils/math_utils.py
================================================
# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
from typing import Optional, Union, List
import numpy as np
import torch
import torch.nn.functional as F
import trimesh.transformations as tra
from scipy.optimize import linear_sum_assignment
import grasp_gen.utils.rotation_conversions as rotation_conversions
import grasp_gen.utils.so3 as so3
def matrix_to_rt(
input: torch.Tensor, grasp_repr: str, kappa: Optional[float] = None
) -> torch.Tensor:
grasps_r_mat = input[:, :3, :3]
grasps_t = input[:, :3, 3]
if grasp_repr == "r3_6d":
grasps_r = matrix_to_rotation_6d(grasps_r_mat)
elif grasp_repr == "r3_so3":
grasps_r = so3.so3_log_map(grasps_r_mat)
noise_scale_rot = 1 / torch.pi
grasps_r = grasps_r * noise_scale_rot # Scale range to [-1, 1]
elif grasp_repr == "r3_euler":
grasps_r = rotation_conversions.matrix_to_euler_angles(
grasps_r_mat, convention="XYZ"
)
noise_scale_rot = 1 / torch.pi
grasps_r = grasps_r * noise_scale_rot # Scale range to [-1, 1]
else:
raise NotImplementedError(f"Representation for rotation {grasp_repr} unknown!")
if kappa is not None:
grasps_t = kappa * grasps_t
grasps_gt = torch.hstack([grasps_t, grasps_r]).float()
return grasps_gt
def rt_to_matrix(
input: torch.Tensor, grasp_repr: str, kappa: Optional[float] = None
) -> torch.Tensor:
batch_size = input.shape[0]
mat = torch.zeros([batch_size, 4, 4]).to(device=input.device)
if grasp_repr == "r3_6d":
mat[:, :3, :3] = rotation_6d_to_matrix(input[:, 3:])
elif grasp_repr == "r3_so3":
mat[:, :3, :3] = so3.so3_exp_map(input[:, 3:] * torch.pi)
elif grasp_repr == "r3_euler":
mat[:, :3, :3] = rotation_conversions.euler_angles_to_matrix(
input[:, 3:] * torch.pi, convention="XYZ"
)
else:
raise NotImplementedError(
f"Rotation representation {grasp_repr} is not implemented!"
)
mat[:, :3, 3] = input[:, :3]
if kappa is not None:
inv_kappa = 1.0 / kappa
mat[:, :3, 3] *= inv_kappa
return mat
def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
"""
Original implementation from https://github.com/facebookresearch/pytorch3d
Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
using Gram--Schmidt orthogonalization per Section B of [1].
Args:
d6: 6D rotation representation, of size (*, 6)
Returns:
batch of rotation matrices of size (*, 3, 3)
[1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
On the Continuity of Rotation Representations in Neural Networks.
IEEE Conference on Computer Vision and Pattern Recognition, 2019.
Retrieved from http://arxiv.org/abs/1812.07035
"""
a1, a2 = d6[..., :3], d6[..., 3:]
b1 = F.normalize(a1, dim=-1)
b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
b2 = F.normalize(b2, dim=-1)
b3 = torch.cross(b1, b2, dim=-1)
return torch.stack((b1, b2, b3), dim=-2)
def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
"""
Original implementation from https://github.com/facebookresearch/pytorch3d
Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
by dropping the last row. Note that 6D representation is not unique.
Args:
matrix: batch of rotation matrices of size (*, 3, 3)
Returns:
6D rotation representation, of size (*, 6)
[1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
On the Continuity of Rotation Representations in Neural Networks.
IEEE Conference on Computer Vision and Pattern Recognition, 2019.
Retrieved from http://arxiv.org/abs/1812.07035
"""
batch_dim = matrix.size()[:-2]
return matrix[..., :2, :].clone().reshape(batch_dim + (6,))
def rotation_from_vectors(v1, v2):
"""
Uses Rodrigues' formula to compute the [3x3] rotation matrix
to align vector v1 onto v2.
"""
u = v1 / np.linalg.norm(v1)
Ru = v2 / np.linalg.norm(v2)
dim = u.size
I = np.identity(dim)
c = np.dot(u, Ru)
eps = 1.0e-10
if np.abs(c - 1.0) < eps:
# same direction
return I
elif np.abs(c + 1.0) < eps:
# opposite direction
return -I
else:
K = np.outer(Ru, u) - np.outer(u, Ru)
return I + K + (K @ K) / (1 + c)
def construct_suction_grasp_from_point_and_vector(
contact_point, approach_vector, normal_vector, offset=-0.005
):
"""
Computes grasp pose given contact point, normal vector and approach vector.
The offset is needed to avoid collisions with the collision checker
"""
# Align the gripper approach vector with the contact point surface normal
T_rotate_gripper_to_surface_normal = np.eye(4)
T_rotate_gripper_to_surface_normal[:3, :3] = rotation_from_vectors(
approach_vector, normal_vector
)
T_rotate_gripper_to_surface_normal[:3, :3] = rotation_matrix_from_vectors(
normal_vector, approach_vector
)
T_rotate_gripper_to_surface_normal = (
T_rotate_gripper_to_surface_normal @ tra.euler_matrix(np.pi, 0, 0)
)
# NOTE: This is to prevent collisions (as in the default state the gripper collides with the object)
T_approach_offset = tra.translation_matrix([0, 0, offset])
T_translate_gripper_to_contact_point = tra.translation_matrix(contact_point)
grasp_pose = (
T_translate_gripper_to_contact_point
@ T_rotate_gripper_to_surface_normal
@ T_approach_offset
)
return grasp_pose
def rotation_matrix_from_vectors(
v1: Union[torch.Tensor, np.ndarray, List], v2: Union[torch.Tensor, np.ndarray, List]
) -> torch.Tensor:
"""
Compute the rotation matrix that aligns vector v1 onto vector v2.
This function uses the axis-angle representation to compute the rotation matrix
that rotates vector v1 to align with vector v2. The rotation is computed using
the cross product to find the rotation axis and the dot product to find the angle.
Args:
v1: First vector to align from. Can be torch.Tensor, np.ndarray, or List.
v2: Second vector to align to. Can be torch.Tensor, np.ndarray, or List.
Returns:
torch.Tensor: 3x3 rotation matrix that rotates v1 to align with v2.
The matrix is computed using axis-angle representation.
Note:
Both input vectors are normalized to unit vectors before computing the rotation.
The function handles edge cases where vectors are parallel or anti-parallel.
"""
if type(v1) in [np.ndarray, list]:
v1 = torch.tensor(v1)
if type(v2) in [np.ndarray, list]:
v2 = torch.tensor(v2)
v1 = v1.float()
v2 = v2.float()
# Ensure v1 and v2 are unit vectors
v1 = v1 / v1.norm(dim=-1, keepdim=True)
v2 = v2 / v2.norm(dim=-1, keepdim=True)
# Compute the cross product (axis of rotation)
axis = torch.cross(v1, v2)
# Compute the dot product (cosine of the angle)
cos_theta = torch.sum(v1 * v2, dim=-1, keepdim=True)
sin_theta = axis.norm(dim=-1, keepdim=True)
# Normalize the axis to get the unit vector for the axis
axis = axis / sin_theta
# Compute the angle of rotation
angle = torch.atan2(sin_theta, cos_theta)
# Convert the axis-angle to a rotation matrix
rotation_matrix = axis_angle_to_matrix(axis * angle)
return rotation_matrix
def compute_pose_distance_batch(
poses1: torch.Tensor, poses2: torch.Tensor
) -> torch.Tensor:
"""
Compute distances between two sets of poses in a batched manner.
Args:
poses1: First set of poses [N1, 4, 4]
poses2: Second set of poses [N2, 4, 4]
Returns:
torch.Tensor: Distance matrix [N1, N2]
"""
# Extract positions [N1, 3] and [N2, 3]
pos1 = poses1[:, :3, 3] # [N1, 3]
pos2 = poses2[:, :3, 3] # [N2, 3]
# Compute pairwise position distances
pos1_expanded = pos1.unsqueeze(1) # [N1, 1, 3]
pos2_expanded = pos2.unsqueeze(0) # [1, N2, 3]
pos_dist = torch.norm(pos1_expanded - pos2_expanded, dim=2) # [N1, N2]
# Extract rotation matrices [N1, 3, 3] and [N2, 3, 3]
R1 = poses1[:, :3, :3] # [N1, 3, 3]
R2 = poses2[:, :3, :3] # [N2, 3, 3]
# Compute relative rotations
R1_expanded = R1.unsqueeze(1) # [N1, 1, 3, 3]
R2_expanded = R2.unsqueeze(0) # [1, N2, 3, 3]
R_diff = torch.matmul(R1_expanded, R2_expanded.transpose(-2, -1)) # [N1, N2, 3, 3]
# Reshape for so3_log_map
N1, N2 = R_diff.shape[:2]
R_diff_flat = R_diff.reshape(-1, 3, 3)
# Apply so3_log_map and compute rotation distances
log_maps = so3.so3_log_map(R_diff_flat) # [-1, 3]
rot_dist = torch.norm(log_maps, dim=1).reshape(N1, N2) / torch.pi
# Combine distances with equal weights
return pos_dist + rot_dist
def compute_pose_emd(poses1: torch.Tensor, poses2: torch.Tensor) -> float:
"""
Compute EMD between two sets of poses with equal weighting of position and rotation.
Args:
poses1: First set of poses [N1, 4, 4]
poses2: Second set of poses [N2, 4, 4]
Returns:
float: EMD distance considering both position and rotation equally
"""
# Ensure input is torch tensor
if isinstance(poses1, np.ndarray):
poses1 = torch.from_numpy(poses1).float()
if isinstance(poses2, np.ndarray):
poses2 = torch.from_numpy(poses2).float()
# Compute cost matrix using vectorized operations
M = compute_pose_distance_batch(poses1, poses2)
# Convert to numpy for linear_sum_assignment
M_np = M.cpu().numpy()
# Solve the assignment problem
row_ind, col_ind = linear_sum_assignment(M_np)
# Compute the total cost and normalize
emd = M_np[row_ind, col_ind].sum() / len(row_ind)
return float(emd)
================================================
FILE: grasp_gen/utils/meshcat_utils.py
================================================
# Copyright (c) 2025, 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.
#
# Author: Adithya Murali
"""
Utility functions for visualization using meshcat.
"""
from typing import Dict, List, Optional, Tuple, Union
import meshcat
import meshcat.geometry as g
import meshcat.transformations as mtf
import numpy as np
import trimesh
import trimesh.transformations as tra
from grasp_gen.robot import load_control_points_for_visualization
from grasp_gen.utils.logging_config import get_logger
logger = get_logger(__name__)
def is_rotation_matrix(M, tol=1e-4):
tag = False
I = np.identity(M.shape[0])
if (np.linalg.norm((np.matmul(M, M.T) - I)) < tol) and (
np.abs(np.linalg.det(M) - 1) < tol
):
tag = True
if tag is False:
logger.info("M @ M.T:\n", np.matmul(M, M.T))
logger.info("det:", np.linalg.det(M))
return tag
def get_color_from_score(labels, use_255_scale=False):
scale = 255.0 if use_255_scale else 1.0
if type(labels) in [np.float32, float]:
return scale * np.array([1 - labels, labels, 0])
else:
scale = 255.0 if use_255_scale else 1.0
score = scale * np.stack(
[np.ones(labels.shape[0]) - labels, labels, np.zeros(labels.shape[0])],
axis=1,
)
return score.astype(np.int32)
def trimesh_to_meshcat_geometry(
mesh: trimesh.Trimesh,
) -> meshcat.geometry.TriangularMeshGeometry:
"""
Args:
mesh: trimesh.TriMesh object
"""
return meshcat.geometry.TriangularMeshGeometry(mesh.vertices, mesh.faces)
def visualize_mesh(
vis: meshcat.Visualizer,
name: str,
mesh: trimesh.Trimesh,
color: Optional[List[int]] = None,
transform: Optional[np.ndarray] = None,
):
"""Visualize a mesh in meshcat"""
if vis is None:
return
if color is None:
color = np.random.randint(low=0, high=256, size=3)
mesh_vis = trimesh_to_meshcat_geometry(mesh)
color_hex = rgb2hex(tuple(color))
material = meshcat.geometry.MeshPhongMaterial(color=color_hex)
vis[name].set_object(mesh_vis, material)
if transform is not None:
vis[name].set_transform(transform)
def rgb2hex(rgb: Tuple[int, int, int]) -> str:
"""
Converts rgb color to hex
Args:
rgb: color in rgb, e.g. (255,0,0)
"""
return "0x%02x%02x%02x" % (rgb)
def create_visualizer(clear=True):
logger.info(
"Waiting for meshcat server... have you started a server? Run `meshcat-server` in a separate terminal to start a server."
)
vis = meshcat.Visualizer(zmq_url="tcp://127.0.0.1:6000")
if clear:
vis.delete()
logger.info("")
return vis
def make_frame(
vis: meshcat.Visualizer,
name: str,
h: float = 0.15,
radius: float = 0.01,
o: float = 1.0,
T: Optional[np.ndarray] = None,
):
"""Add a red-green-blue triad to the Meschat visualizer.
Args:
vis (MeshCat Visualizer): the visualizer
name (string): name for this frame (should be unique)
h (float): height of frame visualization
radius (float): radius of frame visualization
o (float): opacity
"""
vis[name]["x"].set_object(
g.Cylinder(height=h, radius=radius),
g.MeshLambertMaterial(color=0xFF0000, reflectivity=0.8, opacity=o),
)
rotate_x = mtf.rotation_matrix(np.pi / 2.0, [0, 0, 1])
rotate_x[0, 3] = h / 2
vis[name]["x"].set_transform(rotate_x)
vis[name]["y"].set_object(
g.Cylinder(height=h, radius=radius),
g.MeshLambertMaterial(color=0x00FF00, reflectivity=0.8, opacity=o),
)
rotate_y = mtf.rotation_matrix(np.pi / 2.0, [0, 1, 0])
rotate_y[1, 3] = h / 2
vis[name]["y"].set_transform(rotate_y)
vis[name]["z"].set_object(
g.Cylinder(height=h, radius=radius),
g.MeshLambertMaterial(color=0x0000FF, reflectivity=0.8, opacity=o),
)
rotate_z = mtf.rotation_matrix(np.pi / 2.0, [1, 0, 0])
rotate_z[2, 3] = h / 2
vis[name]["z"].set_transform(rotate_z)
if T is not None:
is_valid = is_rotation_matrix(T[:3, :3])
if not is_valid:
raise ValueError("meshcat_utils:attempted to visualize invalid transform T")
vis[name].set_transform(T)
def visualize_bbox(
vis: meshcat.Visualizer,
name: str,
dims: np.ndarray,
T: Optional[np.ndarray] = None,
color: Optional[List[int]] = [255, 0, 0],
):
"""Visualize a bounding box using a wireframe.
Args:
vis (MeshCat Visualizer): the visualizer
name (string): name for this frame (should be unique)
dims (array-like): shape (3,), dimensions of the bounding box
T (4x4 numpy.array): (optional) transform to apply to this geometry
"""
if vis is None:
return
color_hex = rgb2hex(tuple(color))
material = meshcat.geometry.MeshBasicMaterial(wireframe=True, color=color_hex)
bbox = meshcat.geometry.Box(dims)
vis[name].set_object(bbox, material)
if T is not None:
vis[name].set_transform(T)
def visualize_pointcloud(
vis: meshcat.Visualizer,
name: str,
pc: np.ndarray,
color: Optional[np.ndarray] = None,
transform: Optional[np.ndarray] = None,
**kwargs,
):
"""
Args:
vis: meshcat visualizer object
name: str
pc: Nx3 or HxWx3
color: (optional) same shape as pc[0 - 255] scale or just rgb tuple
transform: (optional) 4x4 homogeneous transform
"""
if vis is None:
return
if pc.ndim == 3:
pc = pc.reshape(-1, pc.shape[-1])
if color is not None:
if isinstance(color, list):
color = np.array(color)
color = np.array(color)
# Resize the color np array if needed.
if color.ndim == 3:
color = color.reshape(-1, color.shape[-1])
if color.ndim == 1:
color = np.ones_like(pc) * np.array(color)
# Divide it by 255 to make sure the range is between 0 and 1,
color = color.astype(np.float32) / 255
else:
color = np.ones_like(pc)
vis[name].set_object(
meshcat.geometry.PointCloud(position=pc.T, color=color.T, **kwargs)
)
if transform is not None:
vis[name].set_transform(transform)
def load_visualization_gripper_points(
gripper_name: str = "franka_panda",
) -> List[np.ndarray]:
"""
Need to return np.array of control points of shape [4, N], where is N is num points
"""
ctrl_points = []
for ctrl_pts in load_control_points_for_visualization(gripper_name):
ctrl_pts = np.array(ctrl_pts, dtype=np.float32)
ctrl_pts = np.hstack([ctrl_pts, np.ones([len(ctrl_pts), 1])])
ctrl_pts = ctrl_pts.T
ctrl_points.append(ctrl_pts)
return ctrl_points
def visualize_grasp(
vis: meshcat.Visualizer,
name: str,
transform: np.ndarray,
color: List[int] = [255, 0, 0],
gripper_name: str = "franka_panda",
**kwargs,
):
if vis is None:
return
grasp_vertices = load_visualization_gripper_points(gripper_name)
for i, grasp_vertex in enumerate(grasp_vertices):
vis[name + f"/{i}"].set_object(
g.Line(
g.PointsGeometry(grasp_vertex),
g.MeshBasicMaterial(color=rgb2hex(tuple(color)), **kwargs),
)
)
vis[name].set_transform(transform.astype(float))
def get_normals_from_mesh(
mesh: trimesh.Trimesh, contact_pts: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
from sklearn.neighbors import KDTree
points_codebook, index = mesh.sample(16000, return_index=True)
normals_codebook = mesh.face_normals[index]
contact_radius = 0.005
tree = KDTree(points_codebook)
dist, idx = tree.query(contact_pts)
matched = dist < contact_radius
idx2 = idx[matched]
normals = normals_codebook[idx2]
mask = matched[:, 0]
return normals, contact_pts[mask], mask
================================================
FILE: grasp_gen/utils/plot_utils.py
================================================
# Copyright (c) 2025, 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.
#
# Author: Wentao Yuan
"""
Utility functions for plotting during training.
"""
import numpy as np
import torch
from matplotlib import pyplot as plt
from matplotlib.cm import get_cmap
from matplotlib.gridspec import GridSpec
from grasp_gen.dataset.image_utils import denormalize_rgb
def get_set_colors():
# TODO: Fix this for kitchen scenes
n_colors = [9, 8, 100]
colors = []
for i, n in enumerate(n_colors):
cmap = get_cmap(f"Set{i+1}")
for j in range(n):
colors.append([int(c * 255) for c in cmap(j / n)[:3]])
return colors
def plot_mask_3D(
objectness,
names,
out_masks,
out_argmax,
tgt_masks,
num_targets,
obj_thresh,
pts,
rgb,
depth,
size,
):
seg_pred = np.zeros((size, size, 3), dtype="uint8")
seg = np.zeros((size, size, 3), dtype="uint8")
colors = get_set_colors()
num_rows = int(np.ceil(out_masks.shape[0] / 4))
fig = plt.figure(figsize=((8, num_rows * 2 + 8)))
gs = GridSpec(num_rows + 4, 4)
plt.subplot(gs[:2, :2])
plt.imshow(rgb)
plt.title("RGB")
plt.axis("off")
plt.subplot(gs[:2, 2:])
plt.imshow(depth, cmap="gray")
plt.title("Depth")
plt.axis("off")
for i, (out, tgt) in enumerate(zip(out_masks, tgt_masks)):
mask = out & (out_argmax == i)
seg_pred[pts[1, mask], pts[0, mask]] = colors[i]
if i < num_targets:
seg[pts[1, tgt], pts[0, tgt]] = colors[i]
plt.subplot(gs[4 + i // 4, i % 4])
mask_plot = np.zeros((size, size, 3))
mask_plot[pts[1], pts[0], 0] = tgt
mask_plot[pts[1], pts[0], 1] = out
intersect = (out & tgt).sum()
union = (out | tgt).sum()
iou = intersect / union if union > 0 else 1
c = "g"
if objectness[i] <= obj_thresh:
c = "r"
if i >= num_targets:
c = "b"
title = f"{names[i]}\nObjectness {objectness[i]:.4f}\nIoU {iou:.4f}"
plt.title(title, color=c)
plt.imshow(mask_plot)
plt.axis("off")
plt.subplot(gs[2:4, :2])
plt.imshow(seg_pred)
plt.title("Predicted")
plt.axis("off")
plt.subplot(gs[2:4, 2:])
plt.imshow(seg)
plt.title("Ground Truth")
plt.axis("off")
plt.tight_layout()
return fig
def plot_place_mask_3D(pts, rgb, depth, seg, out_mask, tgt_mask, size):
colors = get_set_colors()
seg_plot = np.zeros((size, size, 3), dtype="uint8")
for i, j in enumerate(np.unique(seg)):
seg_plot[pts[1, seg == j], pts[0, seg == j]] = colors[i]
fig = plt.figure(figsize=((12, 8)))
plt.subplot(2, 3, 1)
plt.imshow(rgb)
plt.title("RGB")
plt.axis("off")
plt.subplot(2, 3, 2)
plt.imshow(depth, cmap="gray")
plt.title("Depth")
plt.axis("off")
plt.subplot(2, 3, 3)
plt.imshow(seg_plot)
plt.title("Segmentation")
plt.axis("off")
out_any = out_mask.any(axis=0)
out_all = out_mask.all(axis=0)
colors = np.zeros((pts.shape[1], 3))
colors[out_any, :2] = 1
colors[out_all, 0] = 0
plot = np.zeros((size, size, 3))
plot[pts[1], pts[0]] = colors
plt.subplot(2, 3, 4)
plt.imshow(plot)
plt.title("Predicted")
plt.axis("off")
tgt_any = tgt_mask.any(axis=0)
tgt_all = tgt_mask.all(axis=0)
colors = np.zeros((pts.shape[1], 3))
colors[tgt_any, :2] = 1
colors[tgt_all, 0] = 0
plot = np.zeros((size, size, 3))
plot[pts[1], pts[0]] = colors
plt.subplot(2, 3, 5)
plt.imshow(plot)
plt.title("Ground truth")
plt.axis("off")
correct = out_mask == tgt_mask
correct_all = correct.all(axis=0)
colors = np.zeros((pts.shape[1], 3))
colors[tgt_any & ~out_any, 0] = 1
colors[out_any & ~tgt_any, 2] = 1
colors[out_any & tgt_any, 1] = 1
colors[correct_all & tgt_any, 0] = 1
plot = np.zeros((size, size, 3))
plot[pts[1], pts[0]] = colors
inter = (out_mask & tgt_mask).sum(axis=1)
union = (out_mask | tgt_mask).sum(axis=1)
iou = np.nan_to_num(np.divide(inter, union), nan=1).mean()
plt.subplot(2, 3, 6)
plt.imshow(plot)
plt.title(f"Intersection IoU {iou:.4f}")
plt.axis("off")
plt.tight_layout()
return fig
def plot_3D(
outputs,
data,
obj_thresh=0.5,
mask_thresh=0,
cam_coord=True,
scale=0.25,
size=128,
f=443.404968,
c=256,
):
def plot_rgb_depth(batch_idx):
pts = data["points"][batch_idx]
if not cam_coord:
world2cam = data["cam_pose"][batch_idx].inverse()
pts = pts @ world2cam[:3, :3].T + world2cam[:3, 3]
depth = pts[:, 2]
pts = ((pts.T[:2] / pts.T[2:] * f + c) * scale).int()
pts = torch.clamp(pts, 0, size - 1).numpy()
rgb = data["inputs"][batch_idx, :, 3:]
rgb = denormalize_rgb(rgb.T.unsqueeze(1)).squeeze(1).T.numpy()
rgb_plot = np.zeros((size, size, 3))
rgb_plot[pts[1], pts[0]] = np.clip(rgb, 0, 1)
depth_plot = np.zeros((size, size))
depth_plot[pts[1], pts[0]] = depth
return pts, rgb_plot, depth_plot
figs = {}
for batch_idx, is_place in enumerate(data["task_is_place"]):
if is_place:
break
if is_place:
pts, rgb_plot, depth_plot = plot_rgb_depth(batch_idx)
seg = data["seg"][batch_idx]
out_mask = outputs["placement_masks"][batch_idx]
out_mask = out_mask.numpy() > mask_thresh
tgt_mask = data["placement_masks"][batch_idx]
tgt_mask = tgt_mask.numpy() > 0
figs["placement"] = plot_place_mask_3D(
pts, rgb_plot, depth_plot, seg, out_mask, tgt_mask, size
)
for batch_idx, is_pick in enumerate(data["task_is_pick"]):
if is_pick:
break
if is_pick:
pts, rgb_plot, depth_plot = plot_rgb_depth(batch_idx)
objness = outputs["objectness"][batch_idx].sigmoid()
objness = objness[outputs["matched_idx"][batch_idx]]
out_masks = outputs["matched_grasping_masks"][batch_idx]
out_argmax = out_masks.argmax(dim=0).numpy()
out_masks = out_masks.numpy() > mask_thresh
tgt_masks = data["grasping_masks"][batch_idx]
names = data["names"][batch_idx]
num_targets = len(names)
tgt_masks = tgt_masks.numpy() > 0
figs["grasping"] = plot_mask_3D(
objness,
names,
out_masks,
out_argmax,
tgt_masks,
num_targets,
obj_thresh,
pts,
rgb_plot,
depth_plot,
size,
)
return figs
================================================
FILE: grasp_gen/utils/point_cloud_utils.py
================================================
# Copyright (c) 2025, 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
import sys
from typing import Tuple, Dict
import numpy as np
import torch
import trimesh
import trimesh.transformations as tra
from tqdm import tqdm
from grasp_gen.utils.logging_config import get_logger
from grasp_gen.dataset.renderer import depth2points
logger = get_logger(__name__)
# @torch.compile
def knn_points(X: torch.Tensor, K: int, norm: int):
"""
Computes the K-nearest neighbors for each point in the point cloud X.
Args:
X: (N, 3) tensor representing the point cloud.
K: Number of nearest neighbors.
Returns:
dists: (N, K) tensor containing squared Euclidean distances to the K nearest neighbors.
idxs: (N, K) tensor containing indices of the K nearest neighbors.
"""
N, _ = X.shape
# Compute pairwise squared Euclidean distances
dist_matrix = torch.cdist(X, X, p=norm) # (N, N)
# Ignore self-distance (optional, but avoids trivial zero distance)
self_mask = torch.eye(N, device=X.device, dtype=torch.bool)
dist_matrix.masked_fill_(self_mask, float("inf")) # Set self-distances to inf
# Get the indices of the K-nearest neighbors
dists, idxs = torch.topk(dist_matrix, K, dim=1, largest=False)
return dists, idxs
def point_cloud_outlier_removal(
obj_pc: torch.Tensor, threshold: float = 0.014, K: int = 20
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Remove outliers from a point cloud. K-nearest neighbors is used to compute the distance to the nearest neighbor for each point.
If the distance is greater than a threshold, the point is considered an outlier and removed.
RANSAC can also be used.
Args:
obj_pc (torch.Tensor or np.ndarray): (N, 3) tensor or array representing the point cloud.
threshold (float): Distance threshold for outlier detection. Points with mean distance to K nearest neighbors greater than this threshold are removed.
K (int): Number of nearest neighbors to consider for outlier detection.
Returns:
Tuple[torch.Tensor, torch.Tensor]: Tuple containing filtered and removed point clouds.
"""
# Convert numpy array to torch tensor if needed
if isinstance(obj_pc, np.ndarray):
obj_pc = torch.from_numpy(obj_pc)
obj_pc = obj_pc.float()
obj_pc = obj_pc.unsqueeze(0)
nn_dists, _ = knn_points(obj_pc[0], K=K, norm=1)
mask = nn_dists.mean(1) < threshold
filtered_pc = obj_pc[0, mask]
removed_pc = obj_pc[0][~mask]
filtered_pc = filtered_pc.view(-1, 3)
removed_pc = removed_pc.view(-1, 3)
logger.info(
f"Removed {obj_pc.shape[1] - filtered_pc.shape[0]} points from point cloud"
)
return filtered_pc, removed_pc
def point_cloud_outlier_removal_with_color(
obj_pc: torch.Tensor,
obj_pc_color: torch.Tensor,
threshold: float = 0.014,
K: int = 20,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Remove outliers from a point cloud with colors. K-nearest neighbors is used to compute the distance to the nearest neighbor for each point.
If the distance is greater than a threshold, the point is considered an outlier and removed.
Args:
obj_pc (torch.Tensor or np.ndarray): (N, 3) tensor or array representing the point cloud.
obj_pc_color (torch.Tensor or np.ndarray): (N, 3) tensor or array representing the point cloud color.
threshold (float): Distance threshold for outlier detection. Points with mean distance to K nearest neighbors greater than this threshold are removed.
K (int): Number of nearest neighbors to consider for outlier detection.
Returns:
Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: Tuple containing filtered and removed point clouds and colors.
"""
# Convert numpy array to torch tensor if needed
if isinstance(obj_pc, np.ndarray):
obj_pc = torch.from_numpy(obj_pc)
if isinstance(obj_pc_color, np.ndarray):
obj_pc_color = torch.from_numpy(obj_pc_color)
obj_pc = obj_pc.float()
obj_pc = obj_pc.unsqueeze(0)
obj_pc_color = obj_pc_color.float()
obj_pc_color = obj_pc_color.unsqueeze(0)
nn_dists, _ = knn_points(obj_pc[0], K=K, norm=1)
mask = nn_dists.mean(1) < threshold
filtered_pc = obj_pc[0, mask]
removed_pc = obj_pc[0][~mask]
filtered_pc = filtered_pc.view(-1, 3)
removed_pc = removed_pc.view(-1, 3)
filtered_pc_color = obj_pc_color[0, mask]
removed_pc_color = obj_pc_color[0][~mask]
filtered_pc_color = filtered_pc_color.view(-1, 3)
removed_pc_color = removed_pc_color.view(-1, 3)
logger.info(
f"Removed {obj_pc.shape[1] - filtered_pc.shape[0]} points from point cloud"
)
return filtered_pc, removed_pc, filtered_pc_color, removed_pc_color
def depth_and_segmentation_to_point_clouds(
depth_image: np.ndarray,
segmentation_mask: np.ndarray,
fx: float,
fy: float,
cx: float,
cy: float,
rgb_image: np.ndarray = None,
target_object_id: int = 1,
remove_object_from_scene: bool = False,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""
Convert depth image and instance segmentation mask to scene and object point clouds.
Args:
depth_image: HxW depth image in meters
segmentation_mask: HxW instance segmentation mask with integer labels
fx, fy, cx, cy: Camera intrinsic parameters
rgb_image: HxWx3 RGB image (optional, for colored point clouds)
target_object_id: ID of the target object in the segmentation mask
remove_object_from_scene: If True, removes object points from scene point cloud
Returns:
scene_pc: Nx3 point cloud of the entire scene (excluding object if remove_object_from_scene=True)
object_pc: Mx3 point cloud of the target object only
scene_colors: Nx3 RGB colors for scene points (or None)
object_colors: Mx3 RGB colors for object points (or None)
Raises:
ValueError: If no target object found or multiple objects detected
"""
# Check that segmentation mask contains the target object
unique_ids = np.unique(segmentation_mask)
if target_object_id not in unique_ids:
raise ValueError(
f"Target object ID {target_object_id} not found in segmentation mask. Available IDs: {unique_ids}"
)
# Check that only background (0) and one object (target_object_id) are present
non_background_ids = unique_ids[unique_ids != 0]
if len(non_background_ids) > 1:
raise ValueError(
f"Multiple objects detected in segmentation mask: {non_background_ids}. Please ensure only one object is present."
)
# Convert depth image to point cloud
pts_data = depth2points(
depth=depth_image,
fx=int(fx),
fy=int(fy),
cx=int(cx),
cy=int(cy),
rgb=rgb_image,
seg=segmentation_mask,
)
xyz = pts_data["xyz"]
rgb = pts_data["rgb"]
seg = pts_data["seg"]
index = pts_data["index"]
# Filter valid points (non-zero depth)
xyz_valid = xyz[index]
seg_valid = seg[index] if seg is not None else None
rgb_valid = rgb[index] if rgb is not None else None
# Scene point cloud (all valid points)
scene_pc = xyz_valid
scene_colors = rgb_valid
# Object point cloud (only target object points)
if seg_valid is not None:
object_mask = seg_valid.flatten() == target_object_id
object_pc = xyz_valid[object_mask]
object_colors = rgb_valid[object_mask] if rgb_valid is not None else None
# Scene point cloud (optionally excluding object points)
if remove_object_from_scene:
scene_mask = ~object_mask # Invert object mask to get scene-only points
scene_pc = xyz_valid[scene_mask]
scene_colors = rgb_valid[scene_mask] if rgb_valid is not None else None
logger.info(
f"Removed {np.sum(object_mask)} object points from scene point cloud"
)
else:
raise ValueError("Segmentation data not available from depth2points")
if len(object_pc) == 0:
raise ValueError(f"No points found for target object ID {target_object_id}")
logger.info(f"Scene point cloud: {len(scene_pc)} points")
logger.info(f"Object point cloud: {len(object_pc)} points")
return scene_pc, object_pc, scene_colors, object_colors
def filter_colliding_grasps(
scene_pc: np.ndarray,
grasp_poses: np.ndarray,
gripper_collision_mesh: trimesh.Trimesh,
collision_threshold: float = 0.002,
num_collision_samples: int = 2000,
) -> np.ndarray:
"""
Filter grasps based on collision detection with scene point cloud.
Args:
scene_pc: Nx3 scene point cloud
grasp_poses: Kx4x4 array of grasp poses
gripper_collision_mesh: Trimesh of gripper collision geometry
collision_threshold: Distance threshold for collision detection (meters)
num_collision_samples: Number of points to sample from gripper mesh surface
Returns:
collision_mask: K-length boolean array, True if grasp is collision-free
"""
# Sample points from gripper collision mesh surface
gripper_surface_points, _ = trimesh.sample.sample_surface(
gripper_collision_mesh, num_collision_samples
)
gripper_surface_points = np.array(gripper_surface_points)
# Convert inputs to torch tensors
scene_pc_torch = torch.from_numpy(scene_pc).float()
collision_free_mask = []
logger.info(
f"Checking collision for {len(grasp_poses)} grasps against {len(scene_pc)} scene points..."
)
for i, grasp_pose in tqdm(
enumerate(grasp_poses), total=len(grasp_poses), desc="Collision checking"
):
# Transform gripper collision points to grasp pose
gripper_points_transformed = tra.transform_points(
gripper_surface_points, grasp_pose
)
gripper_points_torch = torch.from_numpy(gripper_points_transformed).float()
# For each gripper point, find distance to closest scene point
min_distances = []
# Process in batches to avoid memory issues
batch_size = 100
for j in range(0, len(gripper_points_torch), batch_size):
batch_gripper_points = gripper_points_torch[j : j + batch_size]
# Compute distances from batch of gripper points to all scene points
distances = torch.cdist(
batch_gripper_points, scene_pc_torch, p=2
) # Euclidean distance
batch_min_distances = torch.min(distances, dim=1)[0]
min_distances.append(batch_min_distances)
# Concatenate all minimum distances
all_min_distances = torch.cat(min_distances)
# Check if any gripper point is within collision threshold of scene points
collision_detected = torch.any(all_min_distances < collision_threshold)
collision_free_mask.append(not collision_detected.item())
collision_free_mask = np.array(collision_free_mask)
num_collision_free = np.sum(collision_free_mask)
logger.info(f"Found {num_collision_free}/{len(grasp_poses)} collision-free grasps")
return collision_free_mask
def depth_and_segmentation_to_point_clouds(
depth_image: np.ndarray,
segmentation_mask: np.ndarray,
fx: float,
fy: float,
cx: float,
cy: float,
rgb_image: np.ndarray = None,
target_object_id: int = 1,
remove_object_from_scene: bool = False,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""
Convert depth image and instance segmentation mask to scene and object point clouds.
Args:
depth_image: HxW depth image in meters
segmentation_mask: HxW instance segmentation mask with integer labels
fx, fy, cx, cy: Camera intrinsic parameters
rgb_image: HxWx3 RGB image (optional, for colored point clouds)
target_object_id: ID of the target object in the segmentation mask
remove_object_from_scene: If True, removes object points from scene point cloud
Returns:
scene_pc: Nx3 point cloud of the entire scene (excluding object if remove_object_from_scene=True)
object_pc: Mx3 point cloud of the target object only
scene_colors: Nx3 RGB colors for scene points (or None)
object_colors: Mx3 RGB colors for object points (or None)
Raises:
ValueError: If no target object found or multiple objects detected
"""
# Check that segmentation mask contains the target object
unique_ids = np.unique(segmentation_mask)
if target_object_id not in unique_ids:
raise ValueError(
f"Target object ID {target_object_id} not found in segmentation mask. Available IDs: {unique_ids}"
)
# Check that only background (0) and one object (target_object_id) are present
non_background_ids = unique_ids[unique_ids != 0]
if len(non_background_ids) > 1:
raise ValueError(
f"Multiple objects detected in segmentation mask: {non_background_ids}. Please ensure only one object is present."
)
# Convert depth image to point cloud
pts_data = depth2points(
depth=depth_image,
fx=int(fx),
fy=int(fy),
cx=int(cx),
cy=int(cy),
rgb=rgb_image,
seg=segmentation_mask,
)
xyz = pts_data["xyz"]
rgb = pts_data["rgb"]
seg = pts_data["seg"]
index = pts_data["index"]
# Filter valid points (non-zero depth)
xyz_valid = xyz[index]
seg_valid = seg[index] if seg is not None else None
rgb_valid = rgb[index] if rgb is not None else None
# Scene point cloud (all valid points)
scene_pc = xyz_valid
scene_colors = rgb_valid
# Object point cloud (only target object points)
if seg_valid is not None:
object_mask = seg_valid.flatten() == target_object_id
object_pc = xyz_valid[object_mask]
object_colors = rgb_valid[object_mask] if rgb_valid is not None else None
# Scene point cloud (optionally excluding object points)
if remove_object_from_scene:
scene_mask = ~object_mask # Invert object mask to get scene-only points
scene_pc = xyz_valid[scene_mask]
scene_colors = rgb_valid[scene_mask] if rgb_valid is not None else None
logger.info(
f"Removed {np.sum(object_mask)} object points from scene point cloud"
)
else:
raise ValueError("Segmentation data not available from depth2points")
if len(object_pc) == 0:
raise ValueError(f"No points found for target object ID {target_object_id}")
logger.info(f"Scene point cloud: {len(scene_pc)} points")
logger.info(f"Object point cloud: {len(object_pc)} points")
return scene_pc, object_pc, scene_colors, object_colors
def filter_colliding_grasps(
scene_pc: np.ndarray,
grasp_poses: np.ndarray,
gripper_collision_mesh: trimesh.Trimesh,
collision_threshold: float = 0.002,
num_collision_samples: int = 2000,
) -> np.ndarray:
"""
Filter grasps based on collision detection with scene point cloud.
Args:
scene_pc: Nx3 scene point cloud
grasp_poses: Kx4x4 array of grasp poses
gripper_collision_mesh: Trimesh of gripper collision geometry
collision_threshold: Distance threshold for collision detection (meters)
num_collision_samples: Number of points to sample from gripper mesh surface
Returns:
collision_mask: K-length boolean array, True if grasp is collision-free
"""
# Sample points from gripper collision mesh surface
gripper_surface_points, _ = trimesh.sample.sample_surface(
gripper_collision_mesh, num_collision_samples
)
gripper_surface_points = np.array(gripper_surface_points)
# Convert inputs to torch tensors
scene_pc_torch = torch.from_numpy(scene_pc).float()
collision_free_mask = []
logger.info(
f"Checking collision for {len(grasp_poses)} grasps against {len(scene_pc)} scene points..."
)
for i, grasp_pose in tqdm(
enumerate(grasp_poses), total=len(grasp_poses), desc="Collision checking"
):
# Transform gripper collision points to grasp pose
gripper_points_transformed = tra.transform_points(
gripper_surface_points, grasp_pose
)
gripper_points_torch = torch.from_numpy(gripper_points_transformed).float()
# For each gripper point, find distance to closest scene point
min_distances = []
# Process in batches to avoid memory issues
batch_size = 100
for j in range(0, len(gripper_points_torch), batch_size):
batch_gripper_points = gripper_points_torch[j : j + batch_size]
# Compute distances from batch of gripper points to all scene points
distances = torch.cdist(
batch_gripper_points, scene_pc_torch, p=2
) # Euclidean distance
batch_min_distances = torch.min(distances, dim=1)[0]
min_distances.append(batch_min_distances)
# Concatenate all minimum distances
all_min_distances = torch.cat(min_distances)
# Check if any gripper point is within collision threshold of scene points
collision_detected = torch.any(all_min_distances < collision_threshold)
collision_free_mask.append(not collision_detected.item())
collision_free_mask = np.array(collision_free_mask)
num_collision_free = np.sum(collision_free_mask)
logger.info(f"Found {num_collision_free}/{len(grasp_poses)} collision-free grasps")
return collision_free_mask
================================================
FILE: grasp_gen/utils/rotation_conversions.py
================================================
# Copyright (c) 2025, 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.
# pyre-unsafe
from typing import Optional, Union
import torch
import torch.nn.functional as F
Device = Union[str, torch.device]
"""
The transformation matrices returned from the functions in this file assume
the points on which the transformation will be applied are column vectors.
i.e. the R matrix is structured as
R = [
[Rxx, Rxy, Rxz],
[Ryx, Ryy, Ryz],
[Rzx, Rzy, Rzz],
] # (3, 3)
This matrix can be applied to column vectors by post multiplication
by the points e.g.
points = [[0], [1], [2]] # (3 x 1) xyz coordinates of a point
transformed_points = R * points
To apply the same matrix to points which are row vectors, the R matrix
can be transposed and pre multiplied by the points:
e.g.
points = [[0, 1, 2]] # (1 x 3) xyz coordinates of a point
transformed_points = points * R.transpose(1, 0)
"""
def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor:
"""
Convert rotations given as quaternions to rotation matrices.
Args:
quaternions: quaternions with real part first,
as tensor of shape (..., 4).
Returns:
Rotation matrices as tensor of shape (..., 3, 3).
"""
r, i, j, k = torch.unbind(quaternions, -1)
# pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
two_s = 2.0 / (quaternions * quaternions).sum(-1)
o = torch.stack(
(
1 - two_s * (j * j + k * k),
two_s * (i * j - k * r),
two_s * (i * k + j * r),
two_s * (i * j + k * r),
1 - two_s * (i * i + k * k),
two_s * (j * k - i * r),
two_s * (i * k - j * r),
two_s * (j * k + i * r),
1 - two_s * (i * i + j * j),
),
-1,
)
return o.reshape(quaternions.shape[:-1] + (3, 3))
def _copysign(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
"""
Return a tensor where each element has the absolute value taken from the,
corresponding element of a, with sign taken from the corresponding
element of b. This is like the standard copysign floating-point operation,
but is not careful about negative 0 and NaN.
Args:
a: source tensor.
b: tensor whose signs will be used, of the same shape as a.
Returns:
Tensor of the same shape as a with the signs of b.
"""
signs_differ = (a < 0) != (b < 0)
return torch.where(signs_differ, -a, a)
def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
"""
Returns torch.sqrt(torch.max(0, x))
but with a zero subgradient where x is 0.
"""
ret = torch.zeros_like(x)
positive_mask = x > 0
if torch.is_grad_enabled():
ret[positive_mask] = torch.sqrt(x[positive_mask])
else:
ret = torch.where(positive_mask, torch.sqrt(x), ret)
return ret
def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
"""
Convert rotations given as rotation matrices to quaternions.
Args:
matrix: Rotation matrices as tensor of shape (..., 3, 3).
Returns:
quaternions with real part first, as tensor of shape (..., 4).
"""
if matrix.size(-1) != 3 or matrix.size(-2) != 3:
raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")
batch_dim = matrix.shape[:-2]
m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(
matrix.reshape(batch_dim + (9,)), dim=-1
)
q_abs = _sqrt_positive_part(
torch.stack(
[
1.0 + m00 + m11 + m22,
1.0 + m00 - m11 - m22,
1.0 - m00 + m11 - m22,
1.0 - m00 - m11 + m22,
],
dim=-1,
)
)
# we produce the desired quaternion multiplied by each of r, i, j, k
quat_by_rijk = torch.stack(
[
# pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
# `int`.
torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
# pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
# `int`.
torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
# pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
# `int`.
torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
# pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
# `int`.
torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
],
dim=-2,
)
# We floor here at 0.1 but the exact level is not important; if q_abs is small,
# the candidate won't be picked.
flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))
# if not for numerical problems, quat_candidates[i] should be same (up to a sign),
# forall i; we pick the best-conditioned one (with the largest denominator)
out = quat_candidates[
F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :
].reshape(batch_dim + (4,))
return standardize_quaternion(out)
def _axis_angle_rotation(axis: str, angle: torch.Tensor) -> torch.Tensor:
"""
Return the rotation matrices for one of the rotations about an axis
of which Euler angles describe, for each value of the angle given.
Args:
axis: Axis label "X" or "Y or "Z".
angle: any shape tensor of Euler angles in radians
Returns:
Rotation matrices as tensor of shape (..., 3, 3).
"""
cos = torch.cos(angle)
sin = torch.sin(angle)
one = torch.ones_like(angle)
zero = torch.zeros_like(angle)
if axis == "X":
R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos)
elif axis == "Y":
R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos)
elif axis == "Z":
R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one)
else:
raise ValueError("letter must be either X, Y or Z.")
return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3))
def euler_angles_to_matrix(euler_angles: torch.Tensor, convention: str) -> torch.Tensor:
"""
Convert rotations given as Euler angles in radians to rotation matrices.
Args:
euler_angles: Euler angles in radians as tensor of shape (..., 3).
convention: Convention string of three uppercase letters from
{"X", "Y", and "Z"}.
Returns:
Rotation matrices as tensor of shape (..., 3, 3).
"""
if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3:
raise ValueError("Invalid input euler angles.")
if len(convention) != 3:
raise ValueError("Convention must have 3 letters.")
if convention[1] in (convention[0], convention[2]):
raise ValueError(f"Invalid convention {convention}.")
for letter in convention:
if letter not in ("X", "Y", "Z"):
raise ValueError(f"Invalid letter {letter} in convention string.")
matrices = [
_axis_angle_rotation(c, e)
for c, e in zip(convention, torch.unbind(euler_angles, -1))
]
# return functools.reduce(torch.matmul, matrices)
return torch.matmul(torch.matmul(matrices[0], matrices[1]), matrices[2])
def _angle_from_tan(
axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool
) -> torch.Tensor:
"""
Extract the first or third Euler angle from the two members of
the matrix which are positive constant times its sine and cosine.
Args:
axis: Axis label "X" or "Y or "Z" for the angle we are finding.
other_axis: Axis label "X" or "Y or "Z" for the middle axis in the
convention.
data: Rotation matrices as tensor of shape (..., 3, 3).
horizontal: Whether we are looking for the angle for the third axis,
which means the relevant entries are in the same row of the
rotation matrix. If not, they are in the same column.
tait_bryan: Whether the first and third axes in the convention differ.
Returns:
Euler Angles in radians for each matrix in data as a tensor
of shape (...).
"""
i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis]
if horizontal:
i2, i1 = i1, i2
even = (axis + other_axis) in ["XY", "YZ", "ZX"]
if horizontal == even:
return torch.atan2(data[..., i1], data[..., i2])
if tait_bryan:
return torch.atan2(-data[..., i2], data[..., i1])
return torch.atan2(data[..., i2], -data[..., i1])
def _index_from_letter(letter: str) -> int:
if letter == "X":
return 0
if letter == "Y":
return 1
if letter == "Z":
return 2
raise ValueError("letter must be either X, Y or Z.")
def matrix_to_euler_angles(matrix: torch.Tensor, convention: str) -> torch.Tensor:
"""
Convert rotations given as rotation matrices to Euler angles in radians.
Args:
matrix: Rotation matrices as tensor of shape (..., 3, 3).
convention: Convention string of three uppercase letters.
Returns:
Euler angles in radians as tensor of shape (..., 3).
"""
if len(convention) != 3:
raise ValueError("Convention must have 3 letters.")
if convention[1] in (convention[0], convention[2]):
raise ValueError(f"Invalid convention {convention}.")
for letter in convention:
if letter not in ("X", "Y", "Z"):
raise ValueError(f"Invalid letter {letter} in convention string.")
if matrix.size(-1) != 3 or matrix.size(-2) != 3:
raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")
i0 = _index_from_letter(convention[0])
i2 = _index_from_letter(convention[2])
tait_bryan = i0 != i2
if tait_bryan:
central_angle = torch.asin(
matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0)
)
else:
central_angle = torch.acos(matrix[..., i0, i0])
o = (
_angle_from_tan(
convention[0], convention[1], matrix[..., i2], False, tait_bryan
),
central_angle,
_angle_from_tan(
convention[2], convention[1], matrix[..., i0, :], True, tait_bryan
),
)
return torch.stack(o, -1)
def random_quaternions(
n: int, dtype: Optional[torch.dtype] = None, device: Optional[Device] = None
) -> torch.Tensor:
"""
Generate random quaternions representing rotations,
i.e. versors with nonnegative real part.
Args:
n: Number of quaternions in a batch to return.
dtype: Type to return.
device: Desired device of returned tensor. Default:
uses the current device for the default tensor type.
Returns:
Quaternions as tensor of shape (N, 4).
"""
if isinstance(device, str):
device = torch.device(device)
o = torch.randn((n, 4), dtype=dtype, device=device)
s = (o * o).sum(1)
o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None]
return o
def random_rotations(
n: int, dtype: Optional[torch.dtype] = None, device: Optional[Device] = None
) -> torch.Tensor:
"""
Generate random rotations as 3x3 rotation matrices.
Args:
n: Number of rotation matrices in a batch to return.
dtype: Type to return.
device: Device of returned tensor. Default: if None,
uses the current device for the default tensor type.
Returns:
Rotation matrices as tensor of shape (n, 3, 3).
"""
quaternions = random_quaternions(n, dtype=dtype, device=device)
return quaternion_to_matrix(quaternions)
def random_rotation(
dtype: Optional[torch.dtype] = None, device: Optional[Device] = None
) -> torch.Tensor:
"""
Generate a single random 3x3 rotation matrix.
Args:
dtype: Type to return
device: Device of returned tensor. Default: if None,
uses the current device for the default tensor type
Returns:
Rotation matrix as tensor of shape (3, 3).
"""
return random_rotations(1, dtype, device)[0]
def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor:
"""
Convert a unit quaternion to a standard form: one in which the real
part is non negative.
Args:
quaternions: Quaternions with real part first,
as tensor of shape (..., 4).
Returns:
Standardized quaternions as tensor of shape (..., 4).
"""
return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions)
def quaternion_raw_multiply(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
"""
Multiply two quaternions.
Usual torch rules for broadcasting apply.
Args:
a: Quaternions as tensor of shape (..., 4), real part first.
b: Quaternions as tensor of shape (..., 4), real part first.
Returns:
The product of a and b, a tensor of quaternions shape (..., 4).
"""
aw, ax, ay, az = torch.unbind(a, -1)
bw, bx, by, bz = torch.unbind(b, -1)
ow = aw * bw - ax * bx - ay * by - az * bz
ox = aw * bx + ax * bw + ay * bz - az * by
oy = aw * by - ax * bz + ay * bw + az * bx
oz = aw * bz + ax * by - ay * bx + az * bw
return torch.stack((ow, ox, oy, oz), -1)
def quaternion_multiply(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
"""
Multiply two quaternions representing rotations, returning the quaternion
representing their composition, i.e. the versor with nonnegative real part.
Usual torch rules for broadcasting apply.
Args:
a: Quaternions as tensor of shape (..., 4), real part first.
b: Quaternions as tensor of shape (..., 4), real part first.
Returns:
The product of a and b, a tensor of quaternions of shape (..., 4).
"""
ab = quaternion_raw_multiply(a, b)
return standardize_quaternion(ab)
def quaternion_invert(quaternion: torch.Tensor) -> torch.Tensor:
"""
Given a quaternion representing rotation, get the quaternion representing
its inverse.
Args:
quaternion: Quaternions as tensor of shape (..., 4), with real part
first, which must be versors (unit quaternions).
Returns:
The inverse, a tensor of quaternions of shape (..., 4).
"""
scaling = torch.tensor([1, -1, -1, -1], device=quaternion.device)
return quaternion * scaling
def quaternion_apply(quaternion: torch.Tensor, point: torch.Tensor) -> torch.Tensor:
"""
Apply the rotation given by a quaternion to a 3D point.
Usual torch rules for broadcasting apply.
Args:
quaternion: Tensor of quaternions, real part first, of shape (..., 4).
point: Tensor of 3D points of shape (..., 3).
Returns:
Tensor of rotated points of shape (..., 3).
"""
if point.size(-1) != 3:
raise ValueError(f"Points are not in 3D, {point.shape}.")
real_parts = point.new_zeros(point.shape[:-1] + (1,))
point_as_quaternion = torch.cat((real_parts, point), -1)
out = quaternion_raw_multiply(
quaternion_raw_multiply(quaternion, point_as_quaternion),
quaternion_invert(quaternion),
)
return out[..., 1:]
def axis_angle_to_matrix(axis_angle: torch.Tensor, fast: bool = False) -> torch.Tensor:
"""
Convert rotations given as axis/angle to rotation matrices.
Args:
axis_angle: Rotations given as a vector in axis angle form,
as a tensor of shape (..., 3), where the magnitude is
the angle turned anticlockwise in radians around the
vector's direction.
fast: Whether to use the new faster implementation (based on the
Rodrigues formula) instead of the original implementation (which
first converted to a quaternion and then back to a rotation matrix).
Returns:
Rotation matrices as tensor of shape (..., 3, 3).
"""
if not fast:
return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle))
shape = axis_angle.shape
device, dtype = axis_angle.device, axis_angle.dtype
angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True).unsqueeze(-1)
rx, ry, rz = axis_angle[..., 0], axis_angle[..., 1], axis_angle[..., 2]
zeros = torch.zeros(shape[:-1], dtype=dtype, device=device)
cross_product_matrix = torch.stack(
[zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=-1
).view(shape + (3,))
cross_product_matrix_sqrd = cross_product_matrix @ cross_product_matrix
identity = torch.eye(3, dtype=dtype, device=device)
angles_sqrd = angles * angles
angles_sqrd = torch.where(angles_sqrd == 0, 1, angles_sqrd)
return (
identity.expand(cross_product_matrix.shape)
+ torch.sinc(angles / torch.pi) * cross_product_matrix
+ ((1 - torch.cos(angles)) / angles_sqrd) * cross_product_matrix_sqrd
)
def matrix_to_axis_angle(matrix: torch.Tensor, fast: bool = False) -> torch.Tensor:
"""
Convert rotations given as rotation matrices to axis/angle.
Args:
matrix: Rotation matrices as tensor of shape (..., 3, 3).
fast: Whether to use the new faster implementation (based on the
Rodrigues formula) instead of the original implementation (which
first converted to a quaternion and then back to a rotation matrix).
Returns:
Rotations given as a vector in axis angle form, as a tensor
of shape (..., 3), where the magnitude is the angle
turned anticlockwise in radians around the vector's
direction.
"""
if not fast:
return quaternion_to_axis_angle(matrix_to_quaternion(matrix))
if matrix.size(-1) != 3 or matrix.size(-2) != 3:
raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")
omegas = torch.stack(
[
matrix[..., 2, 1] - matrix[..., 1, 2],
matrix[..., 0, 2] - matrix[..., 2, 0],
matrix[..., 1, 0] - matrix[..., 0, 1],
],
dim=-1,
)
norms = torch.norm(omegas, p=2, dim=-1, keepdim=True)
traces = torch.diagonal(matrix, dim1=-2, dim2=-1).sum(-1).unsqueeze(-1)
angles = torch.atan2(norms, traces - 1)
zeros = torch.zeros(3, dtype=matrix.dtype, device=matrix.device)
omegas = torch.where(torch.isclose(angles, torch.zeros_like(angles)), zeros, omegas)
near_pi = torch.isclose(((traces - 1) / 2).abs(), torch.ones_like(traces)).squeeze(
-1
)
axis_angles = torch.empty_like(omegas)
axis_angles[~near_pi] = (
0.5 * omegas[~near_pi] / torch.sinc(angles[~near_pi] / torch.pi)
)
# this derives from: nnT = (R + 1) / 2
n = 0.5 * (
matrix[near_pi][..., 0, :]
+ torch.eye(1, 3, dtype=matrix.dtype, device=matrix.device)
)
axis_angles[near_pi] = angles[near_pi] * n / torch.norm(n)
return axis_angles
def axis_angle_to_quaternion(axis_angle: torch.Tensor) -> torch.Tensor:
"""
Convert rotations given as axis/angle to quaternions.
Args:
axis_angle: Rotations given as a vector in axis angle form,
as a tensor of shape (..., 3), where the magnitude is
the angle turned anticlockwise in radians around the
vector's direction.
Returns:
quaternions with real part first, as tensor of shape (..., 4).
"""
angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True)
sin_half_angles_over_angles = 0.5 * torch.sinc(angles * 0.5 / torch.pi)
return torch.cat(
[torch.cos(angles * 0.5), axis_angle * sin_half_angles_over_angles], dim=-1
)
def quaternion_to_axis_angle(quaternions: torch.Tensor) -> torch.Tensor:
"""
Convert rotations given as quaternions to axis/angle.
Args:
quaternions: quaternions with real part first,
as tensor of shape (..., 4).
Returns:
Rotations given as a vector in axis angle form, as a tensor
of shape (..., 3), where the magnitude is the angle
turned anticlockwise in radians around the vector's
direction.
"""
norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True)
half_angles = torch.atan2(norms, quaternions[..., :1])
sin_half_angles_over_angles = 0.5 * torch.sinc(half_angles / torch.pi)
# angles/2 are between [-pi/2, pi/2], thus sin_half_angles_over_angles
# can't be zero
return quaternions[..., 1:] / sin_half_angles_over_angles
def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
"""
Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
using Gram--Schmidt orthogonalization per Section B of [1].
Args:
d6: 6D rotation representation, of size (*, 6)
Returns:
batch of rotation matrices of size (*, 3, 3)
[1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
On the Continuity of Rotation Representations in Neural Networks.
IEEE Conference on Computer Vision and Pattern Recognition, 2019.
Retrieved from http://arxiv.org/abs/1812.07035
"""
a1, a2 = d6[..., :3], d6[..., 3:]
b1 = F.normalize(a1, dim=-1)
b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
b2 = F.normalize(b2, dim=-1)
b3 = torch.cross(b1, b2, dim=-1)
return torch.stack((b1, b2, b3), dim=-2)
def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
"""
Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
by dropping the last row. Note that 6D representation is not unique.
Args:
matrix: batch of rotation matrices of size (*, 3, 3)
Returns:
6D rotation representation, of size (*, 6)
[1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
On the Continuity of Rotation Representations in Neural Networks.
IEEE Conference on Computer Vision and Pattern Recognition, 2019.
Retrieved from http://arxiv.org/abs/1812.07035
"""
batch_dim = matrix.size()[:-2]
return matrix[..., :2, :].clone().reshape(batch_dim + (6,))
================================================
FILE: grasp_gen/utils/so3.py
================================================
# Copyright (c) 2025, 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.
from typing import Tuple
import torch
import grasp_gen.utils.rotation_conversions as rotation_conversions
def hat(v: torch.Tensor) -> torch.Tensor:
"""
Compute the Hat operator [1] of a batch of 3D vectors.
Args:
v: Batch of vectors of shape `(minibatch , 3)`.
Returns:
Batch of skew-symmetric matrices of shape
`(minibatch, 3 , 3)` where each matrix is of the form:
`[ 0 -v_z v_y ]
[ v_z 0 -v_x ]
[ -v_y v_x 0 ]`
Raises:
ValueError if `v` is of incorrect shape.
[1] https://en.wikipedia.org/wiki/Hat_operator
"""
N, dim = v.shape
if dim != 3:
raise ValueError("Input vectors have to be 3-dimensional.")
h = torch.zeros((N, 3, 3), dtype=v.dtype, device=v.device)
x, y, z = v.unbind(1)
h[:, 0, 1] = -z
h[:, 0, 2] = y
h[:, 1, 0] = z
h[:, 1, 2] = -x
h[:, 2, 0] = -y
h[:, 2, 1] = x
return h
def so3_exp_map(log_rot: torch.Tensor, eps: float = 0.0001) -> torch.Tensor:
"""
Convert a batch of logarithmic representations of rotation matrices `log_rot`
to a batch of 3x3 rotation matrices using Rodrigues formula [1].
In the logarithmic representation, each rotation matrix is represented as
a 3-dimensional vector (`log_rot`) who's l2-norm and direction correspond
to the magnitude of the rotation angle and the axis of rotation respectively.
The conversion has a singularity around `log(R) = 0`
which is handled by clamping controlled with the `eps` argument.
Args:
log_rot: Batch of vectors of shape `(minibatch, 3)`.
eps: A float constant handling the conversion singularity.
Returns:
Batch of rotation matrices of shape `(minibatch, 3, 3)`.
Raises:
ValueError if `log_rot` is of incorrect shape.
[1] https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula
"""
return _so3_exp_map(log_rot, eps=eps)[0]
def _so3_exp_map(
log_rot: torch.Tensor, eps: float = 0.0001
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""
A helper function that computes the so3 exponential map and,
apart from the rotation matrix, also returns intermediate variables
that can be re-used in other functions.
"""
_, dim = log_rot.shape
if dim != 3:
raise ValueError("Input tensor shape has to be Nx3.")
nrms = (log_rot * log_rot).sum(1)
# phis ... rotation angles
rot_angles = torch.clamp(nrms, eps).sqrt()
skews = hat(log_rot)
skews_square = torch.bmm(skews, skews)
R = rotation_conversions.axis_angle_to_matrix(log_rot)
return R, rot_angles, skews, skews_square
def so3_log_map(
R: torch.Tensor, eps: float = 0.0001, cos_bound: float = 1e-4
) -> torch.Tensor:
"""
Convert a batch of 3x3 rotation matrices `R`
to a batch of 3-dimensional matrix logarithms of rotation matrices
The conversion has a singularity around `(R=I)`.
Args:
R: batch of rotation matrices of shape `(minibatch, 3, 3)`.
eps: (unused, for backward compatibility)
cos_bound: (unused, for backward compatibility)
Returns:
Batch of logarithms of input rotation matrices
of shape `(minibatch, 3)`.
"""
N, dim1, dim2 = R.shape
if dim1 != 3 or dim2 != 3:
raise ValueError("Input has to be a batch of 3x3 Tensors.")
return rotation_conversions.matrix_to_axis_angle(R)
================================================
FILE: grasp_gen/utils/train_utils.py
================================================
# Copyright (c) 2025, 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.
#
# Author: Wentao Yuan, Adithya Murali
"""
Utility functions for training models.
"""
import copy
import os
import sys
import numpy as np
import torch
from omegaconf.listconfig import ListConfig
from torch.utils.data import ConcatDataset, DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from grasp_gen.dataset.dataset import ObjectPickDataset, collate
from grasp_gen.utils.logging_config import get_logger
logger = get_logger(__name__)
def worker_init_fn(worker_id):
np.random.seed(np.random.get_state()[1][0] + worker_id)
def get_data_loader(cfg, data_cfg, split, scenes, use_ddp, training, inference=False):
DatasetCls = getattr(sys.modules[__name__], data_cfg.dataset_cls)
kwargs = DatasetCls.from_config(data_cfg)
if not training:
kwargs["jitter_scale"] = 0
kwargs["robot_prob"] = 1
if kwargs["visualize_batch"]:
cfg.num_workers = 0
if type(kwargs["object_root_dir"]) == ListConfig:
# Multiple datasets have been passed in!
list_object_root_dir = kwargs["object_root_dir"]
list_grasp_root_dir = kwargs["grasp_root_dir"]
list_root_dir = kwargs["root_dir"]
list_versions = kwargs["dataset_version"]
list_onpolicy_dataset_dir = kwargs["onpolicy_dataset_dir"]
list_onpolicy_dataset_h5_path = kwargs["onpolicy_dataset_h5_path"]
if list_onpolicy_dataset_dir == None:
list_onpolicy_dataset_dir = [None] * len(list_object_root_dir)
list_onpolicy_dataset_h5_path = [None] * len(list_object_root_dir)
assert (
len(list_object_root_dir)
== len(list_grasp_root_dir)
== len(list_root_dir)
== len(list_versions)
== len(list_onpolicy_dataset_dir)
== len(list_onpolicy_dataset_h5_path)
), "Invalid list of datasets!"
datasets = []
for (
object_root_dir,
grasp_root_dir,
root_dir,
dataset_version,
onpolicy_dataset_dir,
onpolicy_dataset_h5_path,
) in zip(
list_object_root_dir,
list_grasp_root_dir,
list_root_dir,
list_versions,
list_onpolicy_dataset_dir,
list_onpolicy_dataset_h5_path,
):
kwargs_dataset = kwargs.copy()
kwargs_dataset["object_root_dir"] = object_root_dir
kwargs_dataset["grasp_root_dir"] = grasp_root_dir
kwargs_dataset["root_dir"] = root_dir
kwargs_dataset["dataset_version"] = dataset_version
# Hack
if onpolicy_dataset_dir in ["", "None"]:
onpolicy_dataset_dir = None
if onpolicy_dataset_h5_path in ["", "None"]:
onpolicy_dataset_h5_path = None
kwargs_dataset["onpolicy_dataset_dir"] = onpolicy_dataset_dir
kwargs_dataset["onpolicy_dataset_h5_path"] = onpolicy_dataset_h5_path
dataset = DatasetCls(
**kwargs_dataset, split=split, scenes=scenes, inference=inference
)
datasets.append(dataset)
logger.info(f"Concatenating {len(datasets)} datasets into one!")
dataset = ConcatDataset(datasets)
else:
# Single dataset
assert type(kwargs["object_root_dir"]) == str
dataset = DatasetCls(**kwargs, split=split, scenes=scenes, inference=inference)
logger.info(f"Dataset for {split} has {len(dataset)} datapoints")
if use_ddp:
sampler = DistributedSampler(dataset, shuffle=training, drop_last=False)
else:
sampler = RandomSampler(dataset) if training else SequentialSampler(dataset)
persistent = cfg.num_workers > 0
loader = DataLoader(
dataset,
cfg.batch_size,
sampler=sampler,
num_workers=cfg.num_workers,
collate_fn=collate,
persistent_workers=persistent,
pin_memory=True,
worker_init_fn=worker_init_fn,
timeout=300 if use_ddp else 0,
)
return sampler, loader
def build_optimizer(cfg, model):
defaults = {}
defaults["lr"] = cfg.optimizer.lr * cfg.train.num_gpus
defaults["weight_decay"] = cfg.optimizer.weight_decay
norm_module_types = (
torch.nn.BatchNorm1d,
torch.nn.BatchNorm2d,
torch.nn.BatchNorm3d,
torch.nn.SyncBatchNorm,
torch.nn.GroupNorm,
torch.nn.InstanceNorm1d,
torch.nn.InstanceNorm2d,
torch.nn.InstanceNorm3d,
torch.nn.LayerNorm,
torch.nn.LocalResponseNorm,
)
params = []
memo = set()
for module_name, module in model.named_modules():
for param_name, value in module.named_parameters(recurse=False):
if not value.requires_grad:
continue
# Avoid duplicating parameters
if value in memo:
continue
memo.add(value)
hyperparams = copy.copy(defaults)
if (
"relative_position_bias_table" in param_name
or "absolute_pos_embed" in param_name
):
hyperparams["weight_decay"] = 0.0
if isinstance(module, norm_module_types) or isinstance(
module, torch.nn.Embedding
):
hyperparams["weight_decay"] = 0.0
params.append({"params": [value], **hyperparams})
if cfg.optimizer.type == "SGD":
logger.info(f"Using SGD, LR {defaults['lr']}")
optimizer = torch.optim.SGD(
params, defaults["lr"], momentum=cfg.optimizer.momentum
)
elif cfg.optimizer.type == "ADAMW":
logger.info(f"Using ADAM, LR {defaults['lr']}")
optimizer = torch.optim.AdamW(params, defaults["lr"])
return optimizer
def save_model(epoch, model, optimizer, log_dir, use_ddp, name=None, batch_idx=-1):
if use_ddp:
model_state = model.module.state_dict()
else:
model_state = model.state_dict()
ckpt = {"epoch": epoch, "model": model_state, "optimizer": optimizer.state_dict()}
if name is None:
name = f"epoch_{epoch}"
if batch_idx != -1:
ckpt["batch_idx"] = batch_idx
torch.save(ckpt, f"{log_dir}/{name}.pth")
def to_gpu(dic):
for key in dic:
if isinstance(dic[key], torch.Tensor):
dic[key] = dic[key].cuda()
elif isinstance(dic[key], list):
if isinstance(dic[key][0], torch.Tensor):
for i in range(len(dic[key])):
dic[key][i] = dic[key][i].cuda()
elif isinstance(dic[key][0], list):
for i in range(len(dic[key])):
for j in range(len(dic[key][i])):
if isinstance(dic[key][i][j], torch.Tensor):
dic[key][i][j] = dic[key][i][j].detach().cuda()
def to_cpu(dic):
for key in dic:
if isinstance(dic[key], torch.Tensor):
dic[key] = dic[key].detach().cpu()
elif isinstance(dic[key], list):
if isinstance(dic[key][0], torch.Tensor):
for i in range(len(dic[key])):
dic[key][i] = dic[key][i].detach().cpu()
elif isinstance(dic[key][0], list):
for i in range(len(dic[key])):
for j in range(len(dic[key][i])):
if isinstance(dic[key][i][j], torch.Tensor):
dic[key][i][j] = dic[key][i][j].detach().cpu()
def write_scalar_ddp(writer, key, value, step, rank, num, reduce=False, debug=False):
if debug:
logger.info(f"Rank {rank} Step {step} {key} {value.item()}")
if reduce:
try:
torch.distributed.reduce(value, dst=0)
except Exception as e:
logger.error(
f"Exception while reducing key {key}, Rank {rank}, Global step {step}"
)
logger.error(str(e))
return
if rank == 0:
val = torch.div(value, num)
if not torch.isnan(val) and not torch.isinf(val):
writer.add_scalar(key, val.item(), step)
def add_to_dict(dict, key, val):
if key not in dict:
dict[key] = 0
dict[key] += val
def get_iou(out_masks, tgt_masks, reduce=True):
intersect = out_masks & tgt_masks
union = out_masks | tgt_masks
iou = torch.nan_to_num(intersect.sum(dim=-1) / union.sum(dim=-1), nan=1)
if reduce:
iou = iou.mean()
return iou
def compute_iou(out_masks, tgt_masks, thresh=0.0, loss_masks=None, reduce=True):
iou_dict = {}
if isinstance(out_masks, list):
masks = {}
mask_any = {}
mask_list = [mask.flatten(start_dim=1) > thresh for mask in tgt_masks]
# [batch_size, num_points]
mask_any["target"] = torch.stack([mask.any(dim=0) for mask in mask_list])
# [num_objects, num_points]
masks["target"] = torch.cat(mask_list)
mask_list = [mask.flatten(start_dim=1) > thresh for mask in out_masks]
# [batch_size, num_points]
mask_any["output"] = torch.stack([mask.any(dim=0) for mask in mask_list])
# [num_objects, num_points]
masks["output"] = torch.cat(mask_list)
for key, mask_dict in zip(["scene", "object"], [mask_any, masks]):
if mask_dict["output"].shape[0] != mask_dict["target"].shape[0]:
continue
iou_dict[key] = get_iou(mask_dict["output"], mask_dict["target"], reduce)
elif loss_masks is None:
iou_dict["scene"] = get_iou(
out_masks.flatten(start_dim=1) > thresh,
tgt_masks.flatten(start_dim=1) > thresh,
reduce,
)
else:
ious = []
for out_mask, tgt_mask, loss_mask in zip(out_masks, tgt_masks, loss_masks):
if len(out_mask.shape) == len(loss_mask.shape):
out_mask = out_mask[loss_mask] > thresh
tgt_mask = tgt_mask[loss_mask] > thresh
else:
out_mask = out_mask[:, loss_mask] > thresh
tgt_mask = tgt_mask[:, loss_mask] > thresh
ious.append(get_iou(out_mask, tgt_mask))
iou_dict["scene"] = torch.stack(ious)
if reduce:
for key in iou_dict:
iou_dict[key] = iou_dict[key].mean()
return iou_dict
def clip_grad_norm(parameters, max_norm, norm_type):
r"""Clips gradient norm of an iterable of parameters.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
Args:
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized
max_norm (float or int): max norm of the gradients
norm_type (float or int): type of the used p-norm.
Can be ``'inf'`` for infinity norm.
error_if_nonfinite (bool): if True, an error is thrown if the total
norm of the gradients from :attr:`parameters` is ``nan``,
``inf``, or ``-inf``. Default: False
Returns:
Total norm of the parameter gradients (viewed as a single vector).
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
norms = []
for p in parameters:
if p.grad is None:
continue
if norm_type == "inf":
norm = p.grad.detach().abs().max()
else:
norm = torch.norm(p.grad.detach(), norm_type)
norms.append(norm)
if norm_type == "inf":
total_norm = torch.max(torch.stack(norms))
else:
total_norm = torch.norm(torch.stack(norms), norm_type)
clip_coef = torch.clamp(max_norm / total_norm.nan_to_num(), max=1.0)
for p in parameters:
p.grad.detach().mul_(clip_coef.to(p.grad.device))
return total_norm
================================================
FILE: grasp_gen/utils/viser_utils.py
================================================
# Copyright (c) 2025, 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.
#
# Author: Adithya Murali
"""
Utility functions for visualization using viser.
This module provides the same API as meshcat_utils.py but uses viser for visualization.
"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import trimesh
import trimesh.transformations as tra
import viser
import viser.transforms as vtf
from grasp_gen.robot import load_control_points_for_visualization
from grasp_gen.utils.logging_config import get_logger
logger = get_logger(__name__)
def is_rotation_matrix(M, tol=1e-4):
tag = False
I = np.identity(M.shape[0])
if (np.linalg.norm((np.matmul(M, M.T) - I)) < tol) and (
np.abs(np.linalg.det(M) - 1) < tol
):
tag = True
if tag is False:
logger.info("M @ M.T:\n", np.matmul(M, M.T))
logger.info("det:", np.linalg.det(M))
return tag
def get_color_from_score(labels, use_255_scale=False):
scale = 255.0 if use_255_scale else 1.0
if type(labels) in [np.float32, float]:
return scale * np.array([1 - labels, labels, 0])
else:
scale = 255.0 if use_255_scale else 1.0
score = scale * np.stack(
[np.ones(labels.shape[0]) - labels, labels, np.zeros(labels.shape[0])],
axis=1,
)
return score.astype(np.int32)
def rgb2hex(rgb: Tuple[int, int, int]) -> str:
"""
Converts rgb color to hex
Args:
rgb: color in rgb, e.g. (255,0,0)
"""
return "0x%02x%02x%02x" % (rgb)
def _matrix_to_wxyz_position(T: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
"""
Convert a 4x4 homogeneous transformation matrix to wxyz quaternion and position.
Args:
T: 4x4 homogeneous transformation matrix
Returns:
Tuple of (wxyz quaternion, position)
"""
# Extract rotation matrix and convert to quaternion
rotation_matrix = T[:3, :3]
so3 = vtf.SO3.from_matrix(rotation_matrix)
wxyz = so3.wxyz
# Extract translation
position = T[:3, 3]
return wxyz, position
def create_visualizer(clear=True, port: int = 8080):
"""
Create a viser server for visualization.
Args:
clear: If True, clear existing scene content
port: Port number for the viser server (default: 8080)
Returns:
viser.ViserServer instance
"""
logger.info(f"Starting viser server on http://localhost:{port}")
server = viser.ViserServer(port=port)
if clear:
server.scene.reset()
logger.info(f"Viser server running at http://localhost:{port}")
return server
def make_frame(
vis: viser.ViserServer,
name: str,
h: float = 0.15,
radius: float = 0.01,
o: float = 1.0,
T: Optional[np.ndarray] = None,
):
"""Add a red-green-blue triad to the Viser visualizer.
Args:
vis (viser.ViserServer): the visualizer
name (string): name for this frame (should be unique)
h (float): height of frame visualization (axes_length)
radius (float): radius of frame visualization (axes_radius)
o (float): opacity (not used in viser frames, kept for API compatibility)
T (4x4 numpy.array): (optional) transform to apply to this geometry
"""
if vis is None:
return
# Default identity transform
wxyz = (1.0, 0.0, 0.0, 0.0)
position = (0.0, 0.0, 0.0)
if T is not None:
is_valid = is_rotation_matrix(T[:3, :3])
if not is_valid:
raise ValueError("viser_utils: attempted to visualize invalid transform T")
wxyz, position = _matrix_to_wxyz_position(T)
vis.scene.add_frame(
name,
show_axes=True,
axes_length=h,
axes_radius=radius,
wxyz=wxyz,
position=position,
)
def visualize_mesh(
vis: viser.ViserServer,
name: str,
mesh: trimesh.Trimesh,
color: Optional[List[int]] = None,
transform: Optional[np.ndarray] = None,
):
"""Visualize a mesh in viser"""
if vis is None:
return
if color is None:
color = np.random.randint(low=0, high=256, size=3).tolist()
# Ensure color is a tuple of ints
if isinstance(color, np.ndarray):
color = color.tolist()
color_tuple = tuple(int(c) for c in color[:3])
# Default identity transform
wxyz = (1.0, 0.0, 0.0, 0.0)
position = (0.0, 0.0, 0.0)
if transform is not None:
wxyz, position = _matrix_to_wxyz_position(transform)
vis.scene.add_mesh_simple(
name,
vertices=mesh.vertices.astype(np.float32),
faces=mesh.faces.astype(np.uint32),
color=color_tuple,
wxyz=wxyz,
position=position,
)
def visualize_bbox(
vis: viser.ViserServer,
name: str,
dims: np.ndarray,
T: Optional[np.ndarray] = None,
color: Optional[List[int]] = [255, 0, 0],
):
"""Visualize a bounding box using a wireframe.
Args:
vis (viser.ViserServer): the visualizer
name (string): name for this frame (should be unique)
dims (array-like): shape (3,), dimensions of the bounding box
T (4x4 numpy.array): (optional) transform to apply to this geometry
color: RGB color tuple
"""
if vis is None:
return
# Ensure color is a tuple of ints
if isinstance(color, np.ndarray):
color = color.tolist()
color_tuple = tuple(int(c) for c in color[:3])
# Default identity transform
wxyz = (1.0, 0.0, 0.0, 0.0)
position = (0.0, 0.0, 0.0)
if T is not None:
wxyz, position = _matrix_to_wxyz_position(T)
# Convert dims to tuple
if isinstance(dims, np.ndarray):
dims = tuple(float(d) for d in dims)
vis.scene.add_box(
name,
color=color_tuple,
dimensions=dims,
wireframe=True,
wxyz=wxyz,
position=position,
)
def visualize_pointcloud(
vis: viser.ViserServer,
name: str,
pc: np.ndarray,
color: Optional[np.ndarray] = None,
transform: Optional[np.ndarray] = None,
size: float = 0.01,
**kwargs,
):
"""
Args:
vis: viser server object
name: str
pc: Nx3 or HxWx3
color: (optional) same shape as pc[0 - 255] scale or just rgb tuple
transform: (optional) 4x4 homogeneous transform
size: point size (default 0.01)
"""
if vis is None:
return
if pc.ndim == 3:
pc = pc.reshape(-1, pc.shape[-1])
# Ensure pc is (N, 3)
if pc.shape[-1] != 3:
pc = pc[:, :3]
num_points = pc.shape[0]
if color is not None:
if isinstance(color, list):
color = np.array(color)
color = np.array(color)
# Resize the color np array if needed.
if color.ndim == 3:
color = color.reshape(-1, color.shape[-1])
if color.ndim == 1:
# Single color for all points - broadcast to (N, 3)
single_color = np.array(color).flatten()[:3]
color = np.tile(single_color, (num_points, 1))
elif color.ndim == 2:
# Ensure we only have RGB (3 channels), not RGBA (4 channels)
if color.shape[-1] > 3:
color = color[:, :3]
# Ensure number of colors matches number of points
if color.shape[0] != num_points:
# If mismatch, use first color for all points or truncate/pad
if color.shape[0] > num_points:
color = color[:num_points]
else:
# Pad with the last color
padding = np.tile(color[-1:], (num_points - color.shape[0], 1))
color = np.vstack([color, padding])
# Ensure color is uint8 in range [0, 255]
color = color.astype(np.float32)
if color.size > 0 and color.max() <= 1.0:
color = (color * 255).astype(np.uint8)
else:
color = np.clip(color, 0, 255).astype(np.uint8)
else:
color = np.full((num_points, 3), 255, dtype=np.uint8)
# Final shape check
assert color.shape == (
num_points,
3,
), f"Color shape {color.shape} doesn't match expected ({num_points}, 3)"
# Default identity transform
wxyz = (1.0, 0.0, 0.0, 0.0)
position = (0.0, 0.0, 0.0)
if transform is not None:
wxyz, position = _matrix_to_wxyz_position(transform)
vis.scene.add_point_cloud(
name,
points=pc.astype(np.float32),
colors=color,
point_size=size,
wxyz=wxyz,
position=position,
)
def load_visualization_gripper_points(
gripper_name: str = "franka_panda",
) -> List[np.ndarray]:
"""
Need to return np.array of control points of shape [4, N], where is N is num points
"""
ctrl_points = []
for ctrl_pts in load_control_points_for_visualization(gripper_name):
ctrl_pts = np.array(ctrl_pts, dtype=np.float32)
ctrl_pts = np.hstack([ctrl_pts, np.ones([len(ctrl_pts), 1])])
ctrl_pts = ctrl_pts.T
ctrl_points.append(ctrl_pts)
return ctrl_points
def visualize_grasp(
vis: viser.ViserServer,
name: str,
transform: np.ndarray,
color: List[int] = [255, 0, 0],
gripper_name: str = "franka_panda",
linewidth: float = 1.0,
**kwargs,
):
"""
Visualize a grasp using line segments in viser.
Args:
vis: viser server object
name: str, name for this grasp visualization
transform: 4x4 homogeneous transform for the grasp pose
color: RGB color list
gripper_name: name of the gripper to visualize
linewidth: width of the line segments
"""
if vis is None:
return
grasp_vertices = load_visualization_gripper_points(gripper_name)
# Ensure color is a tuple of ints
if isinstance(color, np.ndarray):
color = color.tolist()
color_tuple = tuple(int(c) for c in color[:3])
# Get transform as wxyz and position
wxyz, position = _matrix_to_wxyz_position(transform.astype(float))
for i, grasp_vertex in enumerate(grasp_vertices):
# grasp_vertex is shape [4, N] where N is number of points
# We need to create line segments from consecutive points
points_3d = grasp_vertex[:3, :].T # Shape: [N, 3]
# Create line segments from consecutive points
# Each segment connects point[i] to point[i+1]
num_points = points_3d.shape[0]
if num_points < 2:
continue
# Create segments array of shape [N-1, 2, 3]
segments = np.zeros((num_points - 1, 2, 3), dtype=np.float32)
for j in range(num_points - 1):
segments[j, 0, :] = points_3d[j]
segments[j, 1, :] = points_3d[j + 1]
vis.scene.add_line_segments(
f"{name}/{i}",
points=segments,
colors=color_tuple,
line_width=linewidth,
wxyz=wxyz,
position=position,
)
def get_normals_from_mesh(
mesh: trimesh.Trimesh, contact_pts: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
from sklearn.neighbors import KDTree
points_codebook, index = mesh.sample(16000, return_index=True)
normals_codebook = mesh.face_normals[index]
contact_radius = 0.005
tree = KDTree(points_codebook)
dist, idx = tree.query(contact_pts)
matched = dist < contact_radius
idx2 = idx[matched]
normals = normals_codebook[idx2]
mask = matched[:, 0]
return normals, contact_pts[mask], mask
================================================
FILE: install_pointnet.sh
================================================
#!/bin/bash
# Installation script for pointnet2_ops with automatic CUDA environment setup
# This handles the CUDA compilation environment variables automatically
set -e # Exit on any error
echo "🔧 Setting up CUDA environment variables for pointnet2_ops compilation..."
# Set CUDA compilation environment variables
export CC=/usr/bin/g++
export CXX=/usr/bin/g++
export CUDAHOSTCXX=/usr/bin/g++
export TORCH_CUDA_ARCH_LIST="8.6"
echo "✅ CUDA environment configured"
echo "📦 Installing pointnet2_ops..."
# Navigate to pointnet2_ops directory and install
cd pointnet2_ops && pip install --no-build-isolation .
echo "🎉 pointnet2_ops installation completed successfully!"
================================================
FILE: install_uv_pointnet.sh
================================================
#!/bin/bash
# Installation script for pointnet2_ops with automatic CUDA environment setup
# This handles the CUDA compilation environment variables automatically
set -e # Exit on any error
echo "🔧 Setting up CUDA environment variables for pointnet2_ops compilation..."
# Set CUDA compilation environment variables
export CC=/usr/bin/g++
export CXX=/usr/bin/g++
export CUDAHOSTCXX=/usr/bin/g++
export TORCH_CUDA_ARCH_LIST="8.6"
echo "✅ CUDA environment configured"
echo "📦 Installing pointnet2_ops..."
# Navigate to pointnet2_ops directory and install
cd pointnet2_ops && uv pip install --no-build-isolation .
echo "🎉 pointnet2_ops installation completed successfully!"
================================================
FILE: mcp/.python-version
================================================
3.10
================================================
FILE: mcp/README.md
================================================
# GraspGen MCP Server
A [Model Context Protocol](https://modelcontextprotocol.io/) (MCP) server that enables LLMs (Claude, Cursor, etc.) to generate 6-DOF robotic grasp poses using [GraspGen](https://github.com/NVlabs/GraspGen).
```
┌─────────────────────┐ MCP (stdio) ┌─────────────────────┐ ZMQ (tcp) ┌─────────────────────┐
│ LLM / AI Agent │ ◀─── tool calls ───────▶ │ MCP Server │ ── point cloud ───▶ │ GraspGen Server │
│ (Cursor, Claude…) │ │ (this package) │ ◀── grasps ──────── │ (GPU, model loaded)│
└─────────────────────┘ └─────────────────────┘ └─────────────────────┘
```
The MCP server is a lightweight bridge — it requires no CUDA or model weights. It connects to a running GraspGen ZMQ inference server and exposes its capabilities as MCP tools that any LLM agent can call.
## Available Tools
| Tool | Description |
|------|-------------|
| `generate_grasps_from_mesh` | Generate 6-DOF grasp poses from a 3D mesh file (.obj, .stl, .ply, .glb). Samples a point cloud from the mesh surface and runs GraspGen inference. |
| `generate_grasps_from_point_cloud` | Generate 6-DOF grasp poses from a point cloud file (.npy, .npz, .ply, .pcd). |
| `visualize_grasps` | Generate grasps and visualize them interactively in a 3D [viser](https://viser.studio/) web viewer. Accepts a mesh or point cloud file. Grasps are color-coded by confidence (green=high, red=low). |
| `graspgen_health_check` | Check if the GraspGen inference server is running and responsive. |
| `graspgen_server_info` | Get metadata about the server: loaded gripper name, model config, etc. |
## Prerequisites
The GraspGen ZMQ server must be running. See the [client-server/README.md](../client-server/README.md) for setup instructions.
**Quick start (Docker):**
```bash
# From the GraspGen repo root:
MODELS_DIR=/path/to/GraspGenModels docker compose -f docker/compose.serve.yml up --build
```
**Quick start (local):**
```bash
python client-server/graspgen_server.py --gripper_config /path/to/GraspGenModels/checkpoints/graspgen_robotiq_2f_140.yml
```
## Installation
### Using uv (recommended)
```bash
cd GraspGen/mcp
uv venv --python 3.10 .venv && source .venv/bin/activate
uv pip install -e .
```
### Using pip
```bash
cd GraspGen/mcp
pip install -e .
```
## Configuration
### Configure for Cursor
Add the following to `.cursor/mcp.json` in your workspace (or to your global Cursor settings). Make sure to edit the `--directory` entry.
```json
{
"mcpServers": {
"graspgen": {
"command": "uv",
"args": [
"run",
"--directory", "/absolute/path/to/GraspGen/mcp",
"mcp-server-graspgen"
],
"env": {
"GRASPGEN_HOST": "localhost",
"GRASPGEN_PORT": "5556"
}
}
}
}
```
Or if you installed with pip:
```json
{
"mcpServers": {
"graspgen": {
"command": "python",
"args": ["-m", "mcp_server_graspgen"],
"env": {
"GRASPGEN_HOST": "localhost",
"GRASPGEN_PORT": "5556"
}
}
}
}
```
### Configure for Claude Desktop
Add to your Claude Desktop config (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):
```json
{
"mcpServers": {
"graspgen": {
"command": "uv",
"args": [
"run",
"--directory", "/absolute/path/to/GraspGen/mcp",
"mcp-server-graspgen"
],
"env": {
"GRASPGEN_HOST": "localhost",
"GRASPGEN_PORT": "5556"
}
}
}
}
```
### Configure for VS Code
Add to `.vscode/mcp.json` in your workspace:
```json
{
"mcp": {
"servers": {
"graspgen": {
"command": "uv",
"args": [
"run",
"--directory", "/absolute/path/to/GraspGen/mcp",
"mcp-server-graspgen"
],
"env": {
"GRASPGEN_HOST": "localhost",
"GRASPGEN_PORT": "5556"
}
}
}
}
}
```
### Custom Server Address
If the GraspGen ZMQ server is on a different host or port, set the environment variables:
- `GRASPGEN_HOST` — default: `localhost`
- `GRASPGEN_PORT` — default: `5556`
Or pass them as CLI arguments:
```bash
mcp-server-graspgen --host 192.168.1.100 --port 5557
```
## Example LLM Interactions
Once configured, an LLM can naturally call GraspGen:
> **User:** "Generate grasps for the box mesh at `/models/sample_data/meshes/box.obj`"
>
> **LLM → `generate_grasps_from_mesh`:** `{"mesh_file": "/models/sample_data/meshes/box.obj", "mesh_scale": 1.0}`
>
> **Response:** "Generated 100 grasps. Confidence range: 0.7234 – 0.9812. Top grasp at position (0.012, -0.003, 0.045) with confidence 0.9812..."
> **User:** "Is the grasp server running?"
>
> **LLM → `graspgen_health_check`**
>
> **Response:** "GraspGen server status: ok"
## Debugging
Use the MCP inspector to test the server:
```bash
cd GraspGen/mcp
npx @modelcontextprotocol/inspector uv run mcp-server-graspgen
```
================================================
FILE: mcp/pyproject.toml
================================================
[project]
name = "mcp-server-graspgen"
version = "0.1.0"
description = "A Model Context Protocol server for GraspGen 6-DOF robotic grasp generation"
readme = "README.md"
requires-python = ">=3.10"
authors = [{ name = "NVIDIA Corporation" }]
keywords = ["mcp", "robotics", "grasping", "graspgen", "llm"]
license = { text = "NVIDIA Proprietary" }
classifiers = [
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
]
dependencies = [
"mcp>=1.1.3",
"pyzmq>=25.0.0",
"msgpack>=1.0.0",
"msgpack-numpy>=0.4.8",
"numpy>=1.24.0",
"trimesh>=4.0.0",
"pydantic>=2.0.0",
"viser>=1.0.0",
]
[project.scripts]
mcp-server-graspgen = "mcp_server_graspgen:main"
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src/mcp_server_graspgen"]
[tool.uv]
dev-dependencies = ["pyright>=1.1.389", "ruff>=0.7.3"]
================================================
FILE: mcp/src/mcp_server_graspgen/__init__.py
================================================
# Copyright (c) 2025, 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.
from .server import serve
def main():
"""MCP GraspGen Server — 6-DOF grasp generation for LLM tool-calling."""
import argparse
import asyncio
parser = argparse.ArgumentParser(
description="MCP server that bridges LLM tool-calling to a GraspGen ZMQ inference server",
)
parser.add_argument(
"--host",
type=str,
default="localhost",
help="GraspGen ZMQ server host (default: localhost)",
)
parser.add_argument(
"--port",
type=int,
default=5556,
help="GraspGen ZMQ server port (default: 5556)",
)
args = parser.parse_args()
asyncio.run(serve(args.host, args.port))
if __name__ == "__main__":
main()
================================================
FILE: mcp/src/mcp_server_graspgen/__main__.py
================================================
from mcp_server_graspgen import main
main()
================================================
FILE: mcp/src/mcp_server_graspgen/server.py
================================================
# Copyright (c) 2025, 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.
"""MCP server that bridges LLM tool-calling to the GraspGen ZMQ inference server.
Architecture:
LLM (Cursor / Claude) <-- MCP (stdio) --> This server <-- ZMQ (tcp) --> GraspGen GPU server
The GraspGen ZMQ server must be running separately (see client-server/README.md).
This MCP server is lightweight — no CUDA or model weights required.
"""
from __future__ import annotations
import json
import logging
import os
import threading
from typing import Annotated, Optional
import numpy as np
import trimesh
import viser
import viser.transforms as vtf
import zmq
import msgpack
import msgpack_numpy
from mcp.shared.exceptions import McpError
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import (
ErrorData,
TextContent,
Tool,
INVALID_PARAMS,
INTERNAL_ERROR,
)
from pydantic import BaseModel, Field
msgpack_numpy.patch()
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# ZMQ client helpers (inlined to keep this package self-contained)
# ---------------------------------------------------------------------------
_zmq_ctx: zmq.Context | None = None
_zmq_socket: zmq.Socket | None = None
_server_addr: str = ""
def _get_server_addr() -> str:
global _server_addr
if not _server_addr:
host = os.environ.get("GRASPGEN_HOST", "localhost")
port = os.environ.get("GRASPGEN_PORT", "5556")
_server_addr = f"tcp://{host}:{port}"
return _server_addr
def _get_socket(timeout_ms: int = 30_000) -> zmq.Socket:
global _zmq_ctx, _zmq_socket
if _zmq_socket is None:
_zmq_ctx = zmq.Context()
_zmq_socket = _zmq_ctx.socket(zmq.REQ)
_zmq_socket.setsockopt(zmq.RCVTIMEO, timeout_ms)
_zmq_socket.setsockopt(zmq.SNDTIMEO, timeout_ms)
_zmq_socket.setsockopt(zmq.LINGER, 0)
_zmq_socket.connect(_get_server_addr())
return _zmq_socket
def _reset_socket() -> None:
global _zmq_socket, _zmq_ctx
if _zmq_socket is not None:
_zmq_socket.close()
_zmq_socket = None
if _zmq_ctx is not None:
_zmq_ctx.term()
_zmq_ctx = None
def _zmq_request(payload: dict, timeout_ms: int = 60_000) -> dict:
"""Send a msgpack request to the GraspGen ZMQ server and return the response."""
try:
sock = _get_socket(timeout_ms)
sock.send(msgpack.packb(payload, use_bin_type=True))
raw = sock.recv()
response = msgpack.unpackb(raw, raw=False)
if "error" in response:
raise McpError(
ErrorData(code=INTERNAL_ERROR, message=f"GraspGen server error: {response['error']}")
)
return response
except zmq.error.Again:
_reset_socket()
raise McpError(
ErrorData(
code=INTERNAL_ERROR,
message=f"GraspGen server at {_get_server_addr()} timed out. Is it running?",
)
)
except zmq.error.ZMQError as e:
_reset_socket()
raise McpError(
ErrorData(
code=INTERNAL_ERROR,
message=f"ZMQ communication error with GraspGen server: {e}",
)
)
# ---------------------------------------------------------------------------
# Viser visualization state
# ---------------------------------------------------------------------------
_viser_server: Optional[viser.ViserServer] = None
_viser_port: Optional[int] = None
_viser_lock = threading.Lock()
GRIPPER_DIMS: dict[str, tuple[float, float]] = {
"robotiq_2f_140": (0.136, 0.195),
"franka_panda": (0.08, 0.1034),
"single_suction_cup_30mm": (0.03, 0.10),
}
def _gripper_polyline(width: float, depth: float) -> np.ndarray:
"""Return a 7-point polyline representing a parallel-jaw gripper.
The shape traces: right_tip -> right_base -> mid -> origin -> mid -> left_base -> left_tip
with approach direction along +Z.
"""
hw, hd = width / 2, depth / 2
return np.array(
[
[hw, 0, depth],
[hw, 0, hd],
[0, 0, hd],
[0, 0, 0],
[0, 0, hd],
[-hw, 0, hd],
[-hw, 0, depth],
],
dtype=np.float32,
)
def _get_or_create_viser(port: int) -> viser.ViserServer:
global _viser_server, _viser_port
with _viser_lock:
if _viser_server is not None and _viser_port == port:
_viser_server.scene.reset()
return _viser_server
_viser_server = viser.ViserServer(port=port)
_viser_port = port
return _viser_server
def _visualize_grasps_in_viser(
point_cloud: np.ndarray,
grasps: np.ndarray,
confidences: np.ndarray,
gripper_name: str,
port: int,
) -> str:
"""Populate a viser scene with the point cloud and color-coded grasp poses."""
vis = _get_or_create_viser(port)
heights = point_cloud[:, 2]
h_min, h_max = heights.min(), heights.max()
h_range = h_max - h_min if h_max > h_min else 1.0
t = (heights - h_min) / h_range
pc_colors = np.zeros((len(point_cloud), 3), dtype=np.uint8)
pc_colors[:, 0] = (80 * (1 - t)).astype(np.uint8)
pc_colors[:, 1] = (180 * t + 60 * (1 - t)).astype(np.uint8)
pc_colors[:, 2] = (220 * t + 120 * (1 - t)).astype(np.uint8)
vis.scene.add_point_cloud(
"point_cloud",
points=point_cloud.astype(np.float32),
colors=pc_colors,
point_size=0.003,
)
w, d = GRIPPER_DIMS.get(gripper_name, (0.10, 0.15))
ctrl_pts = _gripper_polyline(w, d)
n_pts = len(ctrl_pts)
segments = np.zeros((n_pts - 1, 2, 3), dtype=np.float32)
for j in range(n_pts - 1):
segments[j, 0] = ctrl_pts[j]
segments[j, 1] = ctrl_pts[j + 1]
for i, (grasp, conf) in enumerate(zip(grasps, confidences)):
grasp = grasp.copy()
grasp[3, 3] = 1.0
color = (int((1 - conf) * 255), int(conf * 255), 0)
so3 = vtf.SO3.from_matrix(grasp[:3, :3].astype(np.float64))
wxyz = so3.wxyz
position = grasp[:3, 3].astype(np.float64)
vis.scene.add_line_segments(
f"grasps/{i:03d}",
points=segments,
colors=color,
line_width=0.6,
wxyz=wxyz,
position=position,
)
url = f"http://localhost:{port}"
return url
# ---------------------------------------------------------------------------
# Tool input schemas
# ---------------------------------------------------------------------------
class GenerateGraspsFromMesh(BaseModel):
"""Generate 6-DOF grasp poses for an object given its mesh file."""
mesh_file: Annotated[
str,
Field(description="Absolute path to the mesh file (.obj, .stl, .ply, .glb)"),
]
mesh_scale: Annotated[
float,
Field(default=1.0, description="Scale factor to apply to the mesh before sampling"),
]
num_sample_points: Annotated[
int,
Field(default=2000, description="Number of points to sample from the mesh surface", gt=0),
]
num_grasps: Annotated[
int,
Field(default=200, description="Number of grasps for the diffusion model to sample", gt=0),
]
topk_num_grasps: Annotated[
int,
Field(
default=100,
description="Return only top-k grasps ranked by confidence. -1 to return all.",
),
]
class GenerateGraspsFromPointCloud(BaseModel):
"""Generate 6-DOF grasp poses for an object given a point cloud file."""
point_cloud_file: Annotated[
str,
Field(
description=(
"Absolute path to a point cloud file. "
"Supported formats: .npy (N,3 float32 array), .npz (must contain key 'point_cloud' or 'xyz'), "
".ply / .pcd (loaded via trimesh)."
)
),
]
num_grasps: Annotated[
int,
Field(default=200, description="Number of grasps for the diffusion model to sample", gt=0),
]
topk_num_grasps: Annotated[
int,
Field(
default=100,
description="Return only top-k grasps ranked by confidence. -1 to return all.",
),
]
class VisualizeGrasps(BaseModel):
"""Generate grasps and visualize them in a 3D viser web viewer."""
mesh_file: Annotated[
Optional[str],
Field(default=None, description="Absolute path to a mesh file (.obj, .stl, .ply, .glb)"),
]
point_cloud_file: Annotated[
Optional[str],
Field(
default=None,
description="Absolute path to a point cloud file (.npy, .npz, .ply, .pcd)",
),
]
mesh_scale: Annotated[
float,
Field(default=1.0, description="Scale factor for the mesh (only used with mesh_file)"),
]
num_sample_points: Annotated[
int,
Field(default=2000, description="Points to sample from the mesh surface", gt=0),
]
num_grasps: Annotated[
int,
Field(default=200, description="Number of grasps for the diffusion model to sample", gt=0),
]
topk_num_grasps: Annotated[
int,
Field(default=100, description="Return only top-k grasps ranked by confidence. -1 for all."),
]
viser_port: Annotated[
int,
Field(default=8080, description="Port for the viser 3D visualization server"),
]
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _read_pcd_ascii(path: str) -> np.ndarray:
"""Minimal ASCII PCD reader (FIELDS x y z)."""
points = []
in_data = False
with open(path, "r") as f:
for line in f:
if in_data:
vals = line.strip().split()
if len(vals) >= 3:
points.append([float(vals[0]), float(vals[1]), float(vals[2])])
elif line.strip().startswith("DATA"):
in_data = True
if not points:
raise McpError(ErrorData(code=INVALID_PARAMS, message=f"No points found in PCD file: {path}"))
return np.array(points, dtype=np.float32)
def _load_point_cloud_from_file(path: str) -> np.ndarray:
"""Load a point cloud from various file formats, returning (N, 3) float32."""
if not os.path.isfile(path):
raise McpError(ErrorData(code=INVALID_PARAMS, message=f"File not found: {path}"))
ext = os.path.splitext(path)[1].lower()
if ext == ".npy":
pc = np.load(path).astype(np.float32)
elif ext == ".npz":
data = np.load(path)
for key in ("point_cloud", "xyz", "points", "pc"):
if key in data:
pc = data[key].astype(np.float32)
break
else:
keys = list(data.keys())
if len(keys) == 1:
pc = data[keys[0]].astype(np.float32)
else:
raise McpError(
ErrorData(
code=INVALID_PARAMS,
message=f"NPZ file has keys {keys}; expected one of: point_cloud, xyz, points, pc",
)
)
elif ext == ".pcd":
pc = _read_pcd_ascii(path)
elif ext in (".ply", ".obj", ".stl", ".glb", ".gltf"):
mesh_or_pc = trimesh.load(path)
if hasattr(mesh_or_pc, "vertices"):
pc = np.array(mesh_or_pc.vertices, dtype=np.float32)
else:
raise McpError(
ErrorData(code=INVALID_PARAMS, message=f"Could not extract points from {path}")
)
else:
raise McpError(
ErrorData(code=INVALID_PARAMS, message=f"Unsupported point cloud format: {ext}")
)
if pc.ndim != 2 or pc.shape[1] != 3:
raise McpError(
ErrorData(code=INVALID_PARAMS, message=f"Point cloud must be (N, 3), got {pc.shape}")
)
return pc
def _load_and_sample_mesh(mesh_file: str, scale: float, num_points: int) -> np.ndarray:
"""Load a mesh, scale it, sample surface points, and center them."""
if not os.path.isfile(mesh_file):
raise McpError(ErrorData(code=INVALID_PARAMS, message=f"Mesh file not found: {mesh_file}"))
try:
mesh = trimesh.load(mesh_file)
except Exception as e:
raise McpError(ErrorData(code=INVALID_PARAMS, message=f"Failed to load mesh: {e}"))
mesh.apply_scale(scale)
xyz, _ = trimesh.sample.sample_surface(mesh, num_points)
xyz = np.array(xyz, dtype=np.float32)
xyz -= xyz.mean(axis=0)
return xyz
def _format_grasp_results(grasps: np.ndarray, confidences: np.ndarray, timing: dict) -> str:
"""Format grasp inference results into human-readable text for the LLM."""
n = len(grasps)
if n == 0:
return "No grasps were generated. The point cloud may be too sparse or the object too small."
lines = [
f"Generated {n} grasp(s).",
f"Confidence range: {confidences.min():.4f} – {confidences.max():.4f}",
f"Inference time: {timing.get('infer_ms', 0):.0f} ms",
"",
"Top grasps (sorted by confidence, highest first):",
]
show_n = min(n, 5)
for i in range(show_n):
pos = grasps[i][:3, 3]
conf = confidences[i]
lines.append(
f" #{i+1}: confidence={conf:.4f}, "
f"position=({pos[0]:.4f}, {pos[1]:.4f}, {pos[2]:.4f})"
)
if n > show_n:
lines.append(f" ... and {n - show_n} more grasps")
lines.append("")
lines.append("Each grasp is a 4x4 homogeneous transformation matrix (SE(3) pose).")
lines.append(
"The full grasp data (poses + confidences) can be used downstream "
"for motion planning with tools like cuRobo."
)
return "\n".join(lines)
# ---------------------------------------------------------------------------
# MCP server
# ---------------------------------------------------------------------------
async def serve(host: str = "localhost", port: int = 5556) -> None:
"""Run the GraspGen MCP server over stdio."""
global _server_addr
_server_addr = f"tcp://{host}:{port}"
server = Server("mcp-graspgen")
@server.list_tools()
async def list_tools() -> list[Tool]:
return [
Tool(
name="generate_grasps_from_mesh",
description=(
"Generate 6-DOF robotic grasp poses for an object given its 3D mesh file. "
"The mesh is sampled into a point cloud and sent to the GraspGen diffusion model. "
"Returns ranked grasp poses (SE(3) matrices) with confidence scores. "
"Supported mesh formats: .obj, .stl, .ply, .glb."
),
inputSchema=GenerateGraspsFromMesh.model_json_schema(),
),
Tool(
name="generate_grasps_from_point_cloud",
description=(
"Generate 6-DOF robotic grasp poses for an object given a point cloud file. "
"The point cloud is sent directly to the GraspGen diffusion model. "
"Returns ranked grasp poses (SE(3) matrices) with confidence scores. "
"Supported formats: .npy, .npz, .ply, .pcd."
),
inputSchema=GenerateGraspsFromPointCloud.model_json_schema(),
),
Tool(
name="graspgen_health_check",
description=(
"Check if the GraspGen inference server is running and responsive. "
"Returns the server status."
),
inputSchema={"type": "object", "properties": {}, "required": []},
),
Tool(
name="graspgen_server_info",
description=(
"Get metadata about the running GraspGen server, including the loaded "
"gripper name (e.g. franka_panda, robotiq_2f_140, single_suction_cup_30mm) "
"and model configuration."
),
inputSchema={"type": "object", "properties": {}, "required": []},
),
Tool(
name="visualize_grasps",
description=(
"Generate 6-DOF grasp poses and visualize them interactively in a 3D viser "
"web viewer. Accepts a mesh file OR a point cloud file. The point cloud and "
"gripper poses are rendered in a browser at the returned URL. "
"Grasps are color-coded by confidence (green=high, red=low)."
),
inputSchema=VisualizeGrasps.model_json_schema(),
),
]
@server.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
if name == "generate_grasps_from_mesh":
return await _handle_generate_grasps_from_mesh(arguments)
elif name == "generate_grasps_from_point_cloud":
return await _handle_generate_grasps_from_point_cloud(arguments)
elif name == "graspgen_health_check":
return _handle_health_check()
elif name == "graspgen_server_info":
return _handle_server_info()
elif name == "visualize_grasps":
return await _handle_visualize_grasps(arguments)
else:
raise McpError(ErrorData(code=INVALID_PARAMS, message=f"Unknown tool: {name}"))
async def _handle_generate_grasps_from_mesh(arguments: dict) -> list[TextContent]:
try:
args = GenerateGraspsFromMesh(**arguments)
except ValueError as e:
raise McpError(ErrorData(code=INVALID_PARAMS, message=str(e)))
logger.info("Loading mesh: %s (scale=%.2f)", args.mesh_file, args.mesh_scale)
point_cloud = _load_and_sample_mesh(args.mesh_file, args.mesh_scale, args.num_sample_points)
response = _zmq_request({
"action": "infer",
"point_cloud": point_cloud,
"num_grasps": args.num_grasps,
"topk_num_grasps": args.topk_num_grasps,
})
grasps = np.asarray(response["grasps"], dtype=np.float32)
confidences = np.asarray(response["confidences"], dtype=np.float32)
timing = response.get("timing", {})
text = (
f"Mesh: {args.mesh_file} (scale={args.mesh_scale}, "
f"sampled {args.num_sample_points} points)\n\n"
+ _format_grasp_results(grasps, confidences, timing)
)
return [TextContent(type="text", text=text)]
async def _handle_generate_grasps_from_point_cloud(arguments: dict) -> list[TextContent]:
try:
args = GenerateGraspsFromPointCloud(**arguments)
except ValueError as e:
raise McpError(ErrorData(code=INVALID_PARAMS, message=str(e)))
logger.info("Loading point cloud: %s", args.point_cloud_file)
point_cloud = _load_point_cloud_from_file(args.point_cloud_file)
response = _zmq_request({
"action": "infer",
"point_cloud": point_cloud,
"num_grasps": args.num_grasps,
"topk_num_grasps": args.topk_num_grasps,
})
grasps = np.asarray(response["grasps"], dtype=np.float32)
confidences = np.asarray(response["confidences"], dtype=np.float32)
timing = response.get("timing", {})
text = (
f"Point cloud: {args.point_cloud_file} ({len(point_cloud)} points)\n\n"
+ _format_grasp_results(grasps, confidences, timing)
)
return [TextContent(type="text", text=text)]
async def _handle_visualize_grasps(arguments: dict) -> list[TextContent]:
try:
args = VisualizeGrasps(**arguments)
except ValueError as e:
raise McpError(ErrorData(code=INVALID_PARAMS, message=str(e)))
if args.mesh_file and args.point_cloud_file:
raise McpError(
ErrorData(code=INVALID_PARAMS, message="Provide mesh_file OR point_cloud_file, not both.")
)
if not args.mesh_file and not args.point_cloud_file:
raise McpError(
ErrorData(code=INVALID_PARAMS, message="Provide either mesh_file or point_cloud_file.")
)
if args.mesh_file:
logger.info("Visualize: loading mesh %s (scale=%.2f)", args.mesh_file, args.mesh_scale)
point_cloud = _load_and_sample_mesh(args.mesh_file, args.mesh_scale, args.num_sample_points)
input_desc = f"Mesh: {args.mesh_file} (scale={args.mesh_scale})"
else:
logger.info("Visualize: loading point cloud %s", args.point_cloud_file)
point_cloud = _load_point_cloud_from_file(args.point_cloud_file)
input_desc = f"Point cloud: {args.point_cloud_file}"
response = _zmq_request({
"action": "infer",
"point_cloud": point_cloud,
"num_grasps": args.num_grasps,
"topk_num_grasps": args.topk_num_grasps,
})
grasps = np.asarray(response["grasps"], dtype=np.float32)
confidences = np.asarray(response["confidences"], dtype=np.float32)
n = len(grasps)
if n == 0:
return [TextContent(type="text", text="No grasps generated — nothing to visualize.")]
metadata = _zmq_request({"action": "metadata"}, timeout_ms=5_000)
gripper_name = metadata.get("gripper_name", "robotiq_2f_140")
url = _visualize_grasps_in_viser(
point_cloud, grasps, confidences, gripper_name, args.viser_port,
)
text = (
f"{input_desc}\n"
f"Generated {n} grasp(s), confidence range: {confidences.min():.4f} – {confidences.max():.4f}\n\n"
f"Viser 3D visualization is running at: {url}\n"
f"Open this URL in a browser to interactively view the point cloud and grasps.\n"
f"Grasps are color-coded: green = high confidence, red = low confidence."
)
return [TextContent(type="text", text=text)]
def _handle_health_check() -> list[TextContent]:
try:
response = _zmq_request({"action": "health"}, timeout_ms=5_000)
status = response.get("status", "unknown")
return [TextContent(type="text", text=f"GraspGen server status: {status}")]
except McpError:
return [
TextContent(
type="text",
text=(
f"GraspGen server at {_get_server_addr()} is not reachable. "
"Make sure it is running (see GraspGen client-server/README.md)."
),
)
]
def _handle_server_info() -> list[TextContent]:
response = _zmq_request({"action": "metadata"}, timeout_ms=5_000)
return [TextContent(type="text", text=json.dumps(response, indent=2))]
options = server.create_initialization_options()
async with stdio_server() as (read_stream, write_stream):
await server.run(read_stream, write_stream, options, raise_exceptions=True)
================================================
FILE: pointnet2_ops/MANIFEST.in
================================================
graft pointnet2_ops/_ext-src
================================================
FILE: pointnet2_ops/pointnet2_ops/__init__.py
================================================
# Copyright (c) 2025, 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 pointnet2_ops.pointnet2_modules
import pointnet2_ops.pointnet2_utils
from pointnet2_ops._version import __version__
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/include/ball_query.h
================================================
#pragma once
#include
at::Tensor ball_query(at::Tensor new_xyz, at::Tensor xyz, const float radius,
const int nsample);
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/include/cuda_utils.h
================================================
#ifndef _CUDA_UTILS_H
#define _CUDA_UTILS_H
#include
#include
#include
#include
#include
#include
#define TOTAL_THREADS 512
inline int opt_n_threads(int work_size) {
const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0);
return max(min(1 << pow_2, TOTAL_THREADS), 1);
}
inline dim3 opt_block_config(int x, int y) {
const int x_threads = opt_n_threads(x);
const int y_threads =
max(min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1);
dim3 block_config(x_threads, y_threads, 1);
return block_config;
}
#define CUDA_CHECK_ERRORS() \
do { \
cudaError_t err = cudaGetLastError(); \
if (cudaSuccess != err) { \
fprintf(stderr, "CUDA kernel failed : %s\n%s at L:%d in %s\n", \
cudaGetErrorString(err), __PRETTY_FUNCTION__, __LINE__, \
__FILE__); \
exit(-1); \
} \
} while (0)
#endif
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/include/group_points.h
================================================
#pragma once
#include
at::Tensor group_points(at::Tensor points, at::Tensor idx);
at::Tensor group_points_grad(at::Tensor grad_out, at::Tensor idx, const int n);
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/include/interpolate.h
================================================
#pragma once
#include
#include
std::vector three_nn(at::Tensor unknowns, at::Tensor knows);
at::Tensor three_interpolate(at::Tensor points, at::Tensor idx,
at::Tensor weight);
at::Tensor three_interpolate_grad(at::Tensor grad_out, at::Tensor idx,
at::Tensor weight, const int m);
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/include/sampling.h
================================================
#pragma once
#include
at::Tensor gather_points(at::Tensor points, at::Tensor idx);
at::Tensor gather_points_grad(at::Tensor grad_out, at::Tensor idx, const int n);
at::Tensor furthest_point_sampling(at::Tensor points, const int nsamples);
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/include/utils.h
================================================
#pragma once
#include
#include
#define CHECK_CUDA(x) \
do { \
AT_ASSERT(x.is_cuda(), #x " must be a CUDA tensor"); \
} while (0)
#define CHECK_CONTIGUOUS(x) \
do { \
AT_ASSERT(x.is_contiguous(), #x " must be a contiguous tensor"); \
} while (0)
#define CHECK_IS_INT(x) \
do { \
AT_ASSERT(x.scalar_type() == at::ScalarType::Int, \
#x " must be an int tensor"); \
} while (0)
#define CHECK_IS_FLOAT(x) \
do { \
AT_ASSERT(x.scalar_type() == at::ScalarType::Float, \
#x " must be a float tensor"); \
} while (0)
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/src/ball_query.cpp
================================================
#include "ball_query.h"
#include "utils.h"
#include
#include
#include
#include
#include
void query_ball_point_kernel_wrapper(int b, int n, int m, float radius,
int nsample, const float *new_xyz,
const float *xyz, int *idx);
at::Tensor ball_query(at::Tensor new_xyz, at::Tensor xyz, const float radius,
const int nsample) {
CHECK_CONTIGUOUS(new_xyz);
CHECK_CONTIGUOUS(xyz);
CHECK_IS_FLOAT(new_xyz);
CHECK_IS_FLOAT(xyz);
if (new_xyz.is_cuda()) {
CHECK_CUDA(xyz);
}
const at::cuda::OptionalCUDAGuard device_guard(device_of(xyz));
at::Tensor idx =
torch::zeros({new_xyz.size(0), new_xyz.size(1), nsample},
at::device(new_xyz.device()).dtype(at::ScalarType::Int));
if (new_xyz.is_cuda()) {
query_ball_point_kernel_wrapper(xyz.size(0), xyz.size(1), new_xyz.size(1),
radius, nsample, new_xyz.data_ptr(),
xyz.data_ptr(), idx.data_ptr());
} else {
AT_ASSERT(false, "CPU not supported");
}
return idx;
}
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/src/ball_query_gpu.cu
================================================
#include
#include
#include
#include "cuda_utils.h"
// input: new_xyz(b, m, 3) xyz(b, n, 3)
// output: idx(b, m, nsample)
__global__ void query_ball_point_kernel(int b, int n, int m, float radius,
int nsample,
const float *__restrict__ new_xyz,
const float *__restrict__ xyz,
int *__restrict__ idx) {
int batch_index = blockIdx.x;
xyz += batch_index * n * 3;
new_xyz += batch_index * m * 3;
idx += m * nsample * batch_index;
int index = threadIdx.x;
int stride = blockDim.x;
float radius2 = radius * radius;
for (int j = index; j < m; j += stride) {
float new_x = new_xyz[j * 3 + 0];
float new_y = new_xyz[j * 3 + 1];
float new_z = new_xyz[j * 3 + 2];
for (int k = 0, cnt = 0; k < n && cnt < nsample; ++k) {
float x = xyz[k * 3 + 0];
float y = xyz[k * 3 + 1];
float z = xyz[k * 3 + 2];
float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) +
(new_z - z) * (new_z - z);
if (d2 < radius2) {
if (cnt == 0) {
for (int l = 0; l < nsample; ++l) {
idx[j * nsample + l] = k;
}
}
idx[j * nsample + cnt] = k;
++cnt;
}
}
}
}
void query_ball_point_kernel_wrapper(int b, int n, int m, float radius,
int nsample, const float *new_xyz,
const float *xyz, int *idx) {
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
query_ball_point_kernel<<>>(
b, n, m, radius, nsample, new_xyz, xyz, idx);
CUDA_CHECK_ERRORS();
}
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/src/bindings.cpp
================================================
#include "ball_query.h"
#include "group_points.h"
#include "interpolate.h"
#include "sampling.h"
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("gather_points", &gather_points);
m.def("gather_points_grad", &gather_points_grad);
m.def("furthest_point_sampling", &furthest_point_sampling);
m.def("three_nn", &three_nn);
m.def("three_interpolate", &three_interpolate);
m.def("three_interpolate_grad", &three_interpolate_grad);
m.def("ball_query", &ball_query);
m.def("group_points", &group_points);
m.def("group_points_grad", &group_points_grad);
}
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/src/group_points.cpp
================================================
#include "group_points.h"
#include "utils.h"
#include
#include
#include
#include
#include
void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample,
const float *points, const int *idx,
float *out);
void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints,
int nsample, const float *grad_out,
const int *idx, float *grad_points);
at::Tensor group_points(at::Tensor points, at::Tensor idx) {
CHECK_CONTIGUOUS(points);
CHECK_CONTIGUOUS(idx);
CHECK_IS_FLOAT(points);
CHECK_IS_INT(idx);
if (points.is_cuda()) {
CHECK_CUDA(idx);
}
const at::cuda::OptionalCUDAGuard device_guard(device_of(points));
at::Tensor output =
torch::zeros({points.size(0), points.size(1), idx.size(1), idx.size(2)},
at::device(points.device()).dtype(at::ScalarType::Float));
if (points.is_cuda()) {
group_points_kernel_wrapper(points.size(0), points.size(1), points.size(2),
idx.size(1), idx.size(2),
points.data_ptr(), idx.data_ptr(),
output.data_ptr());
} else {
AT_ASSERT(false, "CPU not supported");
}
return output;
}
at::Tensor group_points_grad(at::Tensor grad_out, at::Tensor idx, const int n) {
CHECK_CONTIGUOUS(grad_out);
CHECK_CONTIGUOUS(idx);
CHECK_IS_FLOAT(grad_out);
CHECK_IS_INT(idx);
if (grad_out.is_cuda()) {
CHECK_CUDA(idx);
}
const at::cuda::OptionalCUDAGuard device_guard(device_of(grad_out));
at::Tensor output =
torch::zeros({grad_out.size(0), grad_out.size(1), n},
at::device(grad_out.device()).dtype(at::ScalarType::Float));
if (grad_out.is_cuda()) {
group_points_grad_kernel_wrapper(
grad_out.size(0), grad_out.size(1), n, idx.size(1), idx.size(2),
grad_out.data_ptr(), idx.data_ptr(),
output.data_ptr());
} else {
AT_ASSERT(false, "CPU not supported");
}
return output;
}
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/src/group_points_gpu.cu
================================================
#include
#include
#include "cuda_utils.h"
// input: points(b, c, n) idx(b, npoints, nsample)
// output: out(b, c, npoints, nsample)
__global__ void group_points_kernel(int b, int c, int n, int npoints,
int nsample,
const float *__restrict__ points,
const int *__restrict__ idx,
float *__restrict__ out) {
int batch_index = blockIdx.x;
points += batch_index * n * c;
idx += batch_index * npoints * nsample;
out += batch_index * npoints * nsample * c;
const int index = threadIdx.y * blockDim.x + threadIdx.x;
const int stride = blockDim.y * blockDim.x;
for (int i = index; i < c * npoints; i += stride) {
const int l = i / npoints;
const int j = i % npoints;
for (int k = 0; k < nsample; ++k) {
int ii = idx[j * nsample + k];
out[(l * npoints + j) * nsample + k] = points[l * n + ii];
}
}
}
void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample,
const float *points, const int *idx,
float *out) {
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
group_points_kernel<<>>(
b, c, n, npoints, nsample, points, idx, out);
CUDA_CHECK_ERRORS();
}
// input: grad_out(b, c, npoints, nsample), idx(b, npoints, nsample)
// output: grad_points(b, c, n)
__global__ void group_points_grad_kernel(int b, int c, int n, int npoints,
int nsample,
const float *__restrict__ grad_out,
const int *__restrict__ idx,
float *__restrict__ grad_points) {
int batch_index = blockIdx.x;
grad_out += batch_index * npoints * nsample * c;
idx += batch_index * npoints * nsample;
grad_points += batch_index * n * c;
const int index = threadIdx.y * blockDim.x + threadIdx.x;
const int stride = blockDim.y * blockDim.x;
for (int i = index; i < c * npoints; i += stride) {
const int l = i / npoints;
const int j = i % npoints;
for (int k = 0; k < nsample; ++k) {
int ii = idx[j * nsample + k];
atomicAdd(grad_points + l * n + ii,
grad_out[(l * npoints + j) * nsample + k]);
}
}
}
void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints,
int nsample, const float *grad_out,
const int *idx, float *grad_points) {
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
group_points_grad_kernel<<>>(
b, c, n, npoints, nsample, grad_out, idx, grad_points);
CUDA_CHECK_ERRORS();
}
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/src/interpolate.cpp
================================================
#include "interpolate.h"
#include "utils.h"
#include
#include
#include
#include
#include
void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown,
const float *known, float *dist2, int *idx);
void three_interpolate_kernel_wrapper(int b, int c, int m, int n,
const float *points, const int *idx,
const float *weight, float *out);
void three_interpolate_grad_kernel_wrapper(int b, int c, int n, int m,
const float *grad_out,
const int *idx, const float *weight,
float *grad_points);
std::vector three_nn(at::Tensor unknowns, at::Tensor knows) {
CHECK_CONTIGUOUS(unknowns);
CHECK_CONTIGUOUS(knows);
CHECK_IS_FLOAT(unknowns);
CHECK_IS_FLOAT(knows);
if (unknowns.is_cuda()) {
CHECK_CUDA(knows);
}
const at::cuda::OptionalCUDAGuard device_guard(device_of(unknowns));
at::Tensor idx =
torch::zeros({unknowns.size(0), unknowns.size(1), 3},
at::device(unknowns.device()).dtype(at::ScalarType::Int));
at::Tensor dist2 =
torch::zeros({unknowns.size(0), unknowns.size(1), 3},
at::device(unknowns.device()).dtype(at::ScalarType::Float));
if (unknowns.is_cuda()) {
three_nn_kernel_wrapper(unknowns.size(0), unknowns.size(1), knows.size(1),
unknowns.data_ptr(), knows.data_ptr(),
dist2.data_ptr(), idx.data_ptr());
} else {
AT_ASSERT(false, "CPU not supported");
}
return {dist2, idx};
}
at::Tensor three_interpolate(at::Tensor points, at::Tensor idx,
at::Tensor weight) {
CHECK_CONTIGUOUS(points);
CHECK_CONTIGUOUS(idx);
CHECK_CONTIGUOUS(weight);
CHECK_IS_FLOAT(points);
CHECK_IS_INT(idx);
CHECK_IS_FLOAT(weight);
if (points.is_cuda()) {
CHECK_CUDA(idx);
CHECK_CUDA(weight);
}
const at::cuda::OptionalCUDAGuard device_guard(device_of(points));
at::Tensor output =
torch::zeros({points.size(0), points.size(1), idx.size(1)},
at::device(points.device()).dtype(at::ScalarType::Float));
if (points.is_cuda()) {
three_interpolate_kernel_wrapper(
points.size(0), points.size(1), points.size(2), idx.size(1),
points.data_ptr(), idx.data_ptr(), weight.data_ptr(),
output.data_ptr());
} else {
AT_ASSERT(false, "CPU not supported");
}
return output;
}
at::Tensor three_interpolate_grad(at::Tensor grad_out, at::Tensor idx,
at::Tensor weight, const int m) {
CHECK_CONTIGUOUS(grad_out);
CHECK_CONTIGUOUS(idx);
CHECK_CONTIGUOUS(weight);
CHECK_IS_FLOAT(grad_out);
CHECK_IS_INT(idx);
CHECK_IS_FLOAT(weight);
if (grad_out.is_cuda()) {
CHECK_CUDA(idx);
CHECK_CUDA(weight);
}
const at::cuda::OptionalCUDAGuard device_guard(device_of(grad_out));
at::Tensor output =
torch::zeros({grad_out.size(0), grad_out.size(1), m},
at::device(grad_out.device()).dtype(at::ScalarType::Float));
if (grad_out.is_cuda()) {
three_interpolate_grad_kernel_wrapper(
grad_out.size(0), grad_out.size(1), grad_out.size(2), m,
grad_out.data_ptr(), idx.data_ptr(),
weight.data_ptr(), output.data_ptr());
} else {
AT_ASSERT(false, "CPU not supported");
}
return output;
}
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/src/interpolate_gpu.cu
================================================
#include
#include
#include
#include "cuda_utils.h"
// input: unknown(b, n, 3) known(b, m, 3)
// output: dist2(b, n, 3), idx(b, n, 3)
__global__ void three_nn_kernel(int b, int n, int m,
const float *__restrict__ unknown,
const float *__restrict__ known,
float *__restrict__ dist2,
int *__restrict__ idx) {
int batch_index = blockIdx.x;
unknown += batch_index * n * 3;
known += batch_index * m * 3;
dist2 += batch_index * n * 3;
idx += batch_index * n * 3;
int index = threadIdx.x;
int stride = blockDim.x;
for (int j = index; j < n; j += stride) {
float ux = unknown[j * 3 + 0];
float uy = unknown[j * 3 + 1];
float uz = unknown[j * 3 + 2];
double best1 = 1e40, best2 = 1e40, best3 = 1e40;
int besti1 = 0, besti2 = 0, besti3 = 0;
for (int k = 0; k < m; ++k) {
float x = known[k * 3 + 0];
float y = known[k * 3 + 1];
float z = known[k * 3 + 2];
float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);
if (d < best1) {
best3 = best2;
besti3 = besti2;
best2 = best1;
besti2 = besti1;
best1 = d;
besti1 = k;
} else if (d < best2) {
best3 = best2;
besti3 = besti2;
best2 = d;
besti2 = k;
} else if (d < best3) {
best3 = d;
besti3 = k;
}
}
dist2[j * 3 + 0] = best1;
dist2[j * 3 + 1] = best2;
dist2[j * 3 + 2] = best3;
idx[j * 3 + 0] = besti1;
idx[j * 3 + 1] = besti2;
idx[j * 3 + 2] = besti3;
}
}
void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown,
const float *known, float *dist2, int *idx) {
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
three_nn_kernel<<>>(b, n, m, unknown, known,
dist2, idx);
CUDA_CHECK_ERRORS();
}
// input: points(b, c, m), idx(b, n, 3), weight(b, n, 3)
// output: out(b, c, n)
__global__ void three_interpolate_kernel(int b, int c, int m, int n,
const float *__restrict__ points,
const int *__restrict__ idx,
const float *__restrict__ weight,
float *__restrict__ out) {
int batch_index = blockIdx.x;
points += batch_index * m * c;
idx += batch_index * n * 3;
weight += batch_index * n * 3;
out += batch_index * n * c;
const int index = threadIdx.y * blockDim.x + threadIdx.x;
const int stride = blockDim.y * blockDim.x;
for (int i = index; i < c * n; i += stride) {
const int l = i / n;
const int j = i % n;
float w1 = weight[j * 3 + 0];
float w2 = weight[j * 3 + 1];
float w3 = weight[j * 3 + 2];
int i1 = idx[j * 3 + 0];
int i2 = idx[j * 3 + 1];
int i3 = idx[j * 3 + 2];
out[i] = points[l * m + i1] * w1 + points[l * m + i2] * w2 +
points[l * m + i3] * w3;
}
}
void three_interpolate_kernel_wrapper(int b, int c, int m, int n,
const float *points, const int *idx,
const float *weight, float *out) {
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
three_interpolate_kernel<<>>(
b, c, m, n, points, idx, weight, out);
CUDA_CHECK_ERRORS();
}
// input: grad_out(b, c, n), idx(b, n, 3), weight(b, n, 3)
// output: grad_points(b, c, m)
__global__ void three_interpolate_grad_kernel(
int b, int c, int n, int m, const float *__restrict__ grad_out,
const int *__restrict__ idx, const float *__restrict__ weight,
float *__restrict__ grad_points) {
int batch_index = blockIdx.x;
grad_out += batch_index * n * c;
idx += batch_index * n * 3;
weight += batch_index * n * 3;
grad_points += batch_index * m * c;
const int index = threadIdx.y * blockDim.x + threadIdx.x;
const int stride = blockDim.y * blockDim.x;
for (int i = index; i < c * n; i += stride) {
const int l = i / n;
const int j = i % n;
float w1 = weight[j * 3 + 0];
float w2 = weight[j * 3 + 1];
float w3 = weight[j * 3 + 2];
int i1 = idx[j * 3 + 0];
int i2 = idx[j * 3 + 1];
int i3 = idx[j * 3 + 2];
atomicAdd(grad_points + l * m + i1, grad_out[i] * w1);
atomicAdd(grad_points + l * m + i2, grad_out[i] * w2);
atomicAdd(grad_points + l * m + i3, grad_out[i] * w3);
}
}
void three_interpolate_grad_kernel_wrapper(int b, int c, int n, int m,
const float *grad_out,
const int *idx, const float *weight,
float *grad_points) {
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
three_interpolate_grad_kernel<<>>(
b, c, n, m, grad_out, idx, weight, grad_points);
CUDA_CHECK_ERRORS();
}
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/src/sampling.cpp
================================================
#include "sampling.h"
#include "utils.h"
#include
#include
#include
#include
#include
void gather_points_kernel_wrapper(int b, int c, int n, int npoints,
const float *points, const int *idx,
float *out);
void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints,
const float *grad_out, const int *idx,
float *grad_points);
void furthest_point_sampling_kernel_wrapper(int b, int n, int m,
const float *dataset, float *temp,
int *idxs);
at::Tensor gather_points(at::Tensor points, at::Tensor idx) {
CHECK_CONTIGUOUS(points);
CHECK_CONTIGUOUS(idx);
CHECK_IS_FLOAT(points);
CHECK_IS_INT(idx);
if (points.is_cuda()) {
CHECK_CUDA(idx);
}
const at::cuda::OptionalCUDAGuard device_guard(device_of(points));
at::Tensor output =
torch::zeros({points.size(0), points.size(1), idx.size(1)},
at::device(points.device()).dtype(at::ScalarType::Float));
if (points.is_cuda()) {
gather_points_kernel_wrapper(points.size(0), points.size(1), points.size(2),
idx.size(1), points.data_ptr(),
idx.data_ptr(), output.data_ptr());
} else {
AT_ASSERT(false, "CPU not supported");
}
return output;
}
at::Tensor gather_points_grad(at::Tensor grad_out, at::Tensor idx,
const int n) {
CHECK_CONTIGUOUS(grad_out);
CHECK_CONTIGUOUS(idx);
CHECK_IS_FLOAT(grad_out);
CHECK_IS_INT(idx);
if (grad_out.is_cuda()) {
CHECK_CUDA(idx);
}
const at::cuda::OptionalCUDAGuard device_guard(device_of(grad_out));
at::Tensor output =
torch::zeros({grad_out.size(0), grad_out.size(1), n},
at::device(grad_out.device()).dtype(at::ScalarType::Float));
if (grad_out.is_cuda()) {
gather_points_grad_kernel_wrapper(grad_out.size(0), grad_out.size(1), n,
idx.size(1), grad_out.data_ptr(),
idx.data_ptr(),
output.data_ptr());
} else {
AT_ASSERT(false, "CPU not supported");
}
return output;
}
at::Tensor furthest_point_sampling(at::Tensor points, const int nsamples) {
CHECK_CONTIGUOUS(points);
CHECK_IS_FLOAT(points);
const at::cuda::OptionalCUDAGuard device_guard(device_of(points));
at::Tensor output =
torch::zeros({points.size(0), nsamples},
at::device(points.device()).dtype(at::ScalarType::Int));
at::Tensor tmp =
torch::full({points.size(0), points.size(1)}, 1e10,
at::device(points.device()).dtype(at::ScalarType::Float));
if (points.is_cuda()) {
furthest_point_sampling_kernel_wrapper(
points.size(0), points.size(1), nsamples, points.data_ptr(),
tmp.data_ptr(), output.data_ptr());
} else {
AT_ASSERT(false, "CPU not supported");
}
return output;
}
================================================
FILE: pointnet2_ops/pointnet2_ops/_ext-src/src/sampling_gpu.cu
================================================
#include
#include
#include "cuda_utils.h"
// input: points(b, c, n) idx(b, m)
// output: out(b, c, m)
__global__ void gather_points_kernel(int b, int c, int n, int m,
const float *__restrict__ points,
const int *__restrict__ idx,
float *__restrict__ out) {
for (int i = blockIdx.x; i < b; i += gridDim.x) {
for (int l = blockIdx.y; l < c; l += gridDim.y) {
for (int j = threadIdx.x; j < m; j += blockDim.x) {
int a = idx[i * m + j];
out[(i * c + l) * m + j] = points[(i * c + l) * n + a];
}
}
}
}
void gather_points_kernel_wrapper(int b, int c, int n, int npoints,
const float *points, const int *idx,
float *out) {
gather_points_kernel<<>>(b, c, n, npoints,
points, idx, out);
CUDA_CHECK_ERRORS();
}
// input: grad_out(b, c, m) idx(b, m)
// output: grad_points(b, c, n)
__global__ void gather_points_grad_kernel(int b, int c, int n, int m,
const float *__restrict__ grad_out,
const int *__restrict__ idx,
float *__restrict__ grad_points) {
for (int i = blockIdx.x; i < b; i += gridDim.x) {
for (int l = blockIdx.y; l < c; l += gridDim.y) {
for (int j = threadIdx.x; j < m; j += blockDim.x) {
int a = idx[i * m + j];
atomicAdd(grad_points + (i * c + l) * n + a,
grad_out[(i * c + l) * m + j]);
}
}
}
}
void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints,
const float *grad_out, const int *idx,
float *grad_points) {
gather_points_grad_kernel<<>>(
b, c, n, npoints, grad_out, idx, grad_points);
CUDA_CHECK_ERRORS();
}
__device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i,
int idx1, int idx2) {
const float v1 = dists[idx1], v2 = dists[idx2];
const int i1 = dists_i[idx1], i2 = dists_i[idx2];
dists[idx1] = max(v1, v2);
dists_i[idx1] = v2 > v1 ? i2 : i1;
}
// Input dataset: (b, n, 3), tmp: (b, n)
// Ouput idxs (b, m)
template
__global__ void furthest_point_sampling_kernel(
int b, int n, int m, const float *__restrict__ dataset,
float *__restrict__ temp, int *__restrict__ idxs) {
if (m <= 0) return;
__shared__ float dists[block_size];
__shared__ int dists_i[block_size];
int batch_index = blockIdx.x;
dataset += batch_index * n * 3;
temp += batch_index * n;
idxs += batch_index * m;
int tid = threadIdx.x;
const int stride = block_size;
int old = 0;
if (threadIdx.x == 0) idxs[0] = old;
__syncthreads();
for (int j = 1; j < m; j++) {
int besti = 0;
float best = -1;
float x1 = dataset[old * 3 + 0];
float y1 = dataset[old * 3 + 1];
float z1 = dataset[old * 3 + 2];
for (int k = tid; k < n; k += stride) {
float x2, y2, z2;
x2 = dataset[k * 3 + 0];
y2 = dataset[k * 3 + 1];
z2 = dataset[k * 3 + 2];
float mag = (x2 * x2) + (y2 * y2) + (z2 * z2);
if (mag <= 1e-3) continue;
float d =
(x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
float d2 = min(d, temp[k]);
temp[k] = d2;
besti = d2 > best ? k : besti;
best = d2 > best ? d2 : best;
}
dists[tid] = best;
dists_i[tid] = besti;
__syncthreads();
if (block_size >= 512) {
if (tid < 256) {
__update(dists, dists_i, tid, tid + 256);
}
__syncthreads();
}
if (block_size >= 256) {
if (tid < 128) {
__update(dists, dists_i, tid, tid + 128);
}
__syncthreads();
}
if (block_size >= 128) {
if (tid < 64) {
__update(dists, dists_i, tid, tid + 64);
}
__syncthreads();
}
if (block_size >= 64) {
if (tid < 32) {
__update(dists, dists_i, tid, tid + 32);
}
__syncthreads();
}
if (block_size >= 32) {
if (tid < 16) {
__update(dists, dists_i, tid, tid + 16);
}
__syncthreads();
}
if (block_size >= 16) {
if (tid < 8) {
__update(dists, dists_i, tid, tid + 8);
}
__syncthreads();
}
if (block_size >= 8) {
if (tid < 4) {
__update(dists, dists_i, tid, tid + 4);
}
__syncthreads();
}
if (block_size >= 4) {
if (tid < 2) {
__update(dists, dists_i, tid, tid + 2);
}
__syncthreads();
}
if (block_size >= 2) {
if (tid < 1) {
__update(dists, dists_i, tid, tid + 1);
}
__syncthreads();
}
old = dists_i[0];
if (tid == 0) idxs[j] = old;
}
}
void furthest_point_sampling_kernel_wrapper(int b, int n, int m,
const float *dataset, float *temp,
int *idxs) {
unsigned int n_threads = opt_n_threads(n);
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
switch (n_threads) {
case 512:
furthest_point_sampling_kernel<512>
<<>>(b, n, m, dataset, temp, idxs);
break;
case 256:
furthest_point_sampling_kernel<256>
<<>>(b, n, m, dataset, temp, idxs);
break;
case 128:
furthest_point_sampling_kernel<128>
<<>>(b, n, m, dataset, temp, idxs);
break;
case 64:
furthest_point_sampling_kernel<64>
<<>>(b, n, m, dataset, temp, idxs);
break;
case 32:
furthest_point_sampling_kernel<32>
<<>>(b, n, m, dataset, temp, idxs);
break;
case 16:
furthest_point_sampling_kernel<16>
<<>>(b, n, m, dataset, temp, idxs);
break;
case 8:
furthest_point_sampling_kernel<8>
<<>>(b, n, m, dataset, temp, idxs);
break;
case 4:
furthest_point_sampling_kernel<4>
<<>>(b, n, m, dataset, temp, idxs);
break;
case 2:
furthest_point_sampling_kernel<2>
<<>>(b, n, m, dataset, temp, idxs);
break;
case 1:
furthest_point_sampling_kernel<1>
<<>>(b, n, m, dataset, temp, idxs);
break;
default:
furthest_point_sampling_kernel<512>
<<>>(b, n, m, dataset, temp, idxs);
}
CUDA_CHECK_ERRORS();
}
================================================
FILE: pointnet2_ops/pointnet2_ops/_version.py
================================================
__version__ = "3.0.0"
================================================
FILE: pointnet2_ops/pointnet2_ops/pointnet2_modules.py
================================================
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from pointnet2_ops import pointnet2_utils
def build_shared_mlp(mlp_spec: List[int], bn: bool = True):
layers = []
for i in range(1, len(mlp_spec)):
layers.append(
nn.Conv2d(mlp_spec[i - 1], mlp_spec[i], kernel_size=1, bias=not bn)
)
if bn:
layers.append(nn.BatchNorm2d(mlp_spec[i]))
layers.append(nn.ReLU(True))
return nn.Sequential(*layers)
class _PointnetSAModuleBase(nn.Module):
def __init__(self):
super(_PointnetSAModuleBase, self).__init__()
self.npoint = None
self.groupers = None
self.mlps = None
def forward(
self, xyz: torch.Tensor, features: Optional[torch.Tensor]
) -> Tuple[torch.Tensor, torch.Tensor]:
r"""
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor of the xyz coordinates of the features
features : torch.Tensor
(B, C, N) tensor of the descriptors of the the features
Returns
-------
new_xyz : torch.Tensor
(B, npoint, 3) tensor of the new features' xyz
new_features : torch.Tensor
(B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors
"""
new_features_list = []
xyz_flipped = xyz.transpose(1, 2).contiguous()
new_xyz = (
pointnet2_utils.gather_operation(
xyz_flipped, pointnet2_utils.furthest_point_sample(xyz, self.npoint)
)
.transpose(1, 2)
.contiguous()
if self.npoint is not None
else None
)
for i in range(len(self.groupers)):
new_features = self.groupers[i](
xyz, new_xyz, features
) # (B, C, npoint, nsample)
new_features = self.mlps[i](new_features) # (B, mlp[-1], npoint, nsample)
new_features = F.max_pool2d(
new_features, kernel_size=[1, new_features.size(3)]
) # (B, mlp[-1], npoint, 1)
new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint)
new_features_list.append(new_features)
return new_xyz, torch.cat(new_features_list, dim=1)
class PointnetSAModuleMSG(_PointnetSAModuleBase):
r"""Pointnet set abstrction layer with multiscale grouping
Parameters
----------
npoint : int
Number of features
radii : list of float32
list of radii to group with
nsamples : list of int32
Number of samples in each ball query
mlps : list of list of int32
Spec of the pointnet before the global max_pool for each scale
bn : bool
Use batchnorm
"""
def __init__(self, npoint, radii, nsamples, mlps, bn=True, use_xyz=True):
# type: (PointnetSAModuleMSG, int, List[float], List[int], List[List[int]], bool, bool) -> None
super(PointnetSAModuleMSG, self).__init__()
assert len(radii) == len(nsamples) == len(mlps)
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
for i in range(len(radii)):
radius = radii[i]
nsample = nsamples[i]
self.groupers.append(
pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
if npoint is not None
else pointnet2_utils.GroupAll(use_xyz)
)
mlp_spec = mlps[i]
if use_xyz:
mlp_spec[0] += 3
self.mlps.append(build_shared_mlp(mlp_spec, bn))
class PointnetSAModule(PointnetSAModuleMSG):
r"""Pointnet set abstrction layer
Parameters
----------
npoint : int
Number of features
radius : float
Radius of ball
nsample : int
Number of samples in the ball query
mlp : list
Spec of the pointnet before the global max_pool
bn : bool
Use batchnorm
"""
def __init__(
self, mlp, npoint=None, radius=None, nsample=None, bn=True, use_xyz=True
):
# type: (PointnetSAModule, List[int], int, float, int, bool, bool) -> None
super(PointnetSAModule, self).__init__(
mlps=[mlp],
npoint=npoint,
radii=[radius],
nsamples=[nsample],
bn=bn,
use_xyz=use_xyz,
)
class PointnetFPModule(nn.Module):
r"""Propigates the features of one set to another
Parameters
----------
mlp : list
Pointnet module parameters
bn : bool
Use batchnorm
"""
def __init__(self, mlp, bn=True):
# type: (PointnetFPModule, List[int], bool) -> None
super(PointnetFPModule, self).__init__()
self.mlp = build_shared_mlp(mlp, bn=bn)
def forward(self, unknown, known, unknow_feats, known_feats):
# type: (PointnetFPModule, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor) -> torch.Tensor
r"""
Parameters
----------
unknown : torch.Tensor
(B, n, 3) tensor of the xyz positions of the unknown features
known : torch.Tensor
(B, m, 3) tensor of the xyz positions of the known features
unknow_feats : torch.Tensor
(B, C1, n) tensor of the features to be propigated to
known_feats : torch.Tensor
(B, C2, m) tensor of features to be propigated
Returns
-------
new_features : torch.Tensor
(B, mlp[-1], n) tensor of the features of the unknown features
"""
if known is not None:
dist, idx = pointnet2_utils.three_nn(unknown, known)
dist_recip = 1.0 / (dist + 1e-8)
norm = torch.sum(dist_recip, dim=2, keepdim=True)
weight = dist_recip / norm
interpolated_feats = pointnet2_utils.three_interpolate(
known_feats, idx, weight
)
else:
interpolated_feats = known_feats.expand(
*(known_feats.size()[0:2] + [unknown.size(1)])
)
if unknow_feats is not None:
new_features = torch.cat(
[interpolated_feats, unknow_feats], dim=1
) # (B, C2 + C1, n)
else:
new_features = interpolated_feats
new_features = new_features.unsqueeze(-1)
new_features = self.mlp(new_features)
return new_features.squeeze(-1)
================================================
FILE: pointnet2_ops/pointnet2_ops/pointnet2_utils.py
================================================
import torch
import torch.nn as nn
import warnings
from torch.autograd import Function
from typing import *
try:
import pointnet2_ops._ext as _ext
except ImportError:
from torch.utils.cpp_extension import load
import glob
import os.path as osp
import os
warnings.warn("Unable to load pointnet2_ops cpp extension. JIT Compiling.")
_ext_src_root = osp.join(osp.dirname(__file__), "_ext-src")
_ext_sources = glob.glob(osp.join(_ext_src_root, "src", "*.cpp")) + glob.glob(
osp.join(_ext_src_root, "src", "*.cu")
)
_ext_headers = glob.glob(osp.join(_ext_src_root, "include", "*"))
os.environ["TORCH_CUDA_ARCH_LIST"] = "3.7+PTX;5.0;6.0;6.1;6.2;7.0;7.5"
_ext = load(
"_ext",
sources=_ext_sources,
extra_include_paths=[osp.join(_ext_src_root, "include")],
extra_cflags=["-O3"],
extra_cuda_cflags=["-O3", "-Xfatbin", "-compress-all"],
with_cuda=True,
)
class FurthestPointSampling(Function):
@staticmethod
def forward(ctx, xyz, npoint):
# type: (Any, torch.Tensor, int) -> torch.Tensor
r"""
Uses iterative furthest point sampling to select a set of npoint features that have the largest
minimum distance
Parameters
----------
xyz : torch.Tensor
(B, N, 3) tensor where N > npoint
npoint : int32
number of features in the sampled set
Returns
-------
torch.Tensor
(B, npoint) tensor containing the set
"""
out = _ext.furthest_point_sampling(xyz, npoint)
ctx.mark_non_differentiable(out)
return out
@staticmethod
def backward(ctx, grad_out):
return ()
furthest_point_sample = FurthestPointSampling.apply
class GatherOperation(Function):
@staticmethod
def forward(ctx, features, idx):
# type: (Any, torch.Tensor, torch.Tensor) -> torch.Tensor
r"""
Parameters
----------
features : torch.Tensor
(B, C, N) tensor
idx : torch.Tensor
(B, npoint) tensor of the features to gather
Returns
-------
torch.Tensor
(B, C, npoint) tensor
"""
ctx.save_for_backward(idx, features)
output = _ext.gather_points(features, idx)
return output
@staticmethod
def backward(ctx, grad_out):
idx, features = ctx.saved_tensors
N = features.size(2)
grad_features = _ext.gather_points_grad(grad_out.contiguous(), idx, N)
return grad_features, None
gather_operation = GatherOperation.apply
class ThreeNN(Function):
@staticmethod
def forward(ctx, unknown, known):
# type: (Any, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]
r"""
Find the three nearest neighbors of unknown in known
Parameters
----------
unknown : torch.Tensor
(B, n, 3) tensor of known features
known : torch.Tensor
(B, m, 3) tensor of unknown features
Returns
-------
dist : torch.Tensor
(B, n, 3) l2 distance to the three nearest neighbors
idx : torch.Tensor
(B, n, 3) index of 3 nearest neighbors
"""
dist2, idx = _ext.three_nn(unknown, known)
dist = torch.sqrt(dist2)
ctx.mark_non_differentiable(dist, idx)
return dist, idx
@staticmethod
def backward(ctx, grad_dist, grad_idx):
return ()
three_nn = ThreeNN.apply
class ThreeInterpolate(Function):
@staticmethod
def forward(ctx, features, idx, weight):
# type(Any, torch.Tensor, torch.Tensor, torch.Tensor) -> Torch.Tensor
r"""
Performs weight linear interpolation on 3 features
Parameters
----------
features : torch.Tensor
(B, c, m) Features descriptors to be interpolated from
idx : torch.Tensor
(B, n, 3) three nearest neighbors of the target features in features
weight : torch.Tensor
(B, n, 3) weights
Returns
-------
torch.Tensor
(B, c, n) tensor of the interpolated features
"""
ctx.save_for_backward(idx, weight, features)
return _ext.three_interpolate(features, idx, weight)
@staticmethod
def backward(ctx, grad_out):
# type: (Any, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
r"""
Parameters
----------
grad_out : torch.Tensor
(B, c, n) tensor with gradients of ouputs
Returns
-------
grad_features : torch.Tensor
(B, c, m) tensor with gradients of features
None
None
"""
idx, weight, features = ctx.saved_tensors
m = features.size(2)
grad_features = _ext.three_interpolate_grad(
grad_out.contiguous(), idx, weight, m
)
return grad_features, torch.zeros_like(idx), torch.zeros_like(weight)
three_interpolate = ThreeInterpolate.apply
class GroupingOperation(Function):
@staticmethod
def forward(ctx, features, idx):
# type: (Any, torch.Tensor, torch.Tensor) -> torch.Tensor
r"""
Parameters
----------
features : torch.Tensor
(B, C, N) tensor of features to group
idx : torch.Tensor
(B, npoint, nsample) tensor containing the indicies of features to group with
Returns
-------
torch.Tensor
(B, C, npoint, nsample) tensor
"""
ctx.save_for_backward(idx, features)
return _ext.group_points(features, idx)
@staticmethod
def backward(ctx, grad_out):
# type: (Any, torch.tensor) -> Tuple[torch.Tensor, torch.Tensor]
r"""
Parameters
----------
grad_out : torch.Tensor
(B, C, npoint, nsample) tensor of the gradients of the output from forward
Returns
-------
torch.Tensor
(B, C, N) gradient of the features
None
"""
idx, features = ctx.saved_tensors
N = features.size(2)
grad_features = _ext.group_points_grad(grad_out.contiguous(), idx, N)
return grad_features, torch.zeros_like(idx)
grouping_operation = GroupingOperation.apply
class BallQuery(Function):
@staticmethod
def forward(ctx, radius, nsample, xyz, new_xyz):
# type: (Any, float, int, torch.Tensor, torch.Tensor) -> torch.Tensor
r"""
Parameters
----------
radius : float
radius of the balls
nsample : int
maximum number of features in the balls
xyz : torch.Tensor
(B, N, 3) xyz coordinates of the features
new_xyz : torch.Tensor
(B, npoint, 3) centers of the ball query
Returns
-------
torch.Tensor
(B, npoint, nsample) tensor with the indicies of the features that form the query balls
"""
output = _ext.ball_query(new_xyz, xyz, radius, nsample)
ctx.mark_non_differentiable(output)
return output
@staticmethod
def backward(ctx, grad_out):
return ()
ball_query = BallQuery.apply
class QueryAndGroup(nn.Module):
r"""
Groups with a ball query of radius
Parameters
---------
radius : float32
Radius of ball
nsample : int32
Maximum number of features to gather in the ball
"""
def __init__(self, radius, nsample, use_xyz=True):
# type: (QueryAndGroup, float, int, bool) -> None
super(QueryAndGroup, self).__init__()
self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz
def forward(self, xyz, new_xyz, features=None):
# type: (QueryAndGroup, torch.Tensor. torch.Tensor, torch.Tensor) -> Tuple[Torch.Tensor]
r"""
Parameters
----------
xyz : torch.Tensor
xyz coordinates of the features (B, N, 3)
new_xyz : torch.Tensor
centriods (B, npoint, 3)
features : torch.Tensor
Descriptors of the features (B, C, N)
Returns
-------
new_features : torch.Tensor
(B, 3 + C, npoint, nsample) tensor
"""
idx = ball_query(self.radius, self.nsample, xyz, new_xyz)
xyz_trans = xyz.transpose(1, 2).contiguous()
grouped_xyz = grouping_operation(xyz_trans, idx) # (B, 3, npoint, nsample)
grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1)
if features is not None:
grouped_features = grouping_operation(features, idx)
if self.use_xyz:
new_features = torch.cat(
[grouped_xyz, grouped_features], dim=1
) # (B, C + 3, npoint, nsample)
else:
new_features = grouped_features
else:
assert (
self.use_xyz
), "Cannot have not features and not use xyz as a feature!"
new_features = grouped_xyz
return new_features
class GroupAll(nn.Module):
r"""
Groups all features
Parameters
---------
"""
def __init__(self, use_xyz=True):
# type: (GroupAll, bool) -> None
super(GroupAll, self).__init__()
self.use_xyz = use_xyz
def forward(self, xyz, new_xyz, features=None):
# type: (GroupAll, torch.Tensor, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor]
r"""
Parameters
----------
xyz : torch.Tensor
xyz coordinates of the features (B, N, 3)
new_xyz : torch.Tensor
Ignored
features : torch.Tensor
Descriptors of the features (B, C, N)
Returns
-------
new_features : torch.Tensor
(B, C + 3, 1, N) tensor
"""
grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
if features is not None:
grouped_features = features.unsqueeze(2)
if self.use_xyz:
new_features = torch.cat(
[grouped_xyz, grouped_features], dim=1
) # (B, 3 + C, 1, N)
else:
new_features = grouped_features
else:
new_features = grouped_xyz
return new_features
================================================
FILE: pointnet2_ops/setup.py
================================================
import glob
import os.path as osp
from setuptools import find_packages, setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
this_dir = osp.dirname(osp.abspath(__file__))
_ext_src_root = osp.join("pointnet2_ops", "_ext-src")
_ext_sources = glob.glob(osp.join(_ext_src_root, "src", "*.cpp")) + glob.glob(
osp.join(_ext_src_root, "src", "*.cu")
)
requirements = ["torch>=1.4"]
exec(open(osp.join("pointnet2_ops", "_version.py")).read())
setup(
name="pointnet2_ops",
version=__version__,
author="Erik Wijmans",
packages=find_packages(),
install_requires=requirements,
ext_modules=[
CUDAExtension(
name="pointnet2_ops._ext",
sources=_ext_sources,
extra_compile_args={
"cxx": ["-O3"],
"nvcc": ["-O3", "-Xfatbin", "-compress-all"],
},
include_dirs=[osp.join(this_dir, _ext_src_root, "include")],
)
],
cmdclass={"build_ext": BuildExtension},
include_package_data=True,
)
================================================
FILE: pyproject.toml
================================================
[build-system]
requires = ["setuptools>=45,<78", "wheel", "setuptools-scm"]
build-backend = "setuptools.build_meta"
[project]
name = "grasp_gen"
version = "1.0.0"
description = "GraspGen: A Diffusion-based Framework for 6-DOF Grasping"
authors = [
{name = "Adithya Murali", email = "admurali@nvidia.com"},
]
maintainers = [
{name = "Adithya Murali", email = "admurali@nvidia.com"},
]
license = {text = "NVIDIA Corporation"}
readme = "README.md"
requires-python = ">=3.10"
keywords = ["robotics", "manipulation", "learning", "computer-vision", "grasping"]
classifiers = [
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Natural Language :: English",
"Topic :: Scientific/Engineering",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
]
dependencies = [
# Core PyTorch dependencies with CUDA support
"torch==2.1.0",
"torchvision==0.16.0",
# PyTorch Geometric dependencies (installed from PyG wheel index)
"torch-cluster",
"torch-scatter",
"torch-geometric",
# Core dependencies from requirements.txt
"pickle5",
"h5py",
"hydra-core",
"matplotlib",
"meshcat",
"numpy==1.26.4",
"webdataset",
"scikit-learn",
"scipy",
"tensorboard",
"trimesh==4.5.3",
"transformers",
"tensordict",
"diffusers==0.11.1",
"timm==1.0.15",
"huggingface-hub==0.25.2",
"PyOpenGL==3.1.0",
"addict",
"spconv-cu120",
"yapf==0.40.1",
"tensorboardx",
"sharedarray",
"yourdfpy==0.0.56",
# Additional dependencies from pip installation
"pyrender",
"scene-synthesizer[recommend]",
"imageio",
"pytest",
"viser",
# ZMQ serving
"pyzmq",
"msgpack",
"msgpack-numpy",
# Pin setuptools to a version that still includes pkg_resources
# (required by torch==2.1.0 cpp_extension for building CUDA extensions like pointnet2_ops)
"setuptools>=45,<78",
]
# Note: pointnet2_ops is a local dependency that must be installed separately.
# For seamless installation, use: ./install_pointnet.sh
# Or manually: cd pointnet2_ops && uv pip install --no-build-isolation .
[project.urls]
"Homepage" = "https://graspgen.github.io"
"Repository" = "https://github.com/NVlabs/GraspGen"
"Documentation" = "https://graspgen.github.io"
"Paper" = "https://arxiv.org/abs/2507.13097"
"Models" = "https://huggingface.co/adithyamurali/GraspGenModels"
"Dataset" = "https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GraspGen"
[project.optional-dependencies]
dev = [
"black",
"isort",
"flake8",
]
serve = [
"pyzmq",
"msgpack",
"msgpack-numpy",
]
[tool.setuptools]
include-package-data = true
[tool.setuptools.packages.find]
where = ["."]
include = ["grasp_gen*"]
[tool.setuptools.package-data]
"*" = ["*.yaml", "*.yml", "*.json", "*.txt", "*.md"]
[tool.pytest.ini_options]
testpaths = ["tests"]
markers = [
"integration: tests requiring a live GraspGen server on localhost:5556",
]
[tool.black]
line-length = 88
target-version = ['py310']
[tool.isort]
profile = "black"
line_length = 88
[tool.uv]
# Allow pre-release packages (needed for pyrender -> pyglet dependencies)
prerelease = "allow"
# Use find-links for PyG packages (flat HTML page, not a PEP 503 index)
find-links = ["https://data.pyg.org/whl/torch-2.1.0+cu121.html"]
# Build dependencies for packages that need special build requirements
[tool.uv.extra-build-dependencies]
sharedarray = ["setuptools>=45,<78"]
================================================
FILE: requirements.txt
================================================
pickle5
h5py
hydra-core
matplotlib
meshcat
numpy==1.26.4
webdataset
scikit-learn
scipy
tensorboard
trimesh==4.5.3
transformers
tensordict
diffusers==0.11.1
timm==1.0.15
huggingface-hub==0.25.2
PyOpenGL==3.1.5
addict
spconv-cu120
yapf==0.40.1
tensorboardx
sharedarray
torch-geometric
yourdfpy==0.0.56
imageio
================================================
FILE: runs/train_graspgen_franka_panda_dis.sh
================================================
#!/usr/bin/env bash
# Fixed parameters
export NGPU=1
export NWORKER=4
export NEPOCH=5000
export BATCH=8
export PRINT_FREQ=10
export PLOT_FREQ=10
export SAVE_FREQ=2000
export DATASET_NAME="objaverse"
export DATASET_VERSION="v2"
export NUM_GRASPS_PER_OBJ=300
export POSE_REPR="mlp"
export BACKBONE="ptv3"
export ROTATION_REPR="r3_so3"
export TOPK_RATIO=0.75
export NUM_POINTS=2048
export NOISE_SCALE=3.27
export NAME="meshandpc"
export PROBABILITY_SAMPLE_PARTIAL_POINTCLOUD=0.50
export NUM_REDUNDANT_DATAPOINTS=7
export GRIPPER_NAME="franka_panda"
export OBJECT_DATASET_DIR="/object_dataset"
export GRASP_DIR="/grasp_dataset"
export CODE_DIR="/code"
export RESULTS_DIR="/results"
export GRASP_DATASET_DIR="$GRASP_DIR/grasp_data/$GRIPPER_NAME"
export SPLIT_DATASET_DIR="$GRASP_DIR/splits/$GRIPPER_NAME"
export METHOD="grasp_gen"
export RATIO="[0.25,0.24,0.00,0.01,0.00,0.25,0.25]"
export PYOPENGL_PLATFORM="osmesa"
export LOG_DIR="$RESULTS_DIR/logs/${GRIPPER_NAME}_dis_test"
export CACHE_DIR="$RESULTS_DIR/cache"
export CHECKPOINT="$LOG_DIR/last.pth"
export CONSOLE_LOG="$LOG_DIR/console_log.txt"
echo "Running Training for $GRIPPER_NAME"
rm -rf $LOG_DIR
mkdir -p $LOG_DIR
mkdir -p $CACHE_DIR
cd $CODE_DIR && cd $CODE_DIR/scripts && \
python train_graspgen.py \
data.num_points=$NUM_POINTS \
data.load_contact=False \
data.dataset_cls="ObjectPickDataset" \
data.rotation_augmentation=True \
data.cache_dir=$CACHE_DIR \
data.root_dir=$SPLIT_DATASET_DIR \
data.object_root_dir=$OBJECT_DATASET_DIR \
data.grasp_root_dir=$GRASP_DATASET_DIR \
data.dataset_name=$DATASET_NAME \
data.dataset_version=$DATASET_VERSION \
data.prob_point_cloud=$PROBABILITY_SAMPLE_PARTIAL_POINTCLOUD \
data.redundancy=$NUM_REDUNDANT_DATAPOINTS \
data.gripper_name=$GRIPPER_NAME \
train.log_dir=$LOG_DIR \
train.batch_size=$BATCH \
train.num_gpus=$NGPU \
train.num_epochs=$NEPOCH \
train.num_workers=$NWORKER \
train.print_freq=$PRINT_FREQ \
train.plot_freq=$PLOT_FREQ \
train.save_freq=$SAVE_FREQ \
train.checkpoint=$CHECKPOINT \
train.model_name='discriminator' \
optimizer.type="ADAMW" \
optimizer.grad_clip=-1 \
optimizer.lr=0.00001 \
discriminator.gripper_name=$GRIPPER_NAME \
discriminator.topk_ratio=$TOPK_RATIO \
discriminator.obs_backbone=$BACKBONE \
discriminator.grasp_repr=$ROTATION_REPR \
discriminator.pose_repr=$POSE_REPR \
discriminator.kappa=$NOISE_SCALE \
discriminator.ptv3.grid_size=0.01 \
data.num_grasps_per_object=$NUM_GRASPS_PER_OBJ \
data.load_discriminator_dataset=True \
data.discriminator_ratio=$RATIO | tee $CONSOLE_LOG
================================================
FILE: runs/train_graspgen_franka_panda_gen.sh
================================================
#!/usr/bin/env bash
# Fixed parameters
export NGPU=1
export NWORKER=4
export NEPOCH=5000
export BATCH=8
export PRINT_FREQ=10
export PLOT_FREQ=10
export SAVE_FREQ=10
export DATASET_NAME="objaverse"
export DATASET_VERSION="v2"
export TIMESTEPS=10
export NUM_GRASPS_PER_OBJ=500
export BACKBONE="ptv3"
export NUM_POINTS=3500
export NAME="meshandpc"
export PROBABILITY_SAMPLE_PARTIAL_POINTCLOUD=0.50
export NUM_REDUNDANT_DATAPOINTS=7
export GRIPPER_NAME="franka_panda"
export OBJECT_DATASET_DIR="/object_dataset"
export GRASP_DIR="/grasp_dataset"
export CODE_DIR="/code"
export RESULTS_DIR="/results"
export GRASP_DATASET_DIR="$GRASP_DIR/grasp_data/$GRIPPER_NAME"
export SPLIT_DATASET_DIR="$GRASP_DIR/splits/$GRIPPER_NAME"
export METHOD="grasp_gen"
export PYRENDER_INSTALL_PREFIX="apt-get update -y && apt-get install -y tmux libosmesa6-dev && pip install pyrender && pip install PyOpenGL==3.1.5 && export PYOPENGL_PLATFORM=osmesa && "
export ROTATION_REPR="r3_so3"
export PYOPENGL_PLATFORM="osmesa"
export NOISE_SCALE=3.27
export LOG_DIR="$RESULTS_DIR/logs/${GRIPPER_NAME}_gen_test"
export CHECKPOINT="$LOG_DIR/last.pth"
export CACHE_DIR="$RESULTS_DIR/cache"
export CONSOLE_LOG="$LOG_DIR/console_log.txt"
echo "Running Training for $GRIPPER_NAME"
rm -rf $LOG_DIR
mkdir -p $LOG_DIR
mkdir -p $CACHE_DIR
cd $CODE_DIR && cd $CODE_DIR/scripts && \
python train_graspgen.py \
data.num_points=$NUM_POINTS \
data.load_contact=False \
data.dataset_cls="ObjectPickDataset" \
data.rotation_augmentation=True \
data.cache_dir=$CACHE_DIR \
data.root_dir=$SPLIT_DATASET_DIR \
data.object_root_dir=$OBJECT_DATASET_DIR \
data.grasp_root_dir=$GRASP_DATASET_DIR \
data.dataset_name=$DATASET_NAME \
data.dataset_version=$DATASET_VERSION \
data.prob_point_cloud=$PROBABILITY_SAMPLE_PARTIAL_POINTCLOUD \
data.redundancy=$NUM_REDUNDANT_DATAPOINTS \
data.gripper_name=$GRIPPER_NAME \
train.log_dir=$LOG_DIR \
train.batch_size=$BATCH \
train.num_gpus=$NGPU \
train.num_epochs=$NEPOCH \
train.num_workers=$NWORKER \
train.print_freq=$PRINT_FREQ \
train.plot_freq=$PLOT_FREQ \
train.save_freq=$SAVE_FREQ \
train.checkpoint=$CHECKPOINT \
train.model_name='diffusion' \
optimizer.type="ADAMW" \
optimizer.lr=0.00001 \
optimizer.grad_clip=-1 \
diffusion.gripper_name=$GRIPPER_NAME \
diffusion.num_diffusion_iters=$TIMESTEPS \
diffusion.num_diffusion_iters_eval=$TIMESTEPS \
diffusion.obs_backbone=$BACKBONE \
diffusion.grasp_repr=$ROTATION_REPR \
diffusion.attention='cat_attn' \
diffusion.compositional_schedular=True \
diffusion.loss_pointmatching=False \
diffusion.loss_l1_pos=True \
diffusion.loss_l1_rot=True \
diffusion.ptv3.grid_size=0.01 \
diffusion.pose_repr='mlp' \
data.num_grasps_per_object=$NUM_GRASPS_PER_OBJ \
data.load_discriminator_dataset=False \
data.visualize_batch=False \
diffusion.kappa=$NOISE_SCALE | tee $CONSOLE_LOG
================================================
FILE: runs/train_graspgen_robotiq_2f_140_dis.sh
================================================
#!/usr/bin/env bash
# Fixed parameters
export NGPU=1
export NWORKER=4
export NEPOCH=5000
export BATCH=8
export PRINT_FREQ=10
export PLOT_FREQ=10
export SAVE_FREQ=2000
export DATASET_NAME="objaverse"
export DATASET_VERSION="v2"
export NUM_GRASPS_PER_OBJ=300
export POSE_REPR="mlp"
export BACKBONE="pointnet"
export ROTATION_REPR="r3_so3"
export TOPK_RATIO=0.75
export NUM_POINTS=2048
export NOISE_SCALE=2.02217
export NAME="meshandpc"
export PROBABILITY_SAMPLE_PARTIAL_POINTCLOUD=0.50
export NUM_REDUNDANT_DATAPOINTS=7
export GRIPPER_NAME="robotiq_2f_140"
export OBJECT_DATASET_DIR="/object_dataset"
export GRASP_DIR="/grasp_dataset"
export CODE_DIR="/code"
export RESULTS_DIR="/results"
export GRASP_DATASET_DIR="$GRASP_DIR/grasp_data/$GRIPPER_NAME"
export SPLIT_DATASET_DIR="$GRASP_DIR/splits/$GRIPPER_NAME"
export METHOD="grasp_gen"
export RATIO="[0.25,0.24,0.00,0.01,0.00,0.25,0.25]"
export PYOPENGL_PLATFORM="osmesa"
export LOG_DIR="$RESULTS_DIR/logs/${GRIPPER_NAME}_dis_test"
export CACHE_DIR="$RESULTS_DIR/cache"
export CHECKPOINT="$LOG_DIR/last.pth"
export CONSOLE_LOG="$LOG_DIR/console_log.txt"
echo "Running Training for $GRIPPER_NAME"
rm -rf $LOG_DIR
mkdir -p $LOG_DIR
mkdir -p $CACHE_DIR
cd $CODE_DIR && cd $CODE_DIR/scripts && \
python train_graspgen.py \
data.num_points=$NUM_POINTS \
data.load_contact=False \
data.dataset_cls="ObjectPickDataset" \
data.rotation_augmentation=True \
data.root_dir=$SPLIT_DATASET_DIR \
data.object_root_dir=$OBJECT_DATASET_DIR \
data.grasp_root_dir=$GRASP_DATASET_DIR \
data.dataset_name=$DATASET_NAME \
data.dataset_version=$DATASET_VERSION \
data.prob_point_cloud=$PROBABILITY_SAMPLE_PARTIAL_POINTCLOUD \
data.redundancy=$NUM_REDUNDANT_DATAPOINTS \
data.gripper_name=$GRIPPER_NAME \
data.cache_dir=$CACHE_DIR \
train.log_dir=$LOG_DIR \
train.batch_size=$BATCH \
train.num_gpus=$NGPU \
train.num_epochs=$NEPOCH \
train.num_workers=$NWORKER \
train.print_freq=$PRINT_FREQ \
train.plot_freq=$PLOT_FREQ \
train.save_freq=$SAVE_FREQ \
train.checkpoint=$CHECKPOINT \
train.model_name='discriminator' \
optimizer.type="ADAMW" \
optimizer.grad_clip=-1 \
optimizer.lr=0.00001 \
discriminator.gripper_name=$GRIPPER_NAME \
discriminator.topk_ratio=$TOPK_RATIO \
discriminator.obs_backbone=$BACKBONE \
discriminator.grasp_repr=$ROTATION_REPR \
discriminator.pose_repr=$POSE_REPR \
discriminator.kappa=$NOISE_SCALE \
data.num_grasps_per_object=$NUM_GRASPS_PER_OBJ \
data.load_discriminator_dataset=True \
data.discriminator_ratio=$RATIO | tee $CONSOLE_LOG
================================================
FILE: runs/train_graspgen_robotiq_2f_140_gen.sh
================================================
#!/usr/bin/env bash
# Fixed parameters
export NGPU=1
export NWORKER=4
export NEPOCH=5000
export BATCH=8
export PRINT_FREQ=10
export PLOT_FREQ=10
export SAVE_FREQ=2000
export DATASET_NAME="objaverse"
export DATASET_VERSION="v2"
export TIMESTEPS=10
export NUM_GRASPS_PER_OBJ=500
export BACKBONE="pointnet"
export NUM_POINTS=2048
export NAME="meshandpc"
export PROBABILITY_SAMPLE_PARTIAL_POINTCLOUD=0.5
export NUM_REDUNDANT_DATAPOINTS=7
export GRIPPER_NAME="robotiq_2f_140"
export OBJECT_DATASET_DIR="/object_dataset"
export GRASP_DIR="/grasp_dataset"
export CODE_DIR="/code"
export RESULTS_DIR="/results"
export GRASP_DATASET_DIR="$GRASP_DIR/grasp_data/$GRIPPER_NAME"
export SPLIT_DATASET_DIR="$GRASP_DIR/splits/$GRIPPER_NAME"
export METHOD="grasp_gen"
export ROTATION_REPR="r3_so3"
export NOISE_SCALE=2.02217
export PYOPENGL_PLATFORM="osmesa"
export LOG_DIR="$RESULTS_DIR/logs/${GRIPPER_NAME}_gen_test"
export CACHE_DIR="$RESULTS_DIR/cache"
export CHECKPOINT="$LOG_DIR/last.pth"
export CONSOLE_LOG="$LOG_DIR/console_log.txt"
echo "Running Training for $GRIPPER_NAME"
rm -rf $LOG_DIR
mkdir -p $LOG_DIR
mkdir -p $CACHE_DIR
cd $CODE_DIR && cd $CODE_DIR/scripts && \
python train_graspgen.py \
data.num_points=$NUM_POINTS \
data.load_contact=False \
data.dataset_cls="ObjectPickDataset" \
data.rotation_augmentation=True \
data.root_dir=$SPLIT_DATASET_DIR \
data.object_root_dir=$OBJECT_DATASET_DIR \
data.grasp_root_dir=$GRASP_DATASET_DIR \
data.dataset_name=$DATASET_NAME \
data.dataset_version=$DATASET_VERSION \
data.prob_point_cloud=$PROBABILITY_SAMPLE_PARTIAL_POINTCLOUD \
data.redundancy=$NUM_REDUNDANT_DATAPOINTS \
data.gripper_name=$GRIPPER_NAME \
data.cache_dir=$CACHE_DIR \
train.log_dir=$LOG_DIR \
train.batch_size=$BATCH \
train.num_gpus=$NGPU \
train.num_epochs=$NEPOCH \
train.num_workers=$NWORKER \
train.print_freq=$PRINT_FREQ \
train.plot_freq=$PLOT_FREQ \
train.save_freq=$SAVE_FREQ \
train.checkpoint=$CHECKPOINT \
train.model_name='diffusion' \
optimizer.type="ADAMW" \
optimizer.lr=0.00001 \
optimizer.grad_clip=-1 \
diffusion.gripper_name=$GRIPPER_NAME \
diffusion.num_diffusion_iters=$TIMESTEPS \
diffusion.num_diffusion_iters_eval=$TIMESTEPS \
diffusion.obs_backbone=$BACKBONE \
diffusion.grasp_repr=$ROTATION_REPR \
diffusion.attention='cat_attn' \
diffusion.compositional_schedular=True \
diffusion.loss_pointmatching=False \
diffusion.loss_l1_pos=True \
diffusion.loss_l1_rot=True \
diffusion.ptv3.grid_size=0.01 \
diffusion.pose_repr='mlp' \
data.num_grasps_per_object=$NUM_GRASPS_PER_OBJ \
data.load_discriminator_dataset=False \
data.visualize_batch=False \
diffusion.kappa=$NOISE_SCALE | tee $CONSOLE_LOG
================================================
FILE: scripts/config.yaml
================================================
data:
root_dir: ''
cache_dir: '/tmp/graspgen_train_cache/'
tasks: ['pick']
num_points: 16384
num_object_points: 1024
cam_coord: False
num_rotations: 64
grid_resolution: 0.01
jitter_scale: 0.
contact_radius: 0.005
dist_above_table: 0.002
robot_prob: 1.0
offset_bins: [
0, 0.00794435329, 0.0158887021, 0.0238330509,
0.0317773996, 0.0397217484, 0.0476660972,
0.055610446, 0.0635547948, 0.0714991435, 0.08
]
random_seed: -1
rotation_augmentation: False
add_depth_noise: False
downsample_points: True
downsample_to_fixed_batch_size: False
dataset_cls: 'PickPlaceDataset'
load_patch: False
load_discriminator_dataset: False # True for GraspGenDiscriminator
patch_width: 256
prob_point_cloud : -1
object_root_dir : ''
grasp_root_dir : ''
dataset_name : 'acronym'
dataset_version : 'v1'
gripper_name: 'franka_panda' # TODO: Define this just in one place.
num_grasps_per_object: -1
load_contact: False
prefiltering: False
discriminator_ratio: [0.50, 0.20, 0.25, 0.05, 0.0] # [0.40, 0.35, 0.15, 0.05, 0.05]
visualize_batch: False # visualize the batch datapoint during training
onpolicy_dataset_dir: null
onpolicy_dataset_h5_path: null
redundancy: 1
preload_dataset: True
m2t2:
scene_encoder:
type: 'pointnet2_msg'
num_points: 16384
downsample: 4
radius: 0.02
radius_mult: 2
use_rgb: False
object_encoder:
type: 'pointnet2_msg_cls'
num_points: 1024
downsample: 4
radius: 0.02
radius_mult: 2
use_rgb: False
contact_decoder:
mask_feature: 'res0'
in_features: ['res1', 'res2', 'res3']
place_feature: 'res4'
embed_dim: 256
feedforward_dim: 512
num_scales: 3
num_layers: 3
num_heads: 8
num_grasp_queries: 100
num_place_queries: 0
use_attn_mask: True
use_task_embed: False
activation: 'GELU'
pos_enc: 'new'
action_decoder:
use_embed: False
conf_thresh: 0.5
max_num_pred: null
hidden_dim: 256
num_layers: 2
activation: 'GELU'
offset_bins: [
0, 0.00794435329, 0.0158887021, 0.0238330509,
0.0317773996, 0.0397217484, 0.0476660972,
0.055610446, 0.0635547948, 0.0714991435, 0.08
] # DEPRECATED
gripper_depth: 0.1034 # 0.075 for suction
gripper_name: 'franka_panda'
matcher:
object_weight: 2.0
bce_weight: 5.0
dice_weight: 5.0
grasp_loss:
object_weight: 2.0
not_object_weight: 0.1
pseudo_ce_weight: 0.0
bce_topk: 512
bce_weight: 5.0
dice_weight: 5.0
deep_supervision: True
recompute_indices: True
adds_pred2gt: 100.0
adds_gt2pred: 0.0
adds_per_obj: False
contact_dir: 0.0
approach_dir: 0.0
offset: 1.0
offset_bin_weights: [
0.16652107, 0.21488856, 0.37031708, 0.55618503, 0.75124664,
0.93943357, 1.07824539, 1.19423112, 1.55731375, 3.17161779
] # DEPRECATED
gripper_name: 'franka_panda'
place_loss:
bce_topk: 1024
bce_weight: 5.0
dice_weight: 5.0
deep_supervision: True
diffusion:
obs_backbone: 'pointnet'
checkpoint_object_encoder_pretrained: null
num_embed_dim: 256
num_obs_dim: 512
diffusion_embed_dim: 512
image_size: 256
num_diffusion_iters: 100
num_diffusion_iters_eval: 100
compositional_schedular: True
loss_pointmatching: True
loss_l1_pos: False
loss_l1_rot: False
grasp_repr: 'r3_6d'
kappa: -1
clip_sample: True
beta_schedule: 'squaredcos_cap_v2'
npn_architecture: 'ffn'
attention: 'cat'
gripper_name: 'franka_panda'
pose_repr: 'mlp'
num_grasps_per_object: 100 # Only used during eval
ptv3:
grid_size: 0.01 # Only relevant for PTv3 backbone
discriminator:
checkpoint: null
checkpoint_object_encoder_pretrained: null
obs_backbone: 'pointnet'
num_embed_dim: 256
num_obs_dim: 512
grasp_repr: 'r3_6d'
pose_repr: 'mlp'
topk_ratio: 0.75
kappa: 1.0
gripper_name: 'franka_panda'
ptv3:
grid_size: 0.01 # Only relevant for PTv3 backbone
optimizer:
type: 'ADAMW'
base_batch_size: 128 # DEPRECATED
base_lr: 0.0008 # DEPRECATED
backbone_multiplier: 1.0 # DEPRECATED
grad_clip: -1 # DEPRECATED
weight_decay: 0.05
lr_scale_multiplier: 1.0 # DEPRECATED
lr: 0.00005
momentum: 0.9
train:
model_name: 'm2t2'
num_gpus: 1
port: '1234'
mask_thresh: 0.5
batch_size: 8
num_workers: 8
num_epochs: 160
print_freq: 25
plot_freq: 50
save_freq: 10
eval_freq: 5
checkpoint: null
log_dir: ''
num_scenes: null
debug: False
debug_ddp: False
eval:
model_name: 'm2t2' # Choose from [m2t2, diffusion, discriminator]
task: 'pick'
split: 'valid'
first_scene: -1
last_scene: 1000
scene: null
acronym_dir: ''
checkpoint: ''
output_dir: null
exp_name: null
cam_coord: False
object_thresh: 0.2
mask_thresh: 0.2
seed_thresh: null
num_seed_grasps: 150
max_num_grasps: 200
retract: 0.
grid_res: 0.01
placement_height: 0.02
placement_vis_radius: 0.2
batch_size: 4
num_workers: 1
print_freq: 1
num_procs: 1
num_vis: 5
num_runs: 1
debug: False
scene_file: ""
output_file: ""
write_output_file: True
grasp_seeder: False
config_save_file: "/code/FoundationGrasp/configtest.yaml"
color_grasp_with_conf: False
meshcat:
visualize: True
point_size: 0.01
line_width: 2
obj:
scale: 1.0
num_sample_points: 2024
================================================
FILE: scripts/convert_obj_to_usd.py
================================================
# Copyright (c) 2025, 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.
"""Convert mesh files (OBJ, STL, PLY) to USD format using scene_synthesizer."""
import argparse
import os
import sys
import scene_synthesizer as synth
# Light blue (RGB 0-1) for --light-blue option.
LIGHT_BLUE_RGB = (0.68, 0.85, 1.0)
def parse_args():
parser = argparse.ArgumentParser(
description="Convert a mesh file to USD format using scene_synthesizer"
)
parser.add_argument(
"--input",
type=str,
required=True,
help="Path to the input mesh file (obj, stl, or ply)",
)
parser.add_argument(
"--output",
type=str,
default="",
help="Path to the output USD file. If empty, replaces the input extension with .usd",
)
parser.add_argument(
"--scale",
type=float,
default=1.0,
help="Scale factor to apply to the mesh",
)
parser.add_argument(
"--up-axis",
type=str,
default="Z",
choices=["Y", "Z"],
help="Up axis for the USD stage",
)
parser.add_argument(
"--light-blue",
action="store_true",
help="Set the mesh to a light blue display color in the exported USD",
)
parser.add_argument(
"--color",
type=float,
nargs=3,
metavar=("R", "G", "B"),
default=None,
help="Set mesh display color in the exported USD (RGB 0-1, e.g. --color 0.68 0.85 1.0)",
)
return parser.parse_args()
def set_usd_mesh_display_color(usd_path: str, color_rgb: tuple) -> None:
"""Set displayColor on all Mesh prims in a USD file.
Args:
usd_path: Path to the USD file (modified in place).
color_rgb: (r, g, b) in 0-1 range.
"""
from pxr import Gf, Sdf, Usd, UsdGeom, Vt
stage = Usd.Stage.Open(usd_path)
for prim in stage.Traverse():
if prim.IsA(UsdGeom.Mesh):
primvars_api = UsdGeom.PrimvarsAPI(prim)
display_color = primvars_api.CreatePrimvar(
"displayColor", Sdf.ValueTypeNames.Color3f
)
display_color.Set(Vt.Vec3fArray([Gf.Vec3f(*color_rgb)]))
stage.GetRootLayer().Save()
def convert_mesh_to_usd(
input_path: str,
output_path: str,
scale: float = 1.0,
up_axis: str = "Z",
display_color_rgb: tuple = None,
):
"""Convert a mesh file to USD format.
Args:
input_path: Path to the input mesh file.
output_path: Path to the output USD file.
scale: Scale factor to apply.
up_axis: Up axis for the USD stage ('Y' or 'Z').
display_color_rgb: Optional (r, g, b) in 0-1 to set on all mesh prims.
Returns:
The output USD file path.
"""
if not os.path.exists(input_path):
raise FileNotFoundError(f"Input file not found: {input_path}")
asset = synth.Asset(input_path, scale=scale)
scene = asset.scene()
os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
scene.export(output_path, file_type=output_path.rsplit(".", 1)[-1], up_axis=up_axis)
if display_color_rgb is not None:
set_usd_mesh_display_color(output_path, display_color_rgb)
print(f"Set display color to RGB{display_color_rgb}")
print(f"Converted {input_path} -> {output_path}")
return output_path
if __name__ == "__main__":
args = parse_args()
valid_input_extensions = (".obj", ".stl", ".ply")
if not args.input.endswith(valid_input_extensions):
print(f"Error: Input must be one of {valid_input_extensions}", file=sys.stderr)
sys.exit(1)
output_path = args.output
if output_path == "":
output_path = os.path.splitext(args.input)[0] + ".usd"
valid_output_extensions = (".usd", ".usda", ".usdc")
if not output_path.endswith(valid_output_extensions):
print(f"Error: Output must be one of {valid_output_extensions}", file=sys.stderr)
sys.exit(1)
display_color = None
if args.light_blue:
display_color = LIGHT_BLUE_RGB
elif args.color is not None:
display_color = tuple(args.color)
convert_mesh_to_usd(
args.input,
output_path,
scale=args.scale,
up_axis=args.up_axis,
display_color_rgb=display_color,
)
================================================
FILE: scripts/create_grasp_sim_usd.py
================================================
# Copyright (c) 2025, 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.
"""Create a USD scene for Omniverse/Isaac Sim with multiple envs: each env has
the object and one gripper at a predicted grasp pose. Use with
scripts/run_grasp_sim_omniverse.py in Isaac Sim so that on Play the grippers close
and grasp the object.
"""
import argparse
import math
import os
import sys
# Allow running from repo root: python scripts/create_grasp_sim_usd.py
_script_dir = os.path.dirname(os.path.abspath(__file__))
if _script_dir not in sys.path:
sys.path.insert(0, _script_dir)
from save_grasps_to_usd import _load_yaml_grasps
import numpy as np
from pxr import Gf, Sdf, Usd, UsdGeom, UsdPhysics
WORLD_ROOT = "/World"
ENV_SPACING_DEFAULT = 0.6
# Robotiq 2F-85 USD root prim (note: file may say 2F_86 internally)
GRIPPER_DEFAULT_PRIM = "/Robotiq_2F_86"
OBJECT_DEFAULT_PRIM = "/world"
def _numpy_to_gf_matrix4d(m: np.ndarray) -> Gf.Matrix4d:
return Gf.Matrix4d(*m.T.flatten().tolist())
def _env_offset_index(i: int, num_envs: int, env_spacing: float):
"""Return (x, y, 0) world offset for env i in a 2D grid (row-major)."""
cols = math.ceil(math.sqrt(num_envs))
row = i // cols
col = i % cols
return (col * env_spacing, row * env_spacing, 0.0)
def create_grasp_sim_usd(
object_usd_path: str,
grasps: np.ndarray,
confidences: np.ndarray,
output_path: str,
gripper_usd_path: str,
object_prim_path: str = OBJECT_DEFAULT_PRIM,
gripper_prim_path: str = GRIPPER_DEFAULT_PRIM,
num_envs: int = 10,
env_spacing: float = ENV_SPACING_DEFAULT,
) -> str:
"""Build a USD with num_envs environments: each has object + gripper at grasp pose.
Args:
object_usd_path: Path to object USD (e.g. box.usd).
grasps: (N, 4, 4) grasp transforms in object frame.
confidences: (N,) confidence scores (unused but kept for API).
output_path: Output USD path (e.g. box_with_grasps_sim.usd).
gripper_usd_path: Path to gripper USD (e.g. robotiq_2f_85.usd).
object_prim_path: Prim path in object USD to reference (default /world).
gripper_prim_path: Prim path in gripper USD to reference (default /Robotiq_2F_86).
num_envs: Number of envs to create (default 10).
env_spacing: Spacing between env origins in meters.
Returns:
output_path.
"""
assert grasps.ndim == 3 and grasps.shape[1:] == (4, 4)
n = min(num_envs, len(grasps))
grasps = grasps[:n]
confidences = confidences[:n]
stage = Usd.Stage.CreateInMemory()
stage.SetDefaultPrim(stage.DefinePrim(WORLD_ROOT, "Xform"))
root = stage.GetPrimAtPath(WORLD_ROOT)
out_dir = os.path.dirname(os.path.abspath(output_path))
def _ref_path(abs_path: str) -> str:
p = os.path.abspath(abs_path)
return os.path.relpath(p, out_dir) if out_dir else p
object_ref = _ref_path(object_usd_path)
gripper_ref = _ref_path(gripper_usd_path)
for i in range(n):
env_path = f"{WORLD_ROOT}/Env_{i}"
env_xform = UsdGeom.Xform.Define(stage, env_path)
offset = _env_offset_index(i, n, env_spacing)
env_xform.AddTranslateOp().Set(Gf.Vec3d(*offset))
obj_path = f"{env_path}/object"
obj_prim = stage.DefinePrim(obj_path, "Xform")
obj_prim.GetReferences().AddReference(Sdf.Reference(object_ref, object_prim_path))
# Override collision approximation to convexHull so PhysX doesn't
# fall back from triangle mesh (which isn't valid for dynamic bodies).
coll_over = stage.OverridePrim(f"{obj_path}/object/box_obj/box_obj")
coll_over.CreateAttribute(
"physics:approximation", Sdf.ValueTypeNames.Token
).Set("convexHull")
# Override the fixed joint's world-side anchor to the env offset so the
# joint isn't disjointed (original localPos0 is at the world origin).
joint_over = stage.OverridePrim(f"{obj_path}/object/world_fixed_joint")
joint_over.CreateAttribute(
"physics:localPos0", Sdf.ValueTypeNames.Point3f
).Set(Gf.Vec3f(float(offset[0]), float(offset[1]), float(offset[2])))
# Gripper at grasp pose in env-local frame (env Xform already applies offset)
gripper_path = f"{env_path}/gripper"
gripper_xform = UsdGeom.Xform.Define(stage, gripper_path)
gripper_xform.AddTransformOp().Set(_numpy_to_gf_matrix4d(grasps[i]))
gripper_prim = stage.GetPrimAtPath(gripper_path)
gripper_prim.GetReferences().AddReference(
Sdf.Reference(gripper_ref, gripper_prim_path)
)
stage.GetRootLayer().Export(output_path)
print(f"Saved {n} envs to {output_path}")
return output_path
def main():
parser = argparse.ArgumentParser(
description="Create box_with_grasps_sim.usd for Omniverse/Isaac Sim"
)
parser.add_argument(
"--object_usd",
type=str,
required=True,
help="Path to object USD (e.g. assets/objects/box.usd)",
)
parser.add_argument(
"--grasps_yaml",
type=str,
required=True,
help="Path to Isaac-format grasp YAML (e.g. from demo_object_mesh.py --output_file)",
)
parser.add_argument(
"--output",
type=str,
default="box_with_grasps_sim.usd",
help="Output USD path",
)
parser.add_argument(
"--gripper_usd",
type=str,
default="",
help="Path to gripper USD (default: GraspGen assets/bots/robotiq_2f_85.usd)",
)
parser.add_argument(
"--num_envs",
type=int,
default=10,
help="Max number of envs (grasps) to write",
)
parser.add_argument(
"--env_spacing",
type=float,
default=ENV_SPACING_DEFAULT,
help="Spacing between env origins in meters",
)
args = parser.parse_args()
script_dir = os.path.dirname(os.path.abspath(__file__))
repo_root = os.path.dirname(script_dir) # GraspGen
default_gripper = os.path.join(repo_root, "assets", "bots", "robotiq_2f_85.usd")
gripper_usd = args.gripper_usd or default_gripper
if not os.path.exists(args.object_usd):
print(f"Error: Object USD not found: {args.object_usd}", file=sys.stderr)
sys.exit(1)
if not os.path.exists(args.grasps_yaml):
print(f"Error: Grasps YAML not found: {args.grasps_yaml}", file=sys.stderr)
sys.exit(1)
if not os.path.exists(gripper_usd):
print(f"Error: Gripper USD not found: {gripper_usd}", file=sys.stderr)
sys.exit(1)
grasps, confidences = _load_yaml_grasps(args.grasps_yaml)
create_grasp_sim_usd(
args.object_usd,
grasps,
confidences,
args.output,
gripper_usd,
num_envs=args.num_envs,
env_spacing=args.env_spacing,
)
if __name__ == "__main__":
main()
================================================
FILE: scripts/demo_collision_free_grasps.py
================================================
# Copyright (c) 2025, 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 argparse
import os
import time
from typing import Tuple, Dict, List
import numpy as np
import torch
import trimesh
import trimesh.transformations as tra
from IPython import embed
from tqdm import tqdm
from grasp_gen.grasp_server import GraspGenSampler, load_grasp_cfg
from grasp_gen.utils.viser_utils import (
create_visualizer,
get_color_from_score,
make_frame,
visualize_grasp,
visualize_mesh,
visualize_pointcloud,
)
from grasp_gen.utils.point_cloud_utils import (
point_cloud_outlier_removal,
knn_points,
depth_and_segmentation_to_point_clouds,
filter_colliding_grasps,
)
from grasp_gen.robot import get_gripper_info
def parse_args():
parser = argparse.ArgumentParser(
description="Run collision-free grasp inference from depth image and segmentation mask"
)
parser.add_argument(
"--depth_image_path",
type=str,
required=True,
help="Path to the depth image (e.g., .npy or image file)",
)
parser.add_argument(
"--segmentation_mask_path",
type=str,
required=True,
help="Path to the instance segmentation mask (e.g., .npy or image file)",
)
parser.add_argument(
"--rgb_image_path",
type=str,
default=None,
help="Path to the RGB image (optional, for colored point cloud visualization)",
)
parser.add_argument(
"--camera_intrinsics",
type=float,
nargs=4,
required=True,
default=[1480.589599609375, 1480.589599609375, 960.0, 540.0],
help="Camera intrinsics as [fx, fy, cx, cy]",
)
parser.add_argument(
"--gripper_config",
type=str,
required=True,
help="Path to gripper configuration YAML file",
)
parser.add_argument(
"--grasp_threshold",
type=float,
default=0.8,
help="Threshold for valid grasps. If -1.0, then the top 100 grasps will be ranked and returned",
)
parser.add_argument(
"--num_grasps",
type=int,
default=200,
help="Number of grasps to generate",
)
parser.add_argument(
"--return_topk",
action="store_true",
help="Whether to return only the top k grasps",
)
parser.add_argument(
"--topk_num_grasps",
type=int,
default=-1,
help="Number of top grasps to return when return_topk is True",
)
parser.add_argument(
"--collision_threshold",
type=float,
default=0.02, # 2mm
help="Distance threshold for collision detection (in meters)",
)
parser.add_argument(
"--max_scene_points",
type=int,
default=8192,
help="Maximum number of scene points to use for collision checking (for speed optimization)",
)
parser.add_argument(
"--visualize",
action="store_true",
help="Whether to visualize the results",
)
return parser.parse_args()
def process_point_cloud(pc, grasps, grasp_conf, pc_colors=None):
"""Process point cloud and grasps by centering them."""
scores = get_color_from_score(grasp_conf, use_255_scale=True)
print(f"Scores with min {grasp_conf.min():.3f} and max {grasp_conf.max():.3f}")
# Ensure grasps have correct homogeneous coordinate
grasps[:, 3, 3] = 1
# Center point cloud and grasps
T_subtract_pc_mean = tra.translation_matrix(-pc.mean(axis=0))
pc_centered = tra.transform_points(pc, T_subtract_pc_mean)
grasps_centered = np.array(
[T_subtract_pc_mean @ np.array(g) for g in grasps.tolist()]
)
# Add red tint to colors if RGB data is available
pc_colors_centered = pc_colors
if pc_colors is not None:
pc_colors_centered = pc_colors.copy().astype(np.float32)
# Add red tint: increase red channel by 40% while keeping it in valid range
pc_colors_centered[:, 0] = np.clip(pc_colors_centered[:, 0] * 1.4, 0, 255)
pc_colors_centered = pc_colors_centered.astype(np.uint8)
return pc_centered, grasps_centered, scores, T_subtract_pc_mean, pc_colors_centered
if __name__ == "__main__":
start_time = time.time()
args = parse_args()
if not os.path.exists(args.gripper_config):
raise ValueError(f"Gripper config {args.gripper_config} does not exist")
# Handle return_topk logic
if args.return_topk and args.topk_num_grasps == -1:
args.topk_num_grasps = 100
print(f"Starting collision-free grasp detection at {time.strftime('%H:%M:%S')}")
print("=" * 60)
# Load depth image and segmentation mask
load_start = time.time()
if args.depth_image_path.endswith(".npy"):
depth_image = np.load(args.depth_image_path)
else:
# Assume it's an image file that can be loaded with standard libraries
from PIL import Image
depth_image = np.array(Image.open(args.depth_image_path))
# Convert to meters if needed (this may need adjustment based on your data format)
depth_image = (
depth_image.astype(np.float32) / 1000.0
) # Assuming mm to m conversion
if args.segmentation_mask_path.endswith(".npy"):
segmentation_mask = np.load(args.segmentation_mask_path)
else:
from PIL import Image
segmentation_mask = np.array(Image.open(args.segmentation_mask_path))
# Load RGB image if provided
rgb_image = None
if args.rgb_image_path:
if args.rgb_image_path.endswith(".npy"):
rgb_image = np.load(args.rgb_image_path)
else:
from PIL import Image
rgb_image = np.array(Image.open(args.rgb_image_path))
print(f"Loaded RGB image with shape: {rgb_image.shape}")
load_time = time.time() - load_start
print(f"Data loading took: {load_time:.2f} seconds")
print(f"Loaded depth image with shape: {depth_image.shape}")
print(f"Loaded segmentation mask with shape: {segmentation_mask.shape}")
print(f"Segmentation unique values: {np.unique(segmentation_mask)}")
# Unpack camera intrinsics
fx, fy, cx, cy = args.camera_intrinsics
# Create point clouds from depth and segmentation
pc_start = time.time()
try:
scene_pc, object_pc, scene_colors, object_colors = (
depth_and_segmentation_to_point_clouds(
depth_image=depth_image,
segmentation_mask=segmentation_mask,
fx=fx,
fy=fy,
cx=cx,
cy=cy,
rgb_image=rgb_image,
target_object_id=1, # Assuming object has ID 1
remove_object_from_scene=True,
)
)
except ValueError as e:
print(f"Error creating point clouds: {e}")
exit(1)
pc_creation_time = time.time() - pc_start
print(f"Point cloud creation took: {pc_creation_time:.2f} seconds")
# Load grasp configuration and get gripper info
grasp_cfg = load_grasp_cfg(args.gripper_config)
gripper_name = grasp_cfg.data.gripper_name
gripper_info = get_gripper_info(gripper_name)
gripper_collision_mesh = gripper_info.collision_mesh
print(f"Using gripper: {gripper_name}")
print(f"Gripper collision mesh has {len(gripper_collision_mesh.vertices)} vertices")
# Initialize visualization if requested
if args.visualize:
vis = create_visualizer()
# Filter object point cloud to remove outliers
filter_start = time.time()
object_pc_torch = torch.from_numpy(object_pc)
pc_filtered, pc_removed = point_cloud_outlier_removal(object_pc_torch)
pc_filtered = pc_filtered.numpy()
pc_removed = pc_removed.numpy()
filter_time = time.time() - filter_start
print(f"Point cloud filtering took: {filter_time:.2f} seconds")
print(
f"Filtered object point cloud: {len(pc_filtered)} outlier points (removed {len(pc_removed)})"
)
# Initialize GraspGenSampler and run inference
inference_start = time.time()
grasp_sampler = GraspGenSampler(grasp_cfg)
grasps_inferred, grasp_conf_inferred = GraspGenSampler.run_inference(
pc_filtered,
grasp_sampler,
grasp_threshold=args.grasp_threshold,
num_grasps=args.num_grasps,
topk_num_grasps=args.topk_num_grasps,
)
inference_time = time.time() - inference_start
print(f"Grasp inference took: {inference_time:.2f} seconds")
if len(grasps_inferred) == 0:
print("No grasps found from inference!")
exit(1)
# Convert to numpy
grasp_conf_inferred = grasp_conf_inferred.cpu().numpy()
grasps_inferred = grasps_inferred.cpu().numpy()
grasps_inferred[:, 3, 3] = 1
print(
f"Inferred {len(grasps_inferred)} grasps, with scores ranging from {grasp_conf_inferred.min():.3f} - {grasp_conf_inferred.max():.3f}"
)
# Process point clouds and grasps for consistent coordinate frame
pc_centered, grasps_centered, scores, T_center, object_colors_centered = (
process_point_cloud(
pc_filtered, grasps_inferred, grasp_conf_inferred, object_colors
)
)
scene_pc_centered = tra.transform_points(scene_pc, T_center)
# Add red tint to scene colors if RGB data is available
scene_colors_centered = scene_colors
if scene_colors is not None:
scene_colors_centered = scene_colors.copy().astype(np.float32)
# Add red tint: increase red channel by 40% while keeping it in valid range
scene_colors_centered[:, 0] = np.clip(scene_colors_centered[:, 0] * 1.4, 0, 255)
scene_colors_centered = scene_colors_centered.astype(np.uint8)
# Downsample scene point cloud for faster collision checking (keep full resolution for visualization)
if len(scene_pc_centered) > args.max_scene_points:
indices = np.random.choice(
len(scene_pc_centered), args.max_scene_points, replace=False
)
scene_pc_downsampled = scene_pc_centered[indices]
print(
f"Downsampled scene point cloud from {len(scene_pc_centered)} to {len(scene_pc_downsampled)} points for collision checking"
)
else:
scene_pc_downsampled = scene_pc_centered
print(
f"Scene point cloud has {len(scene_pc_centered)} points (no downsampling needed)"
)
# Filter collision grasps using downsampled scene
collision_start = time.time()
collision_free_mask = filter_colliding_grasps(
scene_pc=scene_pc_downsampled,
grasp_poses=grasps_centered,
gripper_collision_mesh=gripper_collision_mesh,
collision_threshold=args.collision_threshold,
)
collision_time = time.time() - collision_start
print(f"Collision detection took: {collision_time:.2f} seconds")
# Filter grasps to only collision-free ones
collision_free_grasps = grasps_centered[collision_free_mask]
collision_free_scores = grasp_conf_inferred[collision_free_mask]
collision_free_colors = scores[collision_free_mask]
print(
f"Final result: {len(collision_free_grasps)} collision-free grasps out of {len(grasps_inferred)} total grasps"
)
# Save results
results = {
"all_grasps": grasps_centered,
"all_scores": grasp_conf_inferred,
"collision_free_mask": collision_free_mask,
"collision_free_grasps": collision_free_grasps,
"collision_free_scores": collision_free_scores,
"scene_pc": scene_pc_centered,
"object_pc": pc_centered,
"camera_intrinsics": {"fx": fx, "fy": fy, "cx": cx, "cy": cy},
}
output_file = "collision_free_grasps_results.npz"
np.savez(output_file, **results)
print(f"Results saved to {output_file}")
# Print timing summary
total_time = time.time() - start_time
print("\n" + "=" * 60)
print("TIMING SUMMARY:")
print("=" * 60)
print(f"Data loading: {load_time:.2f}s")
print(f"Point cloud creation: {pc_creation_time:.2f}s")
print(f"Point cloud filtering: {filter_time:.2f}s")
print(f"Grasp inference: {inference_time:.2f}s")
print(f"Collision detection: {collision_time:.2f}s")
print(f"Total time: {total_time:.2f}s")
print("=" * 60)
# Visualize results if requested
if args.visualize:
viz_start = time.time()
# Visualize scene point cloud - use RGB colors if available, otherwise use gray
if scene_colors_centered is not None:
visualize_pointcloud(
vis, "scene_pc", scene_pc_centered, scene_colors_centered, size=0.002
)
else:
visualize_pointcloud(
vis, "scene_pc", scene_pc_centered, [128, 128, 128], size=0.002
)
# Visualize object point cloud - use RGB colors if available, otherwise use green
if object_colors_centered is not None:
visualize_pointcloud(
vis, "object_pc", pc_centered, object_colors_centered, size=0.0025
)
else:
visualize_pointcloud(
vis, "object_pc", pc_centered, [0, 255, 0], size=0.0025
)
# Visualize collision-free grasps
for i, (grasp, score) in enumerate(
zip(collision_free_grasps, collision_free_colors)
):
visualize_grasp(
vis,
f"collision_free_grasps/{i:03d}/grasp",
grasp,
color=score,
gripper_name=gripper_name,
linewidth=0.8,
)
# Visualize colliding grasps in red
colliding_grasps = grasps_centered[~collision_free_mask]
for i, grasp in enumerate(
colliding_grasps[:20]
): # Limit to first 20 for clarity
visualize_grasp(
vis,
f"colliding_grasps/{i:03d}/grasp",
grasp,
color=[255, 0, 0],
gripper_name=gripper_name,
linewidth=0.4,
)
viz_time = time.time() - viz_start
print(f"Visualization setup took: {viz_time:.2f}s")
if scene_colors_centered is not None or object_colors_centered is not None:
print("Visualization ready with RGB colors from the original image.")
else:
print(
"Visualization ready. Green point cloud is the target object, gray is the scene."
)
print(
f"Collision-free grasps are shown in their quality colors, colliding grasps (up to 20) are shown in red."
)
input("Press Enter to exit...")
================================================
FILE: scripts/demo_object_mesh.py
================================================
# Copyright (c) 2025, 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 argparse
import os
import numpy as np
import torch
import trimesh
import trimesh.transformations as tra
from pathlib import Path
from grasp_gen.grasp_server import GraspGenSampler, load_grasp_cfg
from grasp_gen.utils.viser_utils import (
create_visualizer,
get_color_from_score,
get_normals_from_mesh,
make_frame,
visualize_grasp,
visualize_mesh,
visualize_pointcloud,
)
from grasp_gen.utils.point_cloud_utils import point_cloud_outlier_removal
from grasp_gen.dataset.dataset_utils import sample_points
from grasp_gen.dataset.eval_utils import save_to_isaac_grasp_format
def parse_args():
parser = argparse.ArgumentParser(
description="Visualize grasps on a single object mesh after GraspGen inference"
)
parser.add_argument(
"--mesh_file",
type=str,
required=True,
help="Path to the mesh file (obj, stl, ply, usd, usda, usdc, or usdz)",
)
parser.add_argument(
"--gripper_config",
type=str,
default="",
help="Path to gripper configuration YAML file",
)
parser.add_argument(
"--grasp_threshold",
type=float,
default=-1,
help="Threshold for valid grasps. If -1.0, then the top 100 grasps will be ranked and returned",
)
parser.add_argument(
"--num_grasps",
type=int,
default=400,
help="Number of grasps to generate",
)
parser.add_argument(
"--return_topk",
action="store_true",
help="Whether to return only the top k grasps",
)
parser.add_argument(
"--topk_num_grasps",
type=int,
default=-1,
help="Number of top grasps to return when return_topk is True",
)
parser.add_argument(
"--mesh_scale",
type=float,
default=1.0,
help="Scale factor to apply to the mesh",
)
parser.add_argument(
"--num_sample_points",
type=int,
default=2000,
help="Number of points to sample from the mesh surface",
)
parser.add_argument(
"--output_file",
type=str,
default="",
help="Path to save the output grasps. If empty, will save to outputs/YYYY-MM-DD/latest/output_grasps.yml",
)
parser.add_argument(
"--no-visualization",
action="store_true",
help="Disable meshcat visualization",
)
return parser.parse_args()
def load_mesh_data(mesh_file, scale, num_sample_points):
"""Load mesh data and sample points from surface."""
if mesh_file.endswith("ply"):
import open3d as o3d
pcd = o3d.io.read_point_cloud(mesh_file)
xyz = np.array(pcd.points).astype(np.float32)
pt_idx = sample_points(xyz, num_sample_points)
xyz = xyz[pt_idx]
obj = None
elif mesh_file.endswith((".usd", ".usda", ".usdc", ".usdz")):
import scene_synthesizer as synth
asset = synth.Asset(mesh_file)
obj = asset.mesh()
obj.apply_scale(scale)
xyz, _ = trimesh.sample.sample_surface(obj, num_sample_points)
xyz = np.array(xyz)
else:
obj = trimesh.load(mesh_file)
obj.apply_scale(scale)
xyz, _ = trimesh.sample.sample_surface(obj, num_sample_points)
xyz = np.array(xyz)
# Center point cloud
T_subtract_pc_mean = tra.translation_matrix(-xyz.mean(axis=0))
xyz = tra.transform_points(xyz, T_subtract_pc_mean)
if obj is not None:
obj.apply_transform(T_subtract_pc_mean)
# Create dummy RGB values (white)
rgb = np.ones((len(xyz), 3)) * 255
return xyz, rgb, obj, T_subtract_pc_mean
if __name__ == "__main__":
args = parse_args()
if args.gripper_config == "":
raise ValueError("Gripper config is required")
if not os.path.exists(args.gripper_config):
raise ValueError(f"Gripper config {args.gripper_config} does not exist")
valid_extensions = (".stl", ".obj", ".ply", ".usd", ".usda", ".usdc", ".usdz")
if not args.mesh_file.endswith(valid_extensions):
raise ValueError(f"Mesh file must be one of {valid_extensions}")
# Handle return_topk logic
if args.return_topk and args.topk_num_grasps == -1:
args.topk_num_grasps = 100
# Create visualizer unless visualization is disabled
vis = None if args.no_visualization else create_visualizer()
# Load grasp configuration and initialize sampler
grasp_cfg = load_grasp_cfg(args.gripper_config)
gripper_name = grasp_cfg.data.gripper_name
grasp_sampler = GraspGenSampler(grasp_cfg)
# Load mesh data
print(f"Processing mesh file: {args.mesh_file}")
pc, pc_color, obj_mesh, T_subtract_pc_mean = load_mesh_data(
args.mesh_file, args.mesh_scale, args.num_sample_points
)
# Visualize original mesh
if not args.no_visualization and obj_mesh is not None:
visualize_mesh(vis, "object_mesh", obj_mesh, color=[169, 169, 169])
visualize_pointcloud(vis, "pc", pc, pc_color, size=0.0025)
# Run inference on point cloud
grasps_inferred, grasp_conf_inferred = GraspGenSampler.run_inference(
pc,
grasp_sampler,
grasp_threshold=args.grasp_threshold,
num_grasps=args.num_grasps,
topk_num_grasps=args.topk_num_grasps,
remove_outliers=False,
)
if len(grasps_inferred) > 0:
grasp_conf_inferred = grasp_conf_inferred.cpu().numpy()
grasps_inferred = grasps_inferred.cpu().numpy()
scores_inferred = get_color_from_score(grasp_conf_inferred, use_255_scale=True)
print(
f"Inferred {len(grasps_inferred)} grasps, with scores ranging from {grasp_conf_inferred.min():.3f} - {grasp_conf_inferred.max():.3f}"
)
# Visualize inferred grasps
if not args.no_visualization:
for j, grasp in enumerate(grasps_inferred):
visualize_grasp(
vis,
f"grasps_objectpc_filtered/{j:03d}/grasp",
grasp,
color=scores_inferred[j],
gripper_name=gripper_name,
linewidth=0.6,
)
# Convert grasps back to original mesh frame
grasps_inferred = np.array(
[tra.inverse_matrix(T_subtract_pc_mean) @ g for g in grasps_inferred]
)
# Save grasps to file only if output_file is not empty
if args.output_file != "":
print(f"Saving predicted grasps to {args.output_file}")
save_to_isaac_grasp_format(
grasps_inferred, grasp_conf_inferred, args.output_file
)
else:
print("No output file specified, skipping grasp saving")
else:
print("No grasps found from inference!")
================================================
FILE: scripts/demo_object_pc.py
================================================
# Copyright (c) 2025, 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 argparse
import glob
import json
import os
import omegaconf
import numpy as np
import omegaconf
import torch
import trimesh.transformations as tra
from IPython import embed
from grasp_gen.grasp_server import GraspGenSampler, load_grasp_cfg
from grasp_gen.utils.viser_utils import (
create_visualizer,
get_color_from_score,
get_normals_from_mesh,
make_frame,
visualize_grasp,
visualize_mesh,
visualize_pointcloud,
)
from grasp_gen.utils.point_cloud_utils import point_cloud_outlier_removal
def parse_args():
parser = argparse.ArgumentParser(
description="Visualize grasps on a single object point cloud after GraspGen inference"
)
parser.add_argument(
"--sample_data_dir",
type=str,
default="/code/realrobot_pc/final/",
help="Directory containing JSON files with point cloud data",
)
parser.add_argument(
"--gripper_config",
type=str,
default="",
help="Path to gripper configuration YAML file",
)
parser.add_argument(
"--grasp_threshold",
type=float,
default=0.8,
help="Threshold for valid grasps. If -1.0, then the top 100 grasps will be ranked and returned",
)
parser.add_argument(
"--num_grasps",
type=int,
default=200,
help="Number of grasps to generate",
)
parser.add_argument(
"--return_topk",
action="store_true",
help="Whether to return only the top k grasps",
)
parser.add_argument(
"--topk_num_grasps",
type=int,
default=-1,
help="Number of top grasps to return when return_topk is True",
)
return parser.parse_args()
def process_point_cloud(pc, grasps, grasp_conf):
"""Process point cloud and grasps by centering them."""
scores = get_color_from_score(grasp_conf, use_255_scale=True)
print(f"Scores with min {grasp_conf.min():.3f} and max {grasp_conf.max():.3f}")
# Ensure grasps have correct homogeneous coordinate
grasps[:, 3, 3] = 1
# Center point cloud and grasps
T_subtract_pc_mean = tra.translation_matrix(-pc.mean(axis=0))
pc_centered = tra.transform_points(pc, T_subtract_pc_mean)
grasps_centered = np.array(
[T_subtract_pc_mean @ np.array(g) for g in grasps.tolist()]
)
return pc_centered, grasps_centered, scores
if __name__ == "__main__":
args = parse_args()
if args.gripper_config == "":
raise ValueError("Gripper config is required")
if not os.path.exists(args.gripper_config):
raise ValueError(f"Gripper config {args.gripper_config} does not exist")
# Handle return_topk logic
if args.return_topk and args.topk_num_grasps == -1:
args.topk_num_grasps = 100
json_files = glob.glob(os.path.join(args.sample_data_dir, "*.json"))
vis = create_visualizer()
grasp_cfg = load_grasp_cfg(args.gripper_config)
gripper_name = grasp_cfg.data.gripper_name
# Initialize GraspGenSampler once
grasp_sampler = GraspGenSampler(grasp_cfg)
for json_file in json_files:
print(f"Processing {json_file}")
vis.scene.reset()
# Load data from JSON
data = json.load(open(json_file, "rb"))
pc = np.array(data["pc"])
pc_color = np.array(data["pc_color"])
grasps = np.array(data["grasp_poses"])
grasp_conf = np.array(data["grasp_conf"])
# Process point cloud and grasps
pc_centered, grasps_centered, scores = process_point_cloud(
pc, grasps, grasp_conf
)
# Visualize original point cloud
visualize_pointcloud(vis, "pc", pc_centered, pc_color, size=0.0025)
# Filter point cloud
pc_filtered, pc_removed = point_cloud_outlier_removal(
torch.from_numpy(pc_centered)
)
pc_filtered = pc_filtered.numpy()
pc_removed = pc_removed.numpy()
visualize_pointcloud(vis, "pc_removed", pc_removed, [255, 0, 0], size=0.003)
# Run inference on filtered point cloud
grasps_inferred, grasp_conf_inferred = GraspGenSampler.run_inference(
pc_filtered,
grasp_sampler,
grasp_threshold=args.grasp_threshold,
num_grasps=args.num_grasps,
topk_num_grasps=args.topk_num_grasps,
)
if len(grasps_inferred) > 0:
grasp_conf_inferred = grasp_conf_inferred.cpu().numpy()
grasps_inferred = grasps_inferred.cpu().numpy()
grasps_inferred[:, 3, 3] = 1
scores_inferred = get_color_from_score(
grasp_conf_inferred, use_255_scale=True
)
print(
f"Inferred {len(grasps_inferred)} grasps, with scores ranging from {grasp_conf_inferred.min():.3f} - {grasp_conf_inferred.max():.3f}"
)
# Visualize inferred grasps
for j, grasp in enumerate(grasps_inferred):
visualize_grasp(
vis,
f"grasps_objectpc_filtered/{j:03d}/grasp",
grasp,
color=scores_inferred[j],
gripper_name=gripper_name,
linewidth=0.6,
)
else:
print("No grasps found from inference! Skipping to next object...")
input("Press Enter to continue to next object...")
================================================
FILE: scripts/demo_scene_pc.py
================================================
# Copyright (c) 2025, 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 argparse
import glob
import json
import os
import time
import numpy as np
import omegaconf
import torch
import trimesh.transformations as tra
from tqdm import tqdm
from grasp_gen.grasp_server import GraspGenSampler, load_grasp_cfg
from grasp_gen.utils.viser_utils import (
create_visualizer,
get_color_from_score,
get_normals_from_mesh,
make_frame,
visualize_grasp,
visualize_mesh,
visualize_pointcloud,
)
from grasp_gen.utils.point_cloud_utils import (
point_cloud_outlier_removal_with_color,
filter_colliding_grasps,
)
from grasp_gen.robot import get_gripper_info
def process_grasps_for_visualization(pc, grasps, grasp_conf, pc_colors=None):
"""Process grasps and point cloud for visualization by centering them."""
scores = get_color_from_score(grasp_conf, use_255_scale=True)
print(f"Scores with min {grasp_conf.min():.3f} and max {grasp_conf.max():.3f}")
# Ensure grasps have correct homogeneous coordinate
grasps[:, 3, 3] = 1
# Center point cloud and grasps
T_subtract_pc_mean = tra.translation_matrix(-pc.mean(axis=0))
pc_centered = tra.transform_points(pc, T_subtract_pc_mean)
grasps_centered = np.array(
[T_subtract_pc_mean @ np.array(g) for g in grasps.tolist()]
)
return pc_centered, grasps_centered, scores, T_subtract_pc_mean
def parse_args():
parser = argparse.ArgumentParser(
description="Visualize grasps on a scene point cloud after GraspGen inference, for entire scene"
)
parser.add_argument(
"--sample_data_dir",
type=str,
default="",
help="Directory containing JSON files with point cloud data",
)
parser.add_argument(
"--gripper_config",
type=str,
default="",
help="Path to gripper configuration YAML file",
)
parser.add_argument(
"--grasp_threshold",
type=float,
default=0.80,
help="Threshold for valid grasps. If -1.0, then the top 100 grasps will be ranked and returned",
)
parser.add_argument(
"--num_grasps",
type=int,
default=200,
help="Number of grasps to generate",
)
parser.add_argument(
"--return_topk",
action="store_true",
help="Whether to return only the top k grasps",
)
parser.add_argument(
"--topk_num_grasps",
type=int,
default=-1,
help="Number of top grasps to return when return_topk is True",
)
parser.add_argument(
"--filter_collisions",
action="store_true",
help="Whether to filter grasps based on collision detection with scene point cloud",
)
parser.add_argument(
"--collision_threshold",
type=float,
default=0.02,
help="Distance threshold for collision detection (in meters)",
)
parser.add_argument(
"--max_scene_points",
type=int,
default=8192,
help="Maximum number of scene points to use for collision checking (for speed optimization)",
)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
if args.sample_data_dir == "":
raise ValueError("sample_data_dir is required")
if args.gripper_config == "":
raise ValueError("gripper_config is required")
if not os.path.exists(args.sample_data_dir):
raise FileNotFoundError(
f"sample_data_dir {args.sample_data_dir} does not exist"
)
# Handle return_topk logic
if args.return_topk and args.topk_num_grasps == -1:
args.topk_num_grasps = 100
json_files = glob.glob(os.path.join(args.sample_data_dir, "*.json"))
# Load gripper config and get gripper name
grasp_cfg = load_grasp_cfg(args.gripper_config)
gripper_name = grasp_cfg.data.gripper_name
# Get gripper collision mesh for collision filtering
gripper_info = None
gripper_collision_mesh = None
if args.filter_collisions:
gripper_info = get_gripper_info(gripper_name)
gripper_collision_mesh = gripper_info.collision_mesh
print(f"Using gripper: {gripper_name}")
print(
f"Gripper collision mesh has {len(gripper_collision_mesh.vertices)} vertices"
)
# Initialize GraspGenSampler once
grasp_sampler = GraspGenSampler(grasp_cfg)
vis = create_visualizer()
for json_file in json_files:
print(json_file)
vis.scene.reset()
data = json.load(open(json_file, "rb"))
obj_pc = np.array(data["object_info"]["pc"])
obj_pc_color = np.array(data["object_info"]["pc_color"])
grasps = np.array(data["grasp_info"]["grasp_poses"])
grasp_conf = np.array(data["grasp_info"]["grasp_conf"])
full_pc_key = "pc_color" if "pc_color" in data["scene_info"] else "full_pc"
xyz_scene = np.array(data["scene_info"][full_pc_key])[0]
xyz_scene_color = np.array(data["scene_info"]["img_color"]).reshape(1, -1, 3)[
0, :, :
]
# Remove object points from scene point cloud, as we may use this for collision checking later. It is okay if grasps are close to the object point cloud.
xyz_seg = np.array(data["scene_info"]["obj_mask"]).reshape(-1)
xyz_scene = xyz_scene[xyz_seg != 1]
xyz_scene_color = xyz_scene_color[xyz_seg != 1]
VIZ_BOUNDS = [[-1.5, -1.25, -0.15], [1.5, 1.25, 2.0]]
mask_within_bounds = np.all((xyz_scene > VIZ_BOUNDS[0]), 1)
mask_within_bounds = np.logical_and(
mask_within_bounds, np.all((xyz_scene < VIZ_BOUNDS[1]), 1)
)
# mask_within_bounds = np.ones(xyz_scene.shape[0]).astype(np.bool_)
xyz_scene = xyz_scene[mask_within_bounds]
xyz_scene_color = xyz_scene_color[mask_within_bounds]
visualize_pointcloud(vis, "pc_scene", xyz_scene, xyz_scene_color, size=0.0025)
obj_pc, pc_removed, obj_pc_color, obj_pc_color_removed = (
point_cloud_outlier_removal_with_color(
torch.from_numpy(obj_pc), torch.from_numpy(obj_pc_color)
)
)
obj_pc = obj_pc.cpu().numpy()
pc_removed = pc_removed.cpu().numpy()
obj_pc_color = obj_pc_color.cpu().numpy()
obj_pc_color_removed = obj_pc_color_removed.cpu().numpy()
visualize_pointcloud(vis, "pc_obj", obj_pc, obj_pc_color, size=0.005)
grasps, grasp_conf = GraspGenSampler.run_inference(
obj_pc,
grasp_sampler,
grasp_threshold=args.grasp_threshold,
num_grasps=args.num_grasps,
topk_num_grasps=args.topk_num_grasps,
)
if len(grasps) > 0:
grasp_conf = grasp_conf.cpu().numpy()
grasps = grasps.cpu().numpy()
grasps[:, 3, 3] = 1
# Process grasps for visualization (centering)
obj_pc_centered, grasps_centered, scores, T_center = (
process_grasps_for_visualization(
obj_pc, grasps, grasp_conf, obj_pc_color
)
)
# Center scene point cloud using same transformation
xyz_scene_centered = tra.transform_points(xyz_scene, T_center)
# Apply collision filtering if requested
collision_free_mask = None
collision_free_grasps = grasps_centered
collision_free_scores = scores
colliding_grasps = None
if args.filter_collisions:
print("Applying collision filtering...")
collision_start = time.time()
# Downsample scene point cloud for faster collision checking
if len(xyz_scene_centered) > args.max_scene_points:
indices = np.random.choice(
len(xyz_scene_centered), args.max_scene_points, replace=False
)
xyz_scene_downsampled = xyz_scene_centered[indices]
print(
f"Downsampled scene point cloud from {len(xyz_scene_centered)} to {len(xyz_scene_downsampled)} points"
)
else:
xyz_scene_downsampled = xyz_scene_centered
print(
f"Scene point cloud has {len(xyz_scene_centered)} points (no downsampling needed)"
)
# Filter collision grasps
collision_free_mask = filter_colliding_grasps(
scene_pc=xyz_scene_downsampled,
grasp_poses=grasps_centered,
gripper_collision_mesh=gripper_collision_mesh,
collision_threshold=args.collision_threshold,
)
collision_time = time.time() - collision_start
print(f"Collision detection took: {collision_time:.2f} seconds")
# Separate collision-free and colliding grasps
collision_free_grasps = grasps_centered[collision_free_mask]
colliding_grasps = grasps_centered[~collision_free_mask]
collision_free_scores = scores[collision_free_mask]
print(
f"Found {len(collision_free_grasps)}/{len(grasps_centered)} collision-free grasps"
)
# Visualize collision-free grasps
grasps_to_visualize = (
collision_free_grasps if args.filter_collisions else grasps_centered
)
scores_to_use = collision_free_scores
for j, grasp in enumerate(grasps_to_visualize):
color = scores_to_use[j] if not args.filter_collisions else [0, 185, 0]
visualize_grasp(
vis,
f"grasps/{j:03d}/grasp",
tra.inverse_matrix(T_center) @ grasp,
color=color,
gripper_name=gripper_name,
linewidth=1.5,
)
# Visualize colliding grasps in red if collision filtering is enabled
if args.filter_collisions and colliding_grasps is not None:
for j, grasp in enumerate(
colliding_grasps[:20]
): # Limit to first 20 for clarity
visualize_grasp(
vis,
f"colliding/{j:03d}/grasp",
tra.inverse_matrix(T_center) @ grasp,
color=[255, 0, 0],
gripper_name=gripper_name,
linewidth=0.4,
)
if len(colliding_grasps) > 0:
print(
f"Showing {min(20, len(colliding_grasps))} colliding grasps in red"
)
# # NOTE: Purely for debugging purposes. Steps through each grasp and visualizes the gripper collision mesh at the grasp location.
# # Visualize gripper collision mesh at grasp locations
# if args.filter_collisions and gripper_collision_mesh is not None:
# print("\nStarting gripper collision mesh visualization...")
# # Combine all grasps with their colors and labels
# all_debug_grasps = []
# # Add collision-free grasps (green)
# for i, grasp in enumerate(collision_free_grasps):
# all_debug_grasps.append({
# 'pose': tra.inverse_matrix(T_center) @ grasp,
# 'color': [0, 255, 0], # Green for collision-free
# 'label': f"collision_free_{i:03d}",
# 'type': 'collision-free'
# })
# if i > 5:
# break
# # Add colliding grasps (red)
# if colliding_grasps is not None:
# for i, grasp in enumerate(colliding_grasps):
# all_debug_grasps.append({
# 'pose': tra.inverse_matrix(T_center) @ grasp,
# 'color': [255, 0, 0], # Red for colliding
# 'label': f"colliding_{i:03d}",
# 'type': 'colliding'
# })
# if i > 5:
# break
# print(f"Total grasps to visualize: {len(all_debug_grasps)}")
# # Step through each grasp with tqdm progress bar
# for grasp_info in tqdm(all_debug_grasps, desc="Visualizing gripper meshes"):
# # Clear previous gripper mesh (if it exists)
# try:
# vis["gripper_mesh"].delete()
# except:
# pass # Gripper mesh doesn't exist yet
# # Visualize gripper mesh at grasp pose using transform parameter
# visualize_mesh(
# vis,
# "gripper_mesh",
# gripper_collision_mesh,
# color=grasp_info['color'],
# transform=grasp_info['pose']
# )
# print(f"\nShowing {grasp_info['type']} grasp: {grasp_info['label']}")
# user_input = input("Press Enter for next grasp, 'q' to quit gripper visualization, 's' to skip to next scene: ")
# if user_input.lower() == 'q':
# try:
# vis["gripper_mesh"].delete()
# except:
# pass
# break
# elif user_input.lower() == 's':
# try:
# vis["gripper_mesh"].delete()
# except:
# pass
# break
# # Clean up gripper mesh
# try:
# vis["gripper_mesh"].delete()
# except:
# pass
# print("Gripper mesh visualization completed.")
input("Press Enter to continue to next scene...")
else:
print("No grasps found! Skipping to next scene...")
================================================
FILE: scripts/download_objects.py
================================================
# Copyright (c) 2025, 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.
#
"""
Download specific Objaverse meshes given their UUIDs.
Installation:
pip install trimesh==4.5.3 objaverse==0.1.7 meshcat==0.0.12 webdataset==0.2.111
Usage:
# Download from a single text file:
python download_objects.py --uuid_list /path_to_dataset/splits/franka_panda/train.txt --output_dir /tmp/objs --simplify
# Download from all text files in a directory:
python download_objects.py --uuid_list /path_to_dataset/splits/franka_panda/ --output_dir /tmp/objs --simplify
"""
import argparse
import json
import os
import shutil
import glob
import time
import subprocess
from urllib.error import HTTPError
from multiprocessing import Pool, cpu_count
try:
from objaverse import load_objects
except ImportError:
print(
"Objaverse not found, please see the installation instructions in the top of the file"
)
exit(1)
from pathlib import Path
MANIFOLD_PATH = "manifold"
SIMPLIFY_PATH = "simplify"
def load_uuid_list(uuid_list_path: str) -> list[str]:
"""
Load a list of UUIDs from a text file.
Args:
uuid_list_path (str): Path to the UUID list file
Returns:
list[str]: List of UUIDs
"""
if not os.path.exists(uuid_list_path):
raise FileNotFoundError(f"UUID list file not found: {uuid_list_path}")
if not uuid_list_path.endswith(".txt"):
raise ValueError(
f"Unsupported file format: {uuid_list_path}. Only .txt files are supported."
)
uuids = []
with open(uuid_list_path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line: # Skip empty lines
uuids.append(line)
return uuids
def simplify_mesh(input_path: str, output_path: str) -> bool:
"""
Process a mesh through manifold and simplify commands.
Args:
input_path (str): Path to input mesh file
output_path (str): Path to save simplified mesh
Returns:
bool: True if simplification was successful, False otherwise
"""
# Create output directory if it doesn't exist
os.makedirs(os.path.dirname(output_path), exist_ok=True)
# Waterproof the object
fname_watertight = output_path.replace(".obj", "_watertight.obj")
completed = subprocess.run(
["timeout", "-sKILL", "30", MANIFOLD_PATH, input_path, fname_watertight],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
)
if completed.returncode != 0:
print(f"Skipping object (manifold failed): {input_path}")
return False
# Simplify the object
completed = subprocess.run(
[
"timeout",
"-sKILL",
"30",
SIMPLIFY_PATH,
"-i",
fname_watertight,
"-o",
output_path,
"-m",
"-r",
"0.02",
],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
)
# Clean up temporary watertight file
if os.path.exists(fname_watertight):
os.remove(fname_watertight)
if completed.returncode != 0:
print(f"Skipping object (simplify failed): {fname_watertight}")
return False
return True
def download_with_retry(uuids: list[str], max_retries: int = 5, batch_size: int = 10):
"""
Download objects with retry logic and batching.
Args:
uuids (list[str]): List of UUIDs to download
max_retries (int): Maximum number of retry attempts
batch_size (int): Number of objects to download in each batch
Returns:
dict: Dictionary mapping UUIDs to object paths
"""
all_objects = {}
total_batches = (len(uuids) + batch_size - 1) // batch_size
for i in range(0, len(uuids), batch_size):
batch = uuids[i : i + batch_size]
batch_num = i // batch_size + 1
print(f"\nProcessing batch {batch_num}/{total_batches} ({len(batch)} objects)")
for retry in range(max_retries):
try:
# Add delay between batches to avoid rate limiting
if retry > 0:
delay = 2**retry # Exponential backoff
print(f"Retry {retry}/{max_retries} after {delay} seconds...")
time.sleep(delay)
batch_objects = load_objects(uids=batch)
all_objects.update(batch_objects)
break
except HTTPError as e:
if e.code == 429: # Too Many Requests
if retry == max_retries - 1:
print(f"Failed to download batch after {max_retries} retries")
raise
continue
raise
except Exception as e:
if retry == max_retries - 1:
print(f"Error downloading batch: {e}")
raise
continue
return all_objects
def process_single_object(
obj_path: str, output_dir: str, simplify: bool = False
) -> tuple[bool, str, str | None]:
"""
Process a single object: copy to destination, convert if needed, and simplify if requested.
Args:
obj_path (str): Path to the source object file
output_dir (str): Directory where the object should be saved
simplify (bool): Whether to create a simplified version of the mesh
Returns:
tuple[bool, str, str | None]: (success, dest_path, simplified_path)
- success: True if processing succeeded, False otherwise
- dest_path: Path to the final destination file
- simplified_path: Path to simplified file (if simplify=True and successful), None otherwise
"""
try:
# Create destination path
dest_path = os.path.join(output_dir, os.path.basename(obj_path))
shutil.copy2(obj_path, dest_path)
print(f"Successfully downloaded and saved object, saved to {dest_path}")
if dest_path.endswith(".glb"):
import trimesh
dest_path_obj = dest_path.replace(".glb", ".obj")
print(f"Converting glb to obj: from {dest_path} to {dest_path_obj}")
obj_mesh = trimesh.load(obj_path)
obj_mesh.export(dest_path_obj)
dest_path = dest_path_obj
# Process mesh if simplify is enabled
simplified_path = None
if simplify:
simplified_path = os.path.join(
output_dir, "simplified", os.path.basename(dest_path)
)
if simplify_mesh(dest_path, simplified_path):
print(f"Successfully simplified object, saved to {simplified_path}")
else:
simplified_path = None
return True, dest_path, simplified_path
except Exception as e:
print(f"Error processing object: {e}")
return False, "", None
def process_single_object_with_uuid(
uuid_obj_tuple: tuple[str, str], output_dir: str, simplify: bool = False
) -> tuple[str, bool, str, str | None]:
"""
Wrapper function for multiprocessing that includes the UUID.
Args:
uuid_obj_tuple (tuple[str, str]): Tuple of (uuid, obj_path)
output_dir (str): Directory where the object should be saved
simplify (bool): Whether to create a simplified version of the mesh
Returns:
tuple[str, bool, str, str | None]: (uuid, success, dest_path, simplified_path)
"""
uuid, obj_path = uuid_obj_tuple
if obj_path is None:
print(f"Failed to download object {uuid}")
return uuid, False, "", None
success, dest_path, simplified_path = process_single_object(
obj_path, output_dir, simplify
)
return uuid, success, dest_path, simplified_path
def download_objaverse_meshes(
uuids: list[str], output_dir: str, simplify: bool = False, unused_cpu_count: int = 2
):
"""
Download specific Objaverse meshes given their UUIDs.
Args:
uuids (list[str]): List of UUIDs to download
output_dir (str): Directory where meshes will be saved
simplify (bool): Whether to simplify the downloaded meshes
unused_cpu_count (int): Number of CPUs to leave unused for multiprocessing
"""
# Create output directory if it doesn't exist
os.makedirs(output_dir, exist_ok=True)
# Create simplified directory if needed
if simplify:
simplified_dir = os.path.join(output_dir, "simplified")
os.makedirs(simplified_dir, exist_ok=True)
# Load existing mapping if it exists
map_uuid_to_path = {}
mapping_file = os.path.join(output_dir, "map_uuid_to_path.json")
if os.path.exists(mapping_file):
try:
map_uuid_to_path = json.load(open(mapping_file))
except Exception as e:
print(f"Error loading existing mapping: {e}")
# Initialize simplified mapping and load existing if needed
if simplify:
map_uuid_to_path_simplified = {}
mapping_file_simplified = os.path.join(
output_dir, "map_uuid_to_path_simplified.json"
)
if os.path.exists(mapping_file_simplified):
try:
map_uuid_to_path_simplified = json.load(open(mapping_file_simplified))
except Exception as e:
print(f"Error loading existing simplified mapping: {e}")
# Filter out UUIDs that are already downloaded
uuids_to_download = []
existing_count = 0
for uuid in uuids:
# Check if any file in output_dir contains the UUID
existing_files = glob.glob(os.path.join(output_dir, f"*{uuid}.obj*"))
if simplify:
existing_files = glob.glob(os.path.join(simplified_dir, f"*{uuid}.obj*"))
if existing_files:
assert (
len(existing_files) == 1
), f"Multiple files found for UUID {uuid}: {existing_files}"
assert (
existing_files[0].find(uuid) >= 0
), f"UUID {uuid} not found in {existing_files[0]}"
existing_count += 1
# Update mapping with existing file
map_uuid_to_path[uuid] = os.path.basename(existing_files[0])
# Check for existing simplified file
if simplify:
simplified_files = glob.glob(os.path.join(simplified_dir, f"*{uuid}*"))
if simplified_files:
map_uuid_to_path_simplified[uuid] = os.path.basename(
simplified_files[0]
)
continue
uuids_to_download.append(uuid)
if existing_count > 0:
print(f"Found {existing_count} existing objects")
# Update mapping files with all existing objects
json.dump(map_uuid_to_path, open(mapping_file, "w"))
if simplify:
json.dump(map_uuid_to_path_simplified, open(mapping_file_simplified, "w"))
if not uuids_to_download:
print("All objects are already downloaded!")
return
print(f"Found {len(uuids_to_download)} new UUIDs to download")
try:
# Download objects using Objaverse with retry logic
objects = download_with_retry(uuids_to_download)
except Exception as e:
print(f"Error downloading objects: {e}")
return
# Prepare data for multiprocessing
uuid_obj_pairs = [(uuid, objects[uuid]) for uuid in uuids_to_download]
# Determine number of processes to use
num_processes = max(1, cpu_count() - unused_cpu_count)
if num_processes <= 1:
# Use sequential processing if not enough CPUs
print("Using sequential processing (not enough CPUs available)")
results = []
for uuid, obj_path in uuid_obj_pairs:
result = process_single_object_with_uuid(
(uuid, obj_path), output_dir, simplify
)
results.append(result)
# Clean up the original file
if obj_path and os.path.exists(obj_path):
os.remove(obj_path)
else:
# Use multiprocessing
print(f"Using multiprocessing with {num_processes} processes")
with Pool(num_processes) as p:
# Create a partial function with fixed arguments
from functools import partial
process_func = partial(
process_single_object_with_uuid,
output_dir=output_dir,
simplify=simplify,
)
results = list(p.imap_unordered(process_func, uuid_obj_pairs))
# Clean up original files after processing
for uuid, obj_path in uuid_obj_pairs:
if obj_path and os.path.exists(obj_path):
os.remove(obj_path)
# Process results and update mappings
for uuid, success, dest_path, simplified_path in results:
if success:
# Update simplified mapping if simplification was successful
if simplify and simplified_path:
map_uuid_to_path_simplified[uuid] = os.path.basename(simplified_path)
# Store only the base name in the mapping
map_uuid_to_path[uuid] = os.path.basename(dest_path)
else:
print(f"Failed to process object {uuid}")
# Update both mapping files
json.dump(map_uuid_to_path, open(mapping_file, "w"))
if simplify:
json.dump(map_uuid_to_path_simplified, open(mapping_file_simplified, "w"))
print("Download complete!")
def process_text_file(
text_path: str, output_dir: str, simplify: bool = False, unused_cpu_count: int = 2
):
"""
Process a single text file containing UUIDs.
Args:
text_path (str): Path to text file containing UUIDs
output_dir (str): Directory where meshes will be saved
simplify (bool): Whether to simplify downloaded meshes
unused_cpu_count (int): Number of CPUs to leave unused for multiprocessing
"""
print(f"\nProcessing file: {text_path}")
uuid_list = load_uuid_list(text_path)
download_objaverse_meshes(uuid_list, output_dir, simplify, unused_cpu_count)
def process_directory(
dir_path: str, output_dir: str, simplify: bool = False, unused_cpu_count: int = 2
):
"""
Process all text files in a directory.
Args:
dir_path (str): Path to directory containing text files
output_dir (str): Directory where meshes will be saved
simplify (bool): Whether to simplify downloaded meshes
unused_cpu_count (int): Number of CPUs to leave unused for multiprocessing
"""
# Find all text files in the directory
text_files = glob.glob(os.path.join(dir_path, "*.txt"))
if not text_files:
print(f"No text files found in directory: {dir_path}")
return
print(f"Found {len(text_files)} text files in directory: {dir_path}")
# Combine all UUIDs from all text files
all_uuids = []
for text_file in text_files:
print(f"Loading UUIDs from: {text_file}")
uuids = load_uuid_list(text_file)
all_uuids.extend(uuids)
print(f"Loaded {len(uuids)} UUIDs from {text_file}")
# Remove duplicates while preserving order
unique_uuids = list(dict.fromkeys(all_uuids))
print(f"Total unique UUIDs across all files: {len(unique_uuids)}")
# Download all objects
download_objaverse_meshes(unique_uuids, output_dir, simplify, unused_cpu_count)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--uuid_list",
type=str,
help="Path to UUID list text file or directory containing text files",
)
parser.add_argument("--output_dir", type=str, help="Path to output directory")
parser.add_argument(
"--simplify", action="store_true", help="Whether to simplify downloaded meshes"
)
parser.add_argument(
"--unused_cpu_count",
type=int,
default=2,
help="Number of CPUs to leave unused for multiprocessing",
)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
# Check if input is a directory or file
if os.path.isdir(args.uuid_list):
process_directory(
args.uuid_list, args.output_dir, args.simplify, args.unused_cpu_count
)
else:
process_text_file(
args.uuid_list, args.output_dir, args.simplify, args.unused_cpu_count
)
================================================
FILE: scripts/inference_graspgen.py
================================================
# Copyright (c) 2025, 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.
#
# Author: Adithya Murali
"""
Inference script for GraspGen.
"""
import os
from functools import partial
from pathlib import Path
from time import time
import h5py
import hydra
import numpy as np
import torch
import torch.multiprocessing as mp
import trimesh.transformations as tra
from inference_m2t2 import load_scene, record_worker
from omegaconf import DictConfig
from scene_synthesizer.assets import Asset, BoxAsset, TrimeshAsset
from torch_cluster import fps
from grasp_gen.dataset.eval_utils import (
check_collision,
get_logger,
get_timestamp,
log_worker,
write_info,
write_to_h5,
)
from grasp_gen.utils.meshcat_utils import (
create_visualizer,
get_color_from_score,
visualize_grasp,
visualize_mesh,
visualize_pointcloud,
)
from grasp_gen.robot import get_gripper_info
from grasp_gen.utils.train_utils import get_data_loader, to_cpu, to_gpu
from grasp_gen.dataset.webdataset_utils import GraspWebDatasetReader, is_webdataset
def record_grasps_diffusion(log, cfg, gripper, inputs, output_file=None):
args = cfg.eval
if args.debug and output_file is None:
from grasp_gen.utils.meshcat_utils import create_visualizer, visualize_mesh
vis = create_visualizer()
start = time()
scene = load_scene(inputs["scene_info"])
assets = inputs["scene_info"].pop("assets")
scales = inputs["scene_info"].pop("scales")
poses = inputs["scene_info"].pop("poses")
objects = {}
if "names" not in inputs:
inputs["names"] = ["obj0"]
for i, (name, asset, scale, pose) in enumerate(
zip(inputs["names"], assets, scales, poses)
):
if asset.find("/lustre") >= 0:
asset_abs = os.path.join(cfg.data.object_root_dir, asset)
mesh_asset = Asset(f"{asset}", scale=scale)
scene.add_object(asset=mesh_asset, obj_id=name, transform=pose)
objects[name] = {"asset": asset, "scale": scale, "pose": pose}
outputs = {"scene_info": inputs["scene_info"], "objects": objects}
scene_mesh = scene.scene.dump(concatenate=True)
num_grasps = inputs["grasps_pred"].shape[0]
all_grasps = inputs["grasps_pred"].cpu().numpy()
grasps_gt = inputs["gt_grasps"]
if type(grasps_gt) == torch.tensor:
grasps_gt = inputs["gt_grasps"].cpu().numpy()
if "labels" in inputs:
labels = inputs["labels"].cpu().numpy()
label_mask = np.where(labels.flatten())[0]
grasps_gt = grasps_gt[label_mask]
if "confidence" in inputs:
all_conf = inputs["confidence"]
if len(all_conf.shape) == 2:
all_conf = all_conf.squeeze(1)
else:
all_conf = np.ones(num_grasps)
if "likelihood" in inputs:
all_likelihood = inputs["likelihood"]
if len(all_likelihood.shape) == 2:
all_likelihood = all_likelihood.squeeze(1)
all_grasps = inputs["cam_pose"] @ all_grasps
grasp_mask = all_conf >= 0
if (
cfg.data.gripper_name.find("suction") >= 0
): # The mesh is not relevant for this yet, there is a bug?
collision = np.zeros(all_grasps.shape[0]) # Do not check for collisions
else:
collision = check_collision(scene_mesh, gripper, all_grasps)
# from IPython import embed; embed()
# from grasp_gen.utils.meshcat_utils import visualize_mesh, create_visualizer, visualize_grasp
# import glob
# import os
# import json
# import trimesh
# import numpy as np
# vis = create_visualizer()
# vis.delete()
# visualize_mesh(vis, 'scene_mesh', scene_mesh, transform=np.eye(4), color=[128, 128, 128])
# visualize_mesh(vis, 'gripper_mesh', gripper, transform=np.eye(4), color=[128, 128, 128])
# for i, g in enumerate(all_grasps):
# visualize_mesh(vis, 'gripper_mesh', gripper, transform=g, color=[128, 128, 128])
# input()
# print(f"Num of colliding grasps {collision.sum()}")
collision_rate = collision.mean() if num_grasps > 0 else np.nan
all_col = np.zeros((num_grasps,)).astype("bool")
all_col[grasp_mask] = collision
assert len(poses) == 1
import trimesh.transformations as tra
T_move_back_to_obj_frame = tra.inverse_matrix(poses[0])
T_move_from_obj_frame_to_origin = tra.inverse_matrix(T_move_back_to_obj_frame)
grasps_output, num_grasps = {}, []
num_grasps.append(all_grasps.shape[0])
grasps_output["obj0"] = {
"pred_grasps": T_move_back_to_obj_frame @ all_grasps,
"confidence": all_conf,
"likelihood": all_likelihood,
"collision": all_col,
"gt_grasps": T_move_back_to_obj_frame @ inputs["cam_pose"] @ grasps_gt,
}
log.info(
f"Scene {inputs['scene']} Collision rate {collision_rate} "
f"Total {sum(num_grasps)} Average {np.mean(num_grasps)} grasps"
)
new_grasps = []
from copy import deepcopy as copy
for g in all_grasps:
new_grasp = copy(np.array(g.tolist()))
new_grasp[:3, 3] += T_move_back_to_obj_frame[:3, 3]
new_grasps.append(new_grasp)
new_grasps = np.array(new_grasps)
grasps_output["obj0"]["pred_grasps"] = new_grasps
gripper_name = cfg.data.gripper_name
from grasp_gen.robot import get_gripper_info
gripper_info = get_gripper_info(gripper_name)
gripper_mesh = gripper_info.collision_mesh
if args.debug and output_file is None:
if "xyz" in inputs:
pc = inputs["xyz"][0].reshape(-1, 3).cpu().numpy()
else:
pc = inputs["points"][0].reshape(-1, 3).cpu().numpy()
import trimesh.transformations as tra
pc_color = inputs["rgb"].reshape(-1, 3).float().cpu().numpy()
grasps_pred = grasps_output["obj0"]["pred_grasps"]
grasps_gt = grasps_output["obj0"]["gt_grasps"]
# grasps_pred = np.array([T_move_from_obj_frame_to_origin @ g for g in grasps_pred])
# grasps_gt = np.array([T_move_from_obj_frame_to_origin @ g for g in grasps_gt])
grasps_per_iteration = inputs["grasps_per_iteration"].cpu().numpy()
# NOTE: For some reason, grasps_pred tensor is not working, so all_grasps is used below
# Plotting args
dual_plot = False
step_diffusion_iterations = False
if dual_plot:
assert not step_diffusion_iterations
plot_reverse = True
plot_thresholded = True
plot_mesh = True
use_likelihood_as_score = False
if use_likelihood_as_score:
scores = all_likelihood.cpu().numpy()
score_range = scores.max() - scores.min()
scores = (scores - scores.min()) / score_range
scores = get_color_from_score(scores, use_255_scale=True)
else:
scores = get_color_from_score(all_conf.cpu().numpy(), use_255_scale=True)
print(f"Confidence, max: {all_conf.max()}, min {all_conf.min()}")
print(f"Likelihood, max: {all_likelihood.max()}, min {all_likelihood.min()}")
if dual_plot:
delta = 0.75
# T_gt_shift = tra.translation_matrix([0, 0, delta]) @ T_move_back_to_obj_frame
# T_pred_shift = tra.translation_matrix([0, 0, 0.00]) @ T_move_back_to_obj_frame
T_gt_shift = (
tra.translation_matrix([0, 0, delta]) @ T_move_from_obj_frame_to_origin
)
T_pred_shift = (
tra.translation_matrix([0, 0, 0.00]) @ T_move_from_obj_frame_to_origin
)
# visualize_pointcloud(
# vis, 'pc_gt', tra.transform_points(pc, T_gt_shift), [169, 169, 169], size=0.008
# )
# visualize_pointcloud(
# vis, 'pc_pred', tra.transform_points(pc, T_pred_shift), [169, 169, 169], size=0.008
# )
visualize_mesh(
vis,
"mesh_gt",
scene_mesh,
color=[169, 169, 169],
transform=tra.translation_matrix([0, 0, delta]),
)
visualize_mesh(
vis,
"mesh_pred",
scene_mesh,
color=[169, 169, 169],
transform=tra.translation_matrix([0, 0, 0.00]),
)
# visualize_mesh(vis, "mesh_gt_thresh", scene_mesh, color=[169, 169, 169], transform=tra.translation_matrix([0, 0, -delta]))
for j, grasp in enumerate(grasps_gt):
visualize_grasp(
vis,
f"gt/grasp_{j:03d}",
T_gt_shift @ grasp,
[0, 150, 40],
gripper_name=cfg.data.gripper_name,
linewidth=0.5,
)
if plot_mesh:
visualize_mesh(
vis,
f"meshes/gt/grasp_{j:03d}",
gripper_mesh,
color=[0, 255, 0],
transform=T_gt_shift @ grasp.astype(np.float32),
)
if plot_thresholded:
threshold = cfg.eval.mask_thresh
print(f"Thresholding grasps at {threshold}")
mask_thresh = all_conf.cpu().numpy() > threshold
grasps_thresholded = all_grasps[mask_thresh]
for j, grasp in enumerate(grasps_thresholded):
visualize_grasp(
vis,
f"thresh/grasp_{j:03d}",
grasp,
[0, 150, 250],
gripper_name=cfg.data.gripper_name,
linewidth=0.8,
)
if plot_mesh:
visualize_mesh(
vis,
f"meshes/pred/grasp_{j:03d}",
gripper_mesh,
color=[0, 150, 250],
transform=grasp.astype(np.float32),
)
else:
# NOTE: For some reason, grasps_pred tensor is not working, so all_grasps is used below
for j, grasp in enumerate(all_grasps):
visualize_grasp(
vis,
f"pred/grasp_{j:03d}",
grasp,
scores[j],
gripper_name=cfg.data.gripper_name,
linewidth=0.2,
)
if plot_mesh:
visualize_mesh(
vis,
f"meshes/pred/grasp_{j:03d}",
gripper_mesh,
color=scores[j],
transform=grasp.astype(np.float32),
)
else:
if plot_thresholded:
threshold = cfg.eval.mask_thresh
if all_conf.cpu().numpy().max() <= threshold:
threshold = max(0, all_conf.cpu().numpy().max() - 0.10)
mask_thresh = all_conf.cpu().numpy() > threshold
grasps_visualized = all_grasps[mask_thresh]
scores_visualized = scores[mask_thresh]
print(
f"Thresholding grasps at {threshold}. {grasps_visualized.shape[0]}/{all_grasps.shape[0]} grasps remaining to visualize"
)
for j, grasp in enumerate(grasps_visualized):
visualize_grasp(
vis,
f"pred_thresholded/grasp_{j:03d}",
grasp,
color=[0, 150, 250],
gripper_name=cfg.data.gripper_name,
linewidth=0.2,
)
if j < 10:
if plot_mesh:
visualize_mesh(
vis,
f"pred_thresholded_meshes/grasp_{j:03d}",
gripper_mesh,
color=[0, 40, 150],
transform=grasp,
)
visualize_mesh(vis, "scene_mesh", scene_mesh, color=[192, 192, 192])
# if not step_diffusion_iterations:
# visualize_pointcloud(
# vis, 'pc', pc, [245, 66, 90], size=0.008
# )
for j, grasp in enumerate(all_grasps):
visualize_grasp(
vis,
f"pred/grasp_{j:03d}",
grasp,
scores[j],
gripper_name=cfg.data.gripper_name,
linewidth=0.2,
)
if j < 10:
if plot_mesh:
visualize_mesh(
vis,
f"pred_meshes/grasp_{j:03d}",
gripper_mesh,
color=[0, 40, 150],
transform=grasp,
)
for j, grasp in enumerate(grasps_gt):
visualize_grasp(
vis,
f"gt/grasp_{j:03d}",
T_move_from_obj_frame_to_origin @ grasp,
[0, 150, 40],
gripper_name=cfg.data.gripper_name,
linewidth=0.5,
)
if step_diffusion_iterations:
timesteps = list(range(len(grasps_per_iteration)))
if plot_reverse:
timesteps.reverse()
for t in timesteps:
print(t)
grasps_t = grasps_per_iteration[t]
for j, g in enumerate(grasps_t):
if j < 10:
visualize_grasp(
vis,
f"t/grasp_{j:03d}",
g,
[200, 0, 0],
gripper_name=cfg.data.gripper_name,
linewidth=0.2,
)
visualize_mesh(
vis,
f"meshes_t/grasp_{j:03d}",
gripper_mesh,
color=[0, 150, 250],
transform=g.astype(np.float32),
)
input()
input()
print(f"[DEBUG] output_file: {output_file}")
if output_file is not None:
key_id = inputs["scene"]
saved_data_dict = {}
# gripper_info = get_gripper_info(cfg.data.gripper_name)
from grasp_gen.dataset.dataset import load_object_grasp_data
from grasp_gen.dataset.dataset_utils import GraspJsonDatasetReader
from grasp_gen.dataset.webdataset_utils import (
GraspWebDatasetReader,
is_webdataset,
)
# Initialize the appropriate grasp dataset reader
if is_webdataset(cfg.data.grasp_root_dir):
grasp_dataset_reader = GraspWebDatasetReader(cfg.data.grasp_root_dir)
else:
grasp_dataset_reader = GraspJsonDatasetReader(cfg.data.grasp_root_dir)
error_code, object_grasp_data = load_object_grasp_data(
key_id,
cfg.data.object_root_dir,
cfg.data.grasp_root_dir,
cfg.data.dataset_version,
load_discriminator_dataset=False,
gripper_info=gripper_info,
grasp_dataset_reader=grasp_dataset_reader,
)
if object_grasp_data is not None:
if cfg.data.gripper_name == "franka_panda":
asset_path_rel = "/".join(
object_grasp_data.object_asset_path.split("/")[-3:]
)
if cfg.data.gripper_name == "intrinsic_suction":
asset_path_rel = "/".join(
object_grasp_data.object_asset_path.split("/")[-2:]
)
else:
asset_path_rel = "/".join(
object_grasp_data.object_asset_path.split("/")[-3:]
)
print(f"[DEBUG] asset_path_rel: {asset_path_rel}")
if cfg.data.gripper_name == "intrinsic_suction":
offset_transform = tra.inverse_matrix(gripper_info.offset_transform)
grasps_output["obj0"]["pred_grasps"] = np.array(
[g @ offset_transform for g in grasps_output["obj0"]["pred_grasps"]]
)
grasps_output["obj0"]["gt_grasps"] = np.array(
[g @ offset_transform for g in grasps_output["obj0"]["gt_grasps"]]
)
saved_data_dict["asset_path"] = asset_path_rel
saved_data_dict["asset_scale"] = object_grasp_data.object_scale
saved_data_dict.update(grasps_output["obj0"])
print(
f"Pred {saved_data_dict['pred_grasps'].shape}, Gt {saved_data_dict['gt_grasps'].shape} grasp number"
)
grp = output_file.create_group(key_id)
start = time()
write_info(grp, saved_data_dict)
end = time()
print(f"Writing scene {key_id} data took", end - start, "s")
time_taken = time() - start
log.info(
f"Scene {inputs['scene']} took {round(time_taken,2)} s, saved to {output_file}"
)
@hydra.main(config_path=".", config_name="config", version_base="1.3")
def main(cfg: DictConfig) -> None:
# scenes = np.arange(cfg.eval.first_scene, cfg.eval.last_scene)
# For SIMPLER dataset, just iterate over scenes
scenes = None
sampler, loader = get_data_loader(
cfg.eval,
cfg.data,
cfg.eval.split,
scenes,
use_ddp=False,
training=False,
inference=True,
)
# model = foundation_grasp.from_config(cfg.foundation_grasp)
from grasp_gen.models.grasp_gen import GraspGen, GraspGenGenerator
if cfg.eval.model_name == "diffusion":
model = GraspGenGenerator.from_config(cfg.diffusion)
elif cfg.eval.model_name == "diffusion-discriminator":
model = GraspGen.from_config(cfg.diffusion, cfg.discriminator)
else:
raise NotImplementedError(f"Model name not implemented {cfg.eval.model_name}")
if cfg.eval.model_name == "diffusion-discriminator":
model.load_state_dict(cfg.eval.checkpoint, cfg.discriminator.checkpoint)
else:
ckpt = torch.load(cfg.eval.checkpoint, map_location="cpu")
model.load_state_dict(ckpt)
model = model.cuda().eval()
# gripper = PandaGripper().hand
gripper_info = get_gripper_info(cfg.data.gripper_name)
gripper = gripper_info.collision_mesh
if cfg.eval.output_dir is not None:
os.makedirs(cfg.eval.output_dir, exist_ok=True)
mp.set_start_method("spawn", force=True)
log_queue = mp.Queue()
log_proc = mp.Process(target=log_worker, args=(log_queue,))
log_proc.start()
log = get_logger("main", log_queue)
output_file = None
h5_handle = None
if cfg.eval.output_dir is not None:
os.makedirs(cfg.eval.output_dir, exist_ok=True)
h5_file_name = (
f"{cfg.eval.exp_name}_{cfg.data.gripper_name}_{cfg.eval.split}.h5"
)
output_file_path = os.path.join(cfg.eval.output_dir, h5_file_name)
log.info(f"Saving to {output_file_path}")
os.system(f"rm {output_file_path}") # For safety
output_file = h5py.File(output_file_path, "a")
misc_data = {
"gripper_name": cfg.data.gripper_name,
"dataset_name": cfg.data.dataset_name,
"dataset_version": cfg.data.dataset_version,
"model": cfg.eval.exp_name,
}
grp = output_file.create_group("misc")
write_info(grp, misc_data)
h5_handle = output_file.create_group("objects")
# Evaluate all grasps
log.info(f"Evaluating all grasps without thresholding")
cfg.eval.object_thresh = -1.0
cfg.eval.mask_thresh = -1.0
record_fn = record_grasps_diffusion
if not cfg.eval.debug:
queue = mp.JoinableQueue()
procs = [
mp.Process(
target=record_worker,
args=(i, queue, log_queue, record_fn, gripper, cfg, h5_handle),
)
for i in range(cfg.eval.num_procs)
]
for p in procs:
p.start()
data_time, infer_time, record_time, num_scenes = 0, 0, 0, 0
start = time()
for i, data in enumerate(loader):
if data is None:
continue
to_gpu(data)
num_scenes += len(data["scene"])
data_time += time() - start
start = time()
with torch.no_grad():
outputs, _, stats = model.infer(data, return_metrics=True)
print(
f"Stats, L2 error:{stats['error_trans_l2'].item()} recall: {stats['recall'].item()} phi3 {stats['error_rot_phi3'].item()} "
)
infer_time += time() - start
start = time()
to_cpu(data)
to_cpu(outputs)
for j in range(len(data["scene"])):
if cfg.eval.cam_coord:
cam_pose = data["cam_pose"][j].numpy()
else:
cam_pose = np.eye(4)
inputs = {
"scene": data["scene"][j].split("/")[-1],
"scene_info": data["scene_info"][j],
"cam_pose": cam_pose,
# 'names': data['names'][j],
# 'points': data['xyz'][j]
}
if cfg.eval.task == "pick":
inputs.update(
{
# 'obj_masks': data['instance_masks'][j].bool(),
"gt_grasps": data["grasps_ground_truth"][j],
"grasps_pred": outputs["grasps_pred"][j],
"likelihood": outputs["likelihood"][j],
"grasps_per_iteration": outputs["grasps_per_iteration"][j],
"confidence": outputs["grasp_confidence"][j],
"grasping_masks": outputs["grasping_masks"][j],
"contacts": outputs["grasp_contacts"][j],
}
)
# For GraspGenGenerator
for key in ["seg", "rgb", "xyz", "points", "bbox", "labels"]:
if key in data:
inputs.update({key: data[key][j]})
if cfg.eval.debug:
record_fn(log, cfg, gripper, inputs, h5_handle)
else:
queue.put(inputs)
record_time += time() - start
start = time()
if (i + 1) % cfg.eval.print_freq == 0:
log.info(
f"{i+1}/{len(loader)} "
f"Data time {data_time / num_scenes} "
f"Inference time {infer_time / num_scenes} "
f"Record time {record_time / num_scenes} "
f"num scenes {num_scenes}"
)
data_time, infer_time, record_time, num_scenes = 0, 0, 0, 0
if not cfg.eval.debug:
queue.join()
print("All work completed")
for _ in range(cfg.eval.num_procs * 2):
queue.put(None)
for p in procs:
p.join()
log_queue.put(None)
log_proc.join()
if __name__ == "__main__":
main()
================================================
FILE: scripts/inference_m2t2.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""
Inference script for M2T2.
"""
import os
from functools import partial
from pathlib import Path
from time import time
import h5py
import hydra
import numpy as np
import torch
import torch.multiprocessing as mp
import trimesh.transformations as tra
from h5py._hl.files import File
from omegaconf import DictConfig
from scene_synthesizer.assets import Asset, BoxAsset, TrimeshAsset
from scene_synthesizer.scene import Scene
from torch_cluster import fps
from grasp_gen.dataset.eval_utils import (
check_collision,
get_logger,
get_timestamp,
log_worker,
write_info,
write_to_h5,
)
from grasp_gen.utils.meshcat_utils import (
create_visualizer,
get_color_from_score,
visualize_grasp,
visualize_mesh,
visualize_pointcloud,
)
from grasp_gen.models.m2t2 import M2T2
from grasp_gen.utils.train_utils import get_data_loader, to_cpu, to_gpu
def load_scene(inputs):
scene = Scene()
if "table" in inputs:
table = BoxAsset(inputs["table"]["size"])
transform = tra.translation_matrix(inputs["table"]["pos"])
scene.add_object("table", table, transform)
if "robot_table" in inputs:
table = BoxAsset(inputs["robot_table"]["size"])
transform = tra.translation_matrix(inputs["robot_table"]["pos"])
scene.add_object("robot_table", table, transform)
if "robot" in inputs:
robot = Asset(
f"{Path(__file__).parent.parent}/assets/franka/franka_panda.urdf",
configuration=inputs["robot"]["config"],
)
scene.add_object("robot", robot, inputs["robot"]["pose"])
return scene
def select_grasp(contacts, confidence, args):
seed_mask = confidence > args.seed_thresh
seed_idx = torch.zeros(0).long()
if seed_mask.sum() > 0:
ratio = min(args.num_seed_grasps / seed_mask.sum(), 1.0)
seed_idx = fps(contacts[seed_mask], ratio=ratio)
# num = min(args.num_seed_grasps, seed_mask.sum())
# seed_idx = furthest_point_sample(
# contacts[seed_mask].unsqueeze(0).cuda(), num
# ).squeeze(0).cpu()
seed_idx = np.where(seed_mask)[0][seed_idx]
more_idx = confidence[~seed_mask].argsort()[::-1]
num = max(args.max_num_grasps - seed_idx.shape[0], 0)
more_idx = np.where(~seed_mask)[0][more_idx[:num]]
selected = np.zeros_like(seed_mask)
selected[np.concatenate([seed_idx, more_idx])] = True
return selected
def record_grasps(log, cfg, gripper, inputs, output_file=None):
args = cfg.eval
if args.debug and output_file is None:
vis = create_visualizer()
start = time()
scene = load_scene(inputs["scene_info"])
assets = inputs["scene_info"].pop("assets")
scales = inputs["scene_info"].pop("scales")
poses = inputs["scene_info"].pop("poses")
objects = {}
for i, (name, asset, scale, pose) in enumerate(
zip(inputs["names"], assets, scales, poses)
):
mesh_asset = Asset(f"{asset}", scale=scale)
scene.add_object(asset=mesh_asset, obj_id=name, transform=pose)
objects[name] = {"asset": asset, "scale": scale, "pose": pose}
outputs = {"scene_info": inputs["scene_info"], "objects": objects}
scene_mesh = scene.scene.dump(concatenate=True)
num_pts = inputs["points"].shape[1]
all_conf = np.zeros((num_pts,))
all_grasps = np.zeros((num_pts, 4, 4))
all_contacts = np.zeros((num_pts, 3))
proposal_ids = np.zeros((num_pts,)).astype("uint8")
for i, (mask, confidence, grasps, contacts) in enumerate(
zip(
inputs["grasping_masks"],
inputs["confidence"],
inputs["grasps"],
inputs["contacts"],
)
):
replace = confidence.numpy() > all_conf[mask.numpy()]
mask[np.where(mask)[0][~replace]] = False
all_conf[mask] = confidence[replace].numpy()
all_grasps[mask] = grasps[replace].numpy()
all_contacts[mask] = contacts[replace].numpy()
proposal_ids[mask] = np.full(mask.sum(), i)
all_grasps = inputs["cam_pose"] @ all_grasps
all_contacts = tra.transform_points(all_contacts, inputs["cam_pose"])
all_grasps[:, :3, 3] -= all_grasps[:, :3, 2] * args.retract
grasp_mask = all_conf > 0
collision = check_collision(scene_mesh, gripper, all_grasps[grasp_mask])
collision_rate = collision.mean() if collision.shape[0] > 0 else np.nan
all_col = np.zeros((num_pts,)).astype("bool")
all_col[grasp_mask] = collision
assert len(poses) == 1
T_move_back_to_obj_frame = tra.inverse_matrix(poses[0])
T_move_from_obj_frame_to_origin = tra.inverse_matrix(T_move_back_to_obj_frame)
grasps_output, num_grasps = {}, []
# for i, (name, mask, gt_grasps) in enumerate(zip(
# inputs['names'], inputs['obj_masks'], inputs['gt_grasps']
# )):
# mask = mask.numpy() & grasp_mask
# if args.seed_thresh is not None and mask.sum() > args.max_num_grasps:
# selected = select_grasp(
# inputs['points'][mask], all_conf[mask], args
# )
# mask[np.where(mask)[0][~selected]] = False
# num_grasps.append(mask.sum())
# if type(gt_grasps) == torch.tensor:
# gt_grasps = gt_grasps.numpy()
# grasps_output[name] = {
# 'pred_grasps': T_move_back_to_obj_frame @ all_grasps[mask],
# 'contacts': all_contacts[mask],
# 'confidence': all_conf[mask],
# 'collision': all_col[mask],
# 'proposal_ids': proposal_ids[mask],
# 'gt_grasps': T_move_back_to_obj_frame @ inputs['cam_pose'] @ gt_grasps
# }
gt_grasps = inputs["gt_grasps"]
for i, (name, mask) in enumerate(zip(inputs["names"], inputs["obj_masks"])):
mask = mask.numpy() & grasp_mask
if args.seed_thresh is not None and mask.sum() > args.max_num_grasps:
selected = select_grasp(inputs["points"][mask], all_conf[mask], args)
mask[np.where(mask)[0][~selected]] = False
num_grasps.append(mask.sum())
if type(gt_grasps) == torch.tensor:
gt_grasps = gt_grasps.numpy()
grasps_output[name] = {
"pred_grasps": T_move_back_to_obj_frame @ all_grasps[mask],
"contacts": all_contacts[mask],
"confidence": all_conf[mask],
"collision": all_col[mask],
"proposal_ids": proposal_ids[mask],
"gt_grasps": T_move_back_to_obj_frame @ inputs["cam_pose"] @ gt_grasps,
}
outputs["grasps"] = grasps_output
log.info(
f"Scene {inputs['scene']} Collision rate {collision_rate} "
f"Total {sum(num_grasps)} Average {np.mean(num_grasps)} grasps"
)
gripper_name = cfg.data.gripper_name
from grasp_gen.robot import get_gripper_info
gripper_info = get_gripper_info(gripper_name)
gripper_mesh = gripper_info.collision_mesh
if args.debug and output_file is None:
pc = inputs["points"].cpu().numpy()
# visualize_pointcloud(
# vis,
# f"point_cloud",
# pc,
# [245, 66, 90],
# size=0.005
# )
# visualize_pointcloud(
# vis,
# f"point_cloud",
# tra.transform_points(pc, T_move_back_to_obj_frame),
# [245, 66, 90],
# size=0.005
# )
# visualize_mesh(vis, 'scene_mesh', scene_mesh, color=[192, 192, 192], transform=T_move_back_to_obj_frame)
for name, outputs in grasps_output.items():
print(f"{name} has {outputs['pred_grasps'].shape[0]} grasps")
if outputs["pred_grasps"].shape[0] > 0:
print(
f"Confidence min {outputs['confidence'].min()} "
f"max {outputs['confidence'].max()}\n"
f"Collision rate {outputs['collision'].mean()}"
)
color = np.random.randint(0, 256, (3,))
max_grasps_visualize = 20
grasps_pred = outputs["pred_grasps"]
grasps_gt = outputs["gt_grasps"]
collision = outputs["collision"]
confidence = outputs["confidence"]
contacts = outputs["contacts"]
if len(grasps_pred) > 1:
mask = np.random.randint(0, len(grasps_pred), max_grasps_visualize)
grasps_pred = grasps_pred[mask]
collision = collision[mask]
confidence = confidence[mask]
contacts = contacts[mask]
mask = np.random.randint(0, len(grasps_gt), max_grasps_visualize)
grasps_gt = grasps_gt[mask]
dual_plot = False
if dual_plot:
T_gt_shift = tra.translation_matrix([0, 0, 0.50])
T_pred_shift = tra.translation_matrix([0, 0, 0.00])
visualize_mesh(
vis,
"mesh_gt",
scene_mesh,
color=[169, 169, 169],
transform=T_gt_shift,
)
visualize_mesh(
vis,
"mesh_pred",
scene_mesh,
color=[169, 169, 169],
transform=T_pred_shift,
)
visualize_pointcloud(
vis, f"{name}/pred/contacts", contacts, [0, 255, 0], size=0.01
)
for j, grasp in enumerate(grasps_pred):
visualize_grasp(
vis,
f"{name}/pred/grasp_{j:03d}",
T_move_from_obj_frame_to_origin @ grasp,
[0, 0, 255],
gripper_name=cfg.data.gripper_name,
linewidth=0.2,
)
for j, grasp in enumerate(grasps_gt):
visualize_grasp(
vis,
f"{name}/gt/grasp_{j:03d}",
T_gt_shift @ T_move_from_obj_frame_to_origin @ grasp,
[0, 255, 0],
gripper_name=cfg.data.gripper_name,
linewidth=0.2,
)
else:
visualize_mesh(vis, "scene_mesh", scene_mesh, color=[192, 192, 192])
visualize_pointcloud(
vis, f"{name}/pred/contacts", contacts, [0, 255, 0], size=0.01
)
for j, grasp in enumerate(grasps_pred):
visualize_grasp(
vis,
f"{name}/pred/grasp_{j:03d}",
T_move_from_obj_frame_to_origin @ grasp,
[0, 0, 255],
gripper_name=cfg.data.gripper_name,
linewidth=0.2,
)
for j, grasp in enumerate(grasps_gt):
visualize_grasp(
vis,
f"{name}/gt/grasp_{j:03d}",
T_move_from_obj_frame_to_origin @ grasp,
[0, 255, 0],
gripper_name=cfg.data.gripper_name,
linewidth=0.2,
)
collision = np.logical_not(collision)
collision = collision.astype(np.float32)
collision_colors = get_color_from_score(collision, use_255_scale=True)
collision_colors = collision_colors.astype(np.int32)
# if len(grasps_pred) > 1:
# if output_file is None:
# for j, g in enumerate(grasps_pred):
# print(j)
# visualize_mesh(
# vis, "gripper", gripper,
# transform=T_move_from_obj_frame_to_origin @ g, color=collision_colors[j].tolist()
# )
# if j == 15:
# break
# input()
input()
if output_file is not None:
key_id = inputs["scene"]
saved_data_dict = {}
from grasp_gen.dataset.dataset import load_object_grasp_data
object_grasp_data = load_object_grasp_data(
key_id,
cfg.data.object_root_dir,
cfg.data.grasp_root_dir,
cfg.data.dataset_version,
load_discriminator_dataset=False,
gripper_info=gripper_info,
)
asset_path_rel = "/".join(object_grasp_data.object_asset_path.split("/")[-3:])
saved_data_dict["asset_path"] = asset_path_rel
saved_data_dict["asset_scale"] = object_grasp_data.object_scale
saved_data_dict.update(grasps_output["obj0"])
print(
f"Pred {saved_data_dict['pred_grasps'].shape}, Gt {saved_data_dict['gt_grasps'].shape} grasp number"
)
del saved_data_dict["proposal_ids"]
grp = output_file.create_group(key_id)
start = time()
write_info(grp, saved_data_dict)
end = time()
print(f"Writing scene {key_id} data took", end - start, "s")
time_taken = time() - start
log.info(
f"Scene {inputs['scene']} took {round(time_taken,2)} s, saved to {output_file}"
)
def record_worker(rank, queue, log_queue, record_fn, gripper, args, output_file):
log = get_logger(f"Worker {rank:02d}", log_queue)
log.info("Started")
while True:
inputs = queue.get()
if inputs is None:
break
record_fn(log, args, gripper, inputs, output_file)
queue.task_done()
@hydra.main(config_path=".", config_name="config", version_base="1.3")
def main(cfg: DictConfig) -> None:
scenes = np.arange(cfg.eval.first_scene, cfg.eval.last_scene)
sampler, loader = get_data_loader(
cfg.eval,
cfg.data,
cfg.eval.split,
scenes,
use_ddp=False,
training=False,
inference=True,
)
model = M2T2.from_config(cfg.m2t2)
ckpt = torch.load(cfg.eval.checkpoint, map_location="cpu")
ckpt_mapped = {}
for key, val in ckpt["model"].items():
new_key = key.replace("backbone", "scene_encoder")
new_key = new_key.replace("transformer", "contact_decoder")
new_key = new_key.replace("contact_mlp", "grasp_mlp")
new_key = new_key.replace("con_mask_head", "grasp_mask_head")
new_key = new_key.replace(".feature_proj", ".scene_feature_proj")
ckpt_mapped[new_key] = val
model.load_state_dict(ckpt_mapped)
model = model.cuda().eval()
from grasp_gen.robot import get_gripper_info
gripper_info = get_gripper_info(cfg.data.gripper_name)
gripper = gripper_info.collision_mesh
mp.set_start_method("spawn")
log_queue = mp.Queue()
log_proc = mp.Process(target=log_worker, args=(log_queue,))
log_proc.start()
log = get_logger("main", log_queue)
output_file = None
h5_handle = None
if cfg.eval.output_dir is not None:
os.makedirs(cfg.eval.output_dir, exist_ok=True)
h5_file_name = f"{cfg.eval.exp_name}_{cfg.data.gripper_name}_{cfg.data.dataset_name}_{cfg.eval.split}.h5"
output_file_path = os.path.join(cfg.eval.output_dir, h5_file_name)
log.info(f"Saving to {output_file_path}")
os.system(f"rm {output_file_path}") # For safety
output_file = h5py.File(output_file_path, "a")
misc_data = {
"gripper_name": cfg.data.gripper_name,
"dataset_name": cfg.data.dataset_name,
"dataset_version": cfg.data.dataset_version,
"model": cfg.eval.exp_name,
}
grp = output_file.create_group("misc")
write_info(grp, misc_data)
h5_handle = output_file.create_group("objects")
# Evaluate all grasps
log.info(f"Evaluating all grasps without thresholding")
cfg.eval.object_thresh = -1.0
cfg.eval.mask_thresh = -1.0
record_fn = record_grasps
if not cfg.eval.debug:
queue = mp.JoinableQueue()
procs = [
mp.Process(
target=record_worker,
args=(i, queue, log_queue, record_fn, gripper, cfg, h5_handle),
)
for i in range(cfg.eval.num_procs)
]
for p in procs:
p.start()
data_time, infer_time, record_time, num_scenes = 0, 0, 0, 0
start = time()
for i, data in enumerate(loader):
if data is None:
continue
to_gpu(data)
num_scenes += len(data["scene"])
data_time += time() - start
start = time()
with torch.no_grad():
outputs = model.infer(data, cfg.eval)
infer_time += time() - start
start = time()
to_cpu(data)
to_cpu(outputs)
for j in range(len(data["scene"])):
if cfg.eval.cam_coord:
cam_pose = data["cam_pose"][j].numpy()
else:
cam_pose = np.eye(4)
inputs = {
"scene": data["scene"][j].split("/")[-1],
"scene_info": data["scene_info"][j],
"cam_pose": cam_pose,
"names": data["names"][j],
"points": data["points"][j],
}
inputs.update(
{
"gt_grasps": data["grasps_ground_truth"][j],
"confidence": outputs["grasp_confidence"][j],
"grasping_masks": outputs["grasping_masks"][j],
}
)
if "instance_masks" in data:
inputs.update(
{
"obj_masks": data["instance_masks"][j].bool(),
}
)
if "grasps" in outputs:
inputs.update(
{
"grasps": outputs["grasps"][j],
}
)
if "grasp_contacts" in outputs:
inputs.update(
{
"contacts": outputs["grasp_contacts"][j],
}
)
if cfg.eval.debug:
record_fn(log, cfg, gripper, inputs, h5_handle)
else:
queue.put(inputs)
record_time += time() - start
start = time()
if (i + 1) % cfg.eval.print_freq == 0:
log.info(
f"{i+1}/{len(loader)} "
f"Data time {data_time / num_scenes} "
f"Inference time {infer_time / num_scenes} "
f"Record time {record_time / num_scenes}"
)
data_time, infer_time, record_time, num_scenes = 0, 0, 0, 0
if output_file is not None:
output_file.close()
if not cfg.eval.debug:
queue.join()
print("All work completed")
for _ in range(cfg.eval.num_procs * 2):
queue.put(None)
for p in procs:
p.join()
log_queue.put(None)
log_proc.join()
if __name__ == "__main__":
main()
================================================
FILE: scripts/run_grasp_sim_omniverse.py
================================================
# Copyright (c) 2025, 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.
"""Isaac Sim script: after opening box_with_grasps_sim.usd, run this so that on
Play the grippers close and grasp the object.
Usage in Isaac Sim:
1. Open the stage: File > Open > box_with_grasps_sim.usd
2. Open Script Editor (Window > Script Editor)
3. Run this script (e.g. paste and Run, or run as standalone with isaacsim python)
4. Press Play – grippers will move to closed position after a short delay.
Requires: Isaac Sim with omni.isaac.core (bundled).
"""
import numpy as np
# Isaac Sim / Omniverse imports (available when run inside Isaac Sim)
try:
from omni.isaac.core import World
from omni.isaac.core.articulations import Articulation
from omni.isaac.core.utils.types import ArticulationAction
ISAAC_AVAILABLE = True
except ImportError:
ISAAC_AVAILABLE = False
# Delay after play (seconds) before commanding grippers to close
GRASP_CLOSE_DELAY_S = 0.5
# For Robotiq-style grippers: use lower joint limit as "closed" (fingers close when going to min).
# Set to "upper" if your gripper closes at the upper limit.
CLOSED_JOINT_LIMIT = "lower"
def _get_articulation_roots_under_world(stage):
"""Return prim paths of articulation roots under /World (Env_*/gripper)."""
roots = []
world = stage.GetPrimAtPath("/World")
if not world:
return roots
for env in world.GetChildren():
if not env.GetName().startswith("Env_"):
continue
gripper_path = env.GetPath().pathString + "/gripper"
gripper = stage.GetPrimAtPath(gripper_path)
if gripper and gripper.IsValid():
# Referenced gripper root may have ArticulationRootAPI; if not, use path anyway for Articulation()
roots.append(gripper_path)
return roots
def _get_closed_joint_positions(articulation, use_lower=True):
"""Get joint positions for 'closed' grasp from articulation limits."""
joint_names = articulation.dof_names
limits = articulation.get_joint_limits()
if limits is None or len(limits) != len(joint_names):
return None
pos = []
for (low, high) in limits:
if use_lower:
pos.append(float(low))
else:
pos.append(float(high))
return np.array(pos, dtype=np.float64)
def run_grasp_on_play():
"""Register a timeline callback so that after Play, grippers close."""
if not ISAAC_AVAILABLE:
print("run_grasp_sim_omniverse: omni.isaac.core not available. Run this script inside Isaac Sim.")
return
from omni.isaac.core.utils.stage import get_current_stage
import carb
stage = get_current_stage()
if not stage:
print("run_grasp_sim_omniverse: No stage open. Open box_with_grasps_sim.usd first.")
return
root_paths = _get_articulation_roots_under_world(stage)
if not root_paths:
print("run_grasp_sim_omniverse: No /World/Env_*/gripper articulation roots found.")
return
world = World.instance()
if world is None:
print("run_grasp_sim_omniverse: World not initialized. Press Play once, then run this script again, or use the callback below.")
return
articulations = []
for path in root_paths:
art = world.scene.get(path)
if art is None:
art = Articulation(path)
world.scene.add(art)
articulations.append(art)
# After a short delay, set all grippers to closed
def _close_grippers(dt):
for art in articulations:
if art is None:
continue
try:
closed_pos = _get_closed_joint_positions(art, use_lower=(CLOSED_JOINT_LIMIT == "lower"))
if closed_pos is not None:
art.apply_action(ArticulationAction(joint_positions=closed_pos))
except Exception as e:
carb.log_warn(f"run_grasp_sim_omniverse: {e}")
# Single-shot after delay: use a simple timer via world's update
elapsed = [0.0]
def _on_timestep(timestep):
elapsed[0] += timestep
if elapsed[0] >= GRASP_CLOSE_DELAY_S:
_close_grippers(timestep)
world.remove_timestep_callback("grasp_close")
world.add_timestep_callback("grasp_close", _on_timestep)
print("run_grasp_sim_omniverse: On Play, grippers will close after", GRASP_CLOSE_DELAY_S, "s.")
def main():
"""Entry when run as script. Assumes Isaac Sim has already loaded the stage."""
run_grasp_on_play()
if __name__ == "__main__":
main()
================================================
FILE: scripts/save_grasps_to_usd.py
================================================
# Copyright (c) 2025, 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.
"""Save predicted grasps as Xform poses into a USD file.
Each grasp is stored as a UsdGeom.Xform under /world/grasps/grasp_NNN with its
full 4x4 transform and a confidence custom attribute. The object mesh (if
present in the USD) is left untouched.
Optionally, when gripper_name is provided, adds /world/grasps_visualization
with the same poses and gripper wireframe geometry (BasisCurves) at each pose,
matching the format used in grasp_gen.utils.viser_utils.visualize_grasp.
This can also be used standalone to convert an Isaac-format YAML grasp file into
USD grasp Xforms appended to an existing USD scene.
"""
import argparse
import os
import sys
from typing import List, Optional, Tuple
import numpy as np
import trimesh.transformations as tra
from pxr import Gf, Sdf, Usd, UsdGeom, Vt
GRASPS_ROOT_PATH = "/world/grasps"
GRASPS_VIS_ROOT_PATH = "/world/grasps_visualization"
# Default radius/width for grasp wireframe curves in meters (thin lines in Omniverse).
GRASP_WIREFRAME_WIDTH_M = 0.001
def save_grasps_to_usd(
usd_path: str,
grasps: np.ndarray,
confidences: np.ndarray,
output_path: Optional[str] = None,
grasps_root: str = GRASPS_ROOT_PATH,
gripper_name: Optional[str] = None,
add_visualization: bool = True,
wireframe_width_m: float = GRASP_WIREFRAME_WIDTH_M,
):
"""Write grasp poses as Xforms into a USD file.
Args:
usd_path: Path to an existing USD file to augment (or the output path
if creating from scratch).
grasps: (N, 4, 4) array of homogeneous grasp transforms.
confidences: (N,) array of per-grasp confidence scores.
output_path: If provided, save to this path instead of overwriting
usd_path. If None, overwrites usd_path in place.
grasps_root: Prim path under which to create grasp Xforms.
gripper_name: If set, also add /world/grasps_visualization with gripper
wireframe at each pose (same format as viser_utils.visualize_grasp).
add_visualization: If False, skip adding grasps_visualization even when
gripper_name is set.
wireframe_width_m: Width/radius of the grasp wireframe curves in meters
(default 0.001 = 1mm). Override if lines look too thick in the viewer.
Returns:
The path to the written USD file.
"""
assert len(grasps) == len(confidences)
assert grasps.ndim == 3 and grasps.shape[1:] == (4, 4)
if output_path is None:
output_path = usd_path
if os.path.abspath(usd_path) != os.path.abspath(output_path):
import shutil
shutil.copy2(usd_path, output_path)
stage = Usd.Stage.Open(output_path)
else:
stage = Usd.Stage.Open(usd_path)
_remove_existing_grasps(stage, grasps_root)
UsdGeom.Xform.Define(stage, grasps_root)
for i, (grasp, conf) in enumerate(zip(grasps, confidences)):
grasp_path = f"{grasps_root}/grasp_{i:03d}"
xform = UsdGeom.Xform.Define(stage, grasp_path)
xform_op = xform.AddTransformOp()
mat = _numpy_to_gf_matrix4d(grasp)
xform_op.Set(mat)
prim = xform.GetPrim()
conf_attr = prim.CreateAttribute(
"graspgen:confidence", Sdf.ValueTypeNames.Float
)
conf_attr.Set(float(conf))
if gripper_name and add_visualization:
add_grasps_visualization_to_stage(
stage, grasps, confidences, gripper_name, vis_root=GRASPS_VIS_ROOT_PATH,
wireframe_width_m=wireframe_width_m,
)
print(f"Saved grasps_visualization to {output_path} under {GRASPS_VIS_ROOT_PATH}")
stage.GetRootLayer().Save()
print(f"Saved {len(grasps)} grasps to {output_path} under {grasps_root}")
return output_path
def load_grasps_from_usd(
usd_path: str,
grasps_root: str = GRASPS_ROOT_PATH,
):
"""Read grasp Xform poses back from a USD file.
Args:
usd_path: Path to the USD file.
grasps_root: Prim path under which grasp Xforms were stored.
Returns:
grasps: (N, 4, 4) numpy array of grasp transforms.
confidences: (N,) numpy array of confidence values.
"""
stage = Usd.Stage.Open(usd_path)
root_prim = stage.GetPrimAtPath(grasps_root)
if not root_prim:
return np.empty((0, 4, 4)), np.empty((0,))
grasps = []
confidences = []
for child in root_prim.GetChildren():
xf = UsdGeom.Xformable(child)
mat4 = xf.GetLocalTransformation()
grasps.append(_gf_matrix4d_to_numpy(mat4))
conf_attr = child.GetAttribute("graspgen:confidence")
conf = conf_attr.Get() if conf_attr else 0.0
confidences.append(float(conf))
return np.array(grasps), np.array(confidences)
def _numpy_to_gf_matrix4d(m: np.ndarray) -> Gf.Matrix4d:
"""Convert a 4x4 numpy array to a pxr Gf.Matrix4d (row-major)."""
return Gf.Matrix4d(*m.T.flatten().tolist())
def _gf_matrix4d_to_numpy(m: Gf.Matrix4d) -> np.ndarray:
"""Convert a pxr Gf.Matrix4d back to a 4x4 numpy array."""
return np.array(m, dtype=np.float64).T
def _remove_existing_grasps(stage: Usd.Stage, grasps_root: str):
"""Remove any pre-existing grasps prim tree so we can write fresh."""
prim = stage.GetPrimAtPath(grasps_root)
if prim:
stage.RemovePrim(grasps_root)
def _get_gripper_wireframe_segments(gripper_name: str) -> np.ndarray:
"""Build line segments for gripper wireframe in local frame (meters).
Same logic as grasp_gen.utils.viser_utils.visualize_grasp: consecutive
points in each polyline from load_control_points_for_visualization become
segments. Returns (num_segments, 2, 3) in gripper local frame.
"""
from grasp_gen.robot import load_control_points_for_visualization
segments_list: List[np.ndarray] = []
for ctrl_pts in load_control_points_for_visualization(gripper_name):
pts = np.array(ctrl_pts, dtype=np.float64)
if pts.ndim == 1:
pts = pts.reshape(-1, 3)
if pts.shape[0] < 2:
continue
for j in range(pts.shape[0] - 1):
segments_list.append(pts[j : j + 2])
if not segments_list:
return np.zeros((0, 2, 3), dtype=np.float64)
return np.stack(segments_list, axis=0)
def _add_basis_curves_prim(
stage: Usd.Stage,
parent_prim_path: str,
name: str,
segments: np.ndarray,
color_rgb: Tuple[float, float, float],
width_m: float = GRASP_WIREFRAME_WIDTH_M,
) -> Usd.Prim:
"""Add a UsdGeom.BasisCurves prim for line segments under parent_prim_path.
segments: (N, 2, 3) in meters. color_rgb: (r, g, b) in [0, 1].
width_m: curve width/radius in meters (default thin for wireframe).
"""
if segments.shape[0] == 0:
curves_path = f"{parent_prim_path}/{name}"
curves = UsdGeom.BasisCurves.Define(stage, curves_path)
curves.CreateCurveVertexCountsAttr().Set(Vt.IntArray([]))
curves.CreatePointsAttr().Set(Vt.Vec3fArray([]))
curves.CreateTypeAttr().Set(UsdGeom.Tokens.linear)
prim = curves.GetPrim()
else:
curve_vertex_counts = [2] * segments.shape[0]
points_flat = segments.reshape(-1, 3)
points_list = [Gf.Vec3f(float(p[0]), float(p[1]), float(p[2])) for p in points_flat]
curves_path = f"{parent_prim_path}/{name}"
curves = UsdGeom.BasisCurves.Define(stage, curves_path)
curves.CreateCurveVertexCountsAttr().Set(Vt.IntArray(curve_vertex_counts))
curves.CreatePointsAttr().Set(Vt.Vec3fArray(points_list))
curves.CreateTypeAttr().Set(UsdGeom.Tokens.linear)
# Thin lines: constant width for entire prim (in meters).
curves.CreateWidthsAttr().Set(Vt.FloatArray([width_m]))
prim = curves.GetPrim()
primvars_api = UsdGeom.PrimvarsAPI(prim)
display_color = primvars_api.CreatePrimvar(
"displayColor", Sdf.ValueTypeNames.Color3f
)
display_color.Set(Vt.Vec3fArray([Gf.Vec3f(*color_rgb)]))
return prim
def add_grasps_visualization_to_stage(
stage: Usd.Stage,
grasps: np.ndarray,
confidences: np.ndarray,
gripper_name: str,
vis_root: str = GRASPS_VIS_ROOT_PATH,
wireframe_width_m: float = GRASP_WIREFRAME_WIDTH_M,
) -> None:
"""Add /world/grasps_visualization with gripper wireframe at each grasp pose.
Creates vis_root/grasp_NNN (Xform with grasp transform) and under each
a BasisCurves child with the gripper wireframe. displayColor is set from
confidence (red = low, green = high).
"""
_remove_existing_grasps(stage, vis_root)
segments = _get_gripper_wireframe_segments(gripper_name)
UsdGeom.Xform.Define(stage, vis_root)
for i, (grasp, conf) in enumerate(zip(grasps, confidences)):
grasp_vis_path = f"{vis_root}/grasp_{i:03d}"
xform = UsdGeom.Xform.Define(stage, grasp_vis_path)
xform_op = xform.AddTransformOp()
xform_op.Set(_numpy_to_gf_matrix4d(grasp))
# Color from confidence: (1-c, c, 0) -> red (low) to green (high)
c = float(np.clip(conf, 0.0, 1.0))
color_rgb = (1.0 - c, c, 0.0)
_add_basis_curves_prim(
stage, grasp_vis_path, "wireframe", segments, color_rgb,
width_m=wireframe_width_m,
)
def _load_yaml_grasps(yaml_path: str):
"""Load grasps from an Isaac-format YAML file."""
import yaml
with open(yaml_path, "r") as f:
data = yaml.safe_load(f)
grasp_entries = data.get("grasps", {})
grasps, confidences = [], []
for _, g in sorted(grasp_entries.items()):
pos = np.array(g["position"])
qw = g["orientation"]["w"]
qxyz = g["orientation"]["xyz"]
T = tra.translation_matrix(pos) @ tra.quaternion_matrix([qw] + qxyz)
grasps.append(T)
confidences.append(g.get("confidence", 0.0))
return np.array(grasps), np.array(confidences)
def parse_args():
parser = argparse.ArgumentParser(
description="Save grasps as Xform poses into a USD file"
)
parser.add_argument(
"--usd_file", type=str, required=True, help="Path to the USD file to augment"
)
parser.add_argument(
"--grasps_yaml",
type=str,
required=True,
help="Path to Isaac-format YAML grasp file",
)
parser.add_argument(
"--output",
type=str,
default="",
help="Output USD path (default: overwrite usd_file)",
)
parser.add_argument(
"--gripper_name",
type=str,
default=None,
help="Gripper name for grasps_visualization wireframe (e.g. robotiq_2f_140, franka_panda). If set, adds /world/grasps_visualization.",
)
parser.add_argument(
"--no_visualization",
action="store_true",
help="Do not add grasps_visualization even when --gripper_name is set",
)
parser.add_argument(
"--wireframe_width",
type=float,
default=GRASP_WIREFRAME_WIDTH_M,
help=f"Width/radius of grasp wireframe curves in meters (default: {GRASP_WIREFRAME_WIDTH_M}). Use e.g. 0.0005 for thinner lines.",
)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
if not os.path.exists(args.usd_file):
print(f"Error: USD file not found: {args.usd_file}", file=sys.stderr)
sys.exit(1)
if not os.path.exists(args.grasps_yaml):
print(f"Error: Grasps file not found: {args.grasps_yaml}", file=sys.stderr)
sys.exit(1)
grasps, confidences = _load_yaml_grasps(args.grasps_yaml)
output = args.output if args.output else None
save_grasps_to_usd(
args.usd_file,
grasps,
confidences,
output_path=output,
gripper_name=args.gripper_name,
add_visualization=not args.no_visualization,
wireframe_width_m=args.wireframe_width,
)
================================================
FILE: scripts/train_graspgen.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""
Training script for GraspGen model.
"""
import os
import signal
import sys
import threading
from datetime import timedelta
from functools import partial
from itertools import chain
from time import sleep, time
import hydra
import numpy as np
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from matplotlib import pyplot as plt
from omegaconf import DictConfig, OmegaConf
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.nn.utils import clip_grad_norm_
from torch.utils.tensorboard.writer import SummaryWriter
from grasp_gen.dataset.dataset import (
get_cache_path,
get_cache_prefix,
get_pc_setting_name,
is_valid_cache_dir,
)
from grasp_gen.models.grasp_gen import GraspGenDiscriminator, GraspGenGenerator
from grasp_gen.utils.train_utils import (
add_to_dict,
build_optimizer,
compute_iou,
get_data_loader,
save_model,
to_cpu,
to_gpu,
write_scalar_ddp,
)
from grasp_gen.utils.logging_config import get_logger
# Configure logging
logger = get_logger(__name__)
# Global variables
handler_called = False
def train_one_epoch(
loader,
model,
optimizer,
clip_grad,
writer,
epoch,
global_step,
cfg,
batch_idx,
rank,
):
global handler_called
ws = 1
use_ddp = dist.is_available() and cfg.train.num_gpus > 1
if use_ddp:
rank = dist.get_rank()
ws = dist.get_world_size()
def signal_handler(sig, _):
global handler_called
if not handler_called:
if sig in [signal.SIGTERM, signal.SIGINT] and rank == 0:
handler_called = True
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
params = list(chain(*[x["params"] for x in optimizer.param_groups]))
num_steps = len(loader)
data_time = torch.tensor(0.0, device=rank)
step_time = torch.tensor(0.0, device=rank)
log_time = torch.tensor(0.0, device=rank)
start = time()
num_batch_updates = 0
outputs = {}
# start_training = False
# start_training = True
for i, data in enumerate(loader):
if handler_called:
logger.info(
f"Saving new checkpoints for rank:{rank} epoch: {epoch} batch index: {i}, global_step: {global_step}"
)
os.system(f"rm -rf {os.path.join(cfg.train.log_dir, 'last.pth')}")
save_model(
epoch - 1,
model,
optimizer,
cfg.train.log_dir,
use_ddp,
name="last",
batch_idx=i,
)
logger.info("Terminating training due to a interrupt, sayonara!")
sys.exit(0)
# if i == batch_idx:
# start_training = True
# print(i, data is not None)
if dist.is_initialized():
dist.barrier()
# if not start_training:
# continue
if data is None:
continue
global_step += 1
to_gpu(data)
data_time += time() - start
start = time()
optimizer.zero_grad()
if dist.is_initialized():
dist.barrier()
outputs, losses, stats = model(data, cfg.train)
if dist.is_initialized():
dist.barrier()
loss = sum([w * v for w, v in losses.values()])
if dist.is_initialized():
dist.barrier()
loss.backward()
if dist.is_initialized():
dist.barrier()
grad_has_inf_nan = False
if clip_grad is not None:
# print("Clipping gradients")
grad_norm = clip_grad(params)
grad_has_inf_nan = grad_norm.isinf() or grad_norm.isnan()
if use_ddp:
dist.reduce(grad_norm, dst=0)
if rank == 0:
if grad_norm.isinf():
logger.warning(
"Epoch", epoch, "Step", i + 1, "Gradient contains Inf"
)
elif grad_norm.isnan():
logger.warning(
"Epoch", epoch, "Step", i + 1, "Gradient contains NaN"
)
else:
writer.add_scalar(
"train/gradient_norm", grad_norm.item() / ws, global_step
)
if dist.is_initialized():
dist.barrier()
if not grad_has_inf_nan:
optimizer.step()
num_batch_updates += 1
if dist.is_initialized():
dist.barrier()
step_time += time() - start
start = time()
if dist.is_initialized():
dist.barrier()
losses["all_loss"] = (1, loss.detach())
for key in losses:
val = losses[key][1]
key = f"train_{key}" if "/" in key else f"train/loss/{key}"
write_scalar_ddp(writer, key, val, global_step, rank, ws, use_ddp)
if dist.is_initialized():
dist.barrier()
for key in stats:
val = stats[key]
key = f"train_{key}" if "/" in key else f"train/metric/{key}"
write_scalar_ddp(writer, key, val, global_step, rank, ws, use_ddp)
log_time += time() - start
start = time()
if dist.is_initialized():
dist.barrier()
if (i + 1) % cfg.train.print_freq == 0:
data_time = data_time.item() / ws / cfg.train.print_freq
step_time = step_time.item() / ws / cfg.train.print_freq
log_time = log_time.item() / ws / cfg.train.print_freq
if rank == 0:
logger.info(
f"Train Epoch {epoch:02d} {(i+1):04d}/{num_steps:04d} "
f"Data time {data_time:.4f} Forward time {step_time:.4f}"
f" Logging time {log_time:.4f} Loss {loss.detach():.4f}"
)
data_time = torch.tensor(0.0, device=rank)
step_time = torch.tensor(0.0, device=rank)
log_time = torch.tensor(0.0, device=rank)
return global_step
def eval_one_epoch(loader, model, writer, epoch, global_step, cfg):
global handler_called
rank = 0
ws = 1
use_ddp = dist.is_available() and cfg.train.num_gpus > 1
if use_ddp:
rank = dist.get_rank()
ws = dist.get_world_size()
num_steps = len(loader)
num_plots = num_steps // cfg.train.plot_freq
plot_ids = torch.randperm(num_steps)[:num_plots]
data_time = torch.tensor(0.0, device=rank)
step_time = torch.tensor(0.0, device=rank)
log_time = torch.tensor(0.0, device=rank)
total = {}
loss_dict_epoch, stats_epoch, stats_recon_epoch = {}, {}, {}
start = time()
outputs = {}
for i, data in enumerate(loader):
if handler_called:
logger.info("Terminating training, sayonara!")
sys.exit(0)
if data is None:
continue
to_gpu(data)
data_time += time() - start
start = time()
with torch.no_grad():
outputs, losses, stats = model(data, cfg.train)
if cfg.train.model_name == "diffusion":
_, _, stats_recon = model(data, eval=True)
loss = sum([w * v for w, v in losses.values()])
losses["all_loss"] = (1, loss.detach())
step_time += time() - start
start = time()
for key in losses:
add_to_dict(loss_dict_epoch, key, losses[key][1])
for key in stats:
add_to_dict(stats_epoch, key, stats[key])
if cfg.train.model_name == "diffusion":
for key in stats_recon:
add_to_dict(stats_recon_epoch, key, stats_recon[key])
log_time += time() - start
start = time()
if (i + 1) % cfg.train.print_freq == 0:
# if use_ddp:
# dist.reduce(data_time, 0)
# dist.reduce(step_time, 0)
# dist.reduce(log_time, 0)
if rank == 0:
data_time = data_time.item() / ws / cfg.train.print_freq
step_time = step_time.item() / ws / cfg.train.print_freq
log_time = log_time.item() / ws / cfg.train.print_freq
logger.info(
f"Valid Epoch {epoch:02d} {(i+1):04d}/{num_steps:04d} "
f"Data time {data_time:.4f} Forward time {step_time:.4f}"
f" Logging time {log_time:.4f}"
)
data_time = torch.tensor(0.0, device=rank)
step_time = torch.tensor(0.0, device=rank)
log_time = torch.tensor(0.0, device=rank)
total["steps"] = torch.tensor(i + 1, device=rank)
if use_ddp:
for key in total:
dist.reduce(total[key], 0)
for key, val in loss_dict_epoch.items():
write_scalar_ddp(
writer, f"valid/loss/{key}", val, global_step, rank, total["steps"], use_ddp
)
for key, val in stats_epoch.items():
write_scalar_ddp(
writer,
f"valid/metric/noise/{key}",
val,
global_step,
rank,
total["steps"],
use_ddp,
)
if cfg.train.model_name == "diffusion":
for key, val in stats_recon_epoch.items():
write_scalar_ddp(
writer,
f"valid/metric/reconstruction/{key}",
val,
global_step,
rank,
total["steps"],
use_ddp,
)
def init_seeds(seed):
# refer to https://pytorch.org/docs/stable/notes/randomness.html
np.random.seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
torch.use_deterministic_algorithms(mode=True, warn_only=True)
def train(rank, cfg):
if cfg.data.random_seed != -1:
seed = cfg.data.random_seed
logger.info(f"Setting seed to {seed}")
init_seeds(seed)
torch.backends.cudnn.benchmark = True
torch.set_num_threads(1)
use_ddp = dist.is_available() and cfg.train.num_gpus > 1
if use_ddp:
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = cfg.train.port
os.environ["NCCL_BLOCKING_WAIT"] = "0" # ADDed later
dist.init_process_group(
"nccl",
timeout=timedelta(seconds=7200000),
rank=rank,
world_size=cfg.train.num_gpus,
)
torch.cuda.set_device(rank)
scenes = None
if cfg.train.num_scenes is not None:
scenes = np.arange(cfg.train.num_scenes)
train_sampler, train_loader = get_data_loader(
cfg.train, cfg.data, "train", scenes, use_ddp, training=True
)
valid_sampler, valid_loader = get_data_loader(
cfg.train, cfg.data, "valid", scenes, use_ddp, training=False
)
if cfg.train.model_name == "diffusion":
model = GraspGenGenerator.from_config(cfg.diffusion).to(rank)
elif cfg.train.model_name == "discriminator":
model = GraspGenDiscriminator.from_config(cfg.discriminator).to(rank)
optimizer = build_optimizer(cfg, model)
total_params = sum(p.numel() for p in model.parameters())
logger.info(f"Number of parameters in {cfg.train.model_name} model: {total_params}")
init_epoch = 0
init_batch_idx = 0
logger.info(f"Attempting to load checkpoint from {cfg.train.checkpoint}")
try:
if cfg.train.checkpoint is not None:
if os.path.exists(cfg.train.checkpoint):
ckpt = torch.load(cfg.train.checkpoint, map_location="cpu")
init_epoch = ckpt["epoch"]
model.load_state_dict(ckpt["model"])
optimizer.load_state_dict(ckpt["optimizer"])
logger.info(f"Loading from checkpoint {cfg.train.checkpoint}")
init_batch_idx = ckpt["batch_idx"] if "batch_idx" in ckpt else 0
else:
logger.warning(f"Checkpoint file not found {cfg.train.checkpoint}")
except (RuntimeError, EOFError) as e:
logger.error(e)
logger.error("Checkpoint last.pth is most likly corrupted")
import glob
ckpt_dir = cfg.train.log_dir
if ckpt_dir.endswith("/"):
ckpt_dir = ckpt_dir.rstrip("/")
ckpt_list = [
ckpt for ckpt in glob.glob(ckpt_dir + "/*.pth") if ckpt.find("last") < 0
]
highest_ckpt_idx = sorted(
[
int(os.path.basename(ckpt_file).split("epoch_")[1].split(".pth")[0])
for ckpt_file in ckpt_list
]
)[-1]
ckpt_file = os.path.join(ckpt_dir, f"epoch_{str(highest_ckpt_idx)}.pth")
ckpt = torch.load(ckpt_file, map_location="cpu")
init_epoch = ckpt["epoch"]
model.load_state_dict(ckpt["model"])
optimizer.load_state_dict(ckpt["optimizer"])
logger.info(f"Loading from checkpoint {ckpt_file}")
init_batch_idx = ckpt["batch_idx"] if "batch_idx" in ckpt else 0
batch_idx = init_batch_idx
if use_ddp:
# https://github.com/Lightning-AI/lightning/issues/6789
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = DDP(model, device_ids=[rank], output_device=rank)
clip_grad = None
if cfg.optimizer.grad_clip > 0:
clip_grad = partial(clip_grad_norm_, max_norm=cfg.optimizer.grad_clip)
writer = None
if rank == 0:
writer = SummaryWriter(cfg.train.log_dir)
if rank == 0:
# Save training configuration to YAML file
config_save_path = os.path.join(cfg.train.log_dir, "config.yaml")
with open(config_save_path, "w") as f:
OmegaConf.save(cfg, f)
logger.info(f"Saved training configuration to {config_save_path}")
global_step = init_epoch * len(train_loader) + batch_idx
start = time()
logger.info(
f"Training starting at epoch {init_epoch+1} and batch index {batch_idx}"
)
for epoch in range(init_epoch, cfg.train.num_epochs):
model.train()
if use_ddp:
train_sampler.set_epoch(epoch)
global_step = train_one_epoch(
train_loader,
model,
optimizer,
clip_grad,
writer,
epoch + 1,
global_step,
cfg,
batch_idx,
rank,
)
batch_idx = 0 # Reset to 0 after first (and every) epoch
if (epoch + 1) % cfg.train.save_freq == 0 and rank == 0:
save_model(epoch + 1, model, optimizer, cfg.train.log_dir, use_ddp)
os.system(f"rm -rf {os.path.join(cfg.train.log_dir, 'last.pth')}")
save_model(
epoch + 1, model, optimizer, cfg.train.log_dir, use_ddp, name="last"
)
if (epoch + 1) % cfg.train.eval_freq == 0 or cfg.data.prefiltering:
model.eval()
if use_ddp:
valid_sampler.set_epoch(epoch)
eval_one_epoch(valid_loader, model, writer, epoch + 1, global_step, cfg)
if cfg.data.prefiltering:
logger.info(
"Prefiltering mode. Exiting Train script since we iterated one pass over the dataset"
)
break
total_time = torch.tensor(time() - start).to(rank)
if use_ddp:
dist.reduce(total_time, 0)
total_time = total_time.item() / cfg.train.num_gpus
if rank == 0:
try:
logger.info(f"Total training time: {timedelta(seconds=total_time)}")
except Exception as e:
logger.error(f"Error logging total training time: {e}")
pass
@hydra.main(config_path=".", config_name="config", version_base="1.3")
def main(cfg: DictConfig) -> None:
# TODO - Unify this later to a single gripper name and variable
if cfg.train.model_name == "diffusion":
assert (
cfg.diffusion.gripper_name == cfg.data.gripper_name
), "Gripper names must be the same for diffusion and data loader"
elif cfg.train.model_name == "discriminator":
assert (
cfg.discriminator.gripper_name == cfg.data.gripper_name
), "Gripper names must be the same for discriminator and data loader"
# These are just set by default now. TODO - once stable, remove these from config
cfg.data.prefiltering = False
cfg.data.preload_dataset = True
cache_dir = get_cache_path(cfg.data.cache_dir, cfg.data.root_dir)
# Detect number of available GPUs and set cfg.train.num_gpus
num_gpus_available = torch.cuda.device_count()
cfg.train.num_gpus = num_gpus_available
logger.info(
f"Detected {num_gpus_available} GPU(s) available, setting cfg.train.num_gpus = {num_gpus_available}"
)
if not is_valid_cache_dir(cfg.data):
logger.info("Cache is imcomplete. Running in prefiltering mode first!")
num_gpus_original = cfg.train.num_gpus
num_workers_original = cfg.train.num_workers
visualize_batch_original = cfg.data.visualize_batch
os.makedirs(cache_dir, exist_ok=True)
cfg.data.prefiltering = True
cfg.train.num_gpus = 1
cfg.train.num_workers = 0
cfg.data.visualize_batch = False
logger.info("Prefiltering mode. Running with 1 GPU")
train(0, cfg)
assert os.path.exists(cache_dir)
cfg.data.prefiltering = False
cfg.train.num_gpus = num_gpus_original
cfg.train.num_workers = num_workers_original
cfg.data.visualize_batch = visualize_batch_original
logger.info(
f"Prefiltering mode done. Running with original number of {num_gpus_original} GPUs"
)
sleep(2)
if cfg.train.debug:
train(0, cfg)
else:
mp.spawn(train, args=(cfg,), nprocs=cfg.train.num_gpus, join=True)
if __name__ == "__main__":
main()
================================================
FILE: scripts/train_m2t2.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""
Training script for M2T2.
"""
import os
import signal
import sys
import threading
from datetime import timedelta
from functools import partial
from itertools import chain
from time import sleep, time
import hydra
import numpy as np
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from matplotlib import pyplot as plt
from omegaconf import DictConfig, OmegaConf
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.nn.utils import clip_grad_norm_
from torch.utils.tensorboard.writer import SummaryWriter
from grasp_gen.models.m2t2 import M2T2
from grasp_gen.utils.plot_utils import plot_3D
from grasp_gen.utils.train_utils import (
add_to_dict,
build_optimizer,
compute_iou,
get_data_loader,
save_model,
to_cpu,
to_gpu,
write_scalar_ddp,
)
from grasp_gen.utils.logging_config import get_logger
# Configure logging
logger = get_logger(__name__)
# Global variables
handler_called = False
# lock = threading.Lock()
def train_one_epoch(
loader,
model,
optimizer,
clip_grad,
writer,
epoch,
global_step,
plot_fn,
cfg,
batch_idx,
rank,
):
global handler_called
ws = 1
use_ddp = dist.is_available() and cfg.train.num_gpus > 1
if use_ddp:
rank = dist.get_rank()
ws = dist.get_world_size()
def signal_handler(sig, _):
global handler_called
# lock.acquire()
if not handler_called:
if sig in [signal.SIGTERM, signal.SIGINT] and rank == 0:
handler_called = True
# lock.release()
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
params = list(chain(*[x["params"] for x in optimizer.param_groups]))
num_steps = len(loader)
data_time = torch.tensor(0.0, device=rank)
step_time = torch.tensor(0.0, device=rank)
log_time = torch.tensor(0.0, device=rank)
start = time()
num_batch_updates = 0
outputs = {}
start_training = False
for i, data in enumerate(loader):
# lock.acquire()
if handler_called:
logger.info(
f"Saving new checkpoints for rank:{rank} epoch: {epoch} batch index: {i}, global_step: {global_step}"
)
os.system(f"rm -rf {os.path.join(cfg.train.log_dir, 'last.pth')}")
save_model(
epoch - 1,
model,
optimizer,
cfg.train.log_dir,
use_ddp,
name="last",
batch_idx=i,
)
logger.info("Terminating training due to a interrupt, sayonara!")
sys.exit(0)
# lock.release()
if i == batch_idx:
start_training = True
if not start_training:
continue
if data is None:
continue
global_step += 1
to_gpu(data)
data_time += time() - start
start = time()
optimizer.zero_grad()
outputs, losses = model(data, cfg.train)
loss = sum([w * v for w, v in losses.values()])
loss.backward()
grad_has_inf_nan = False
if clip_grad is not None:
grad_norm = clip_grad(params)
grad_has_inf_nan = grad_norm.isinf() or grad_norm.isnan()
if use_ddp:
dist.reduce(grad_norm, dst=0)
if rank == 0:
if grad_norm.isinf():
logger.warning(
"Epoch", epoch, "Step", i + 1, "Gradient contains Inf"
)
elif grad_norm.isnan():
logger.warning(
"Epoch", epoch, "Step", i + 1, "Gradient contains NaN"
)
else:
writer.add_scalar(
"train/gradient_norm", grad_norm.item() / ws, global_step
)
if not grad_has_inf_nan:
optimizer.step()
num_batch_updates += 1
step_time += time() - start
start = time()
losses["all_loss"] = (1, loss.detach())
for key in losses:
val = losses[key][1]
key = f"train_{key}" if "/" in key else f"train/{key}"
write_scalar_ddp(writer, key, val, global_step, rank, ws, use_ddp)
iou = {}
if "grasping_masks" in outputs:
pred = outputs["matched_grasping_masks"]
target = data["grasping_masks"]
is_pick = torch.where(data["task_is_pick"])[0]
pred = [pred[j] for j in is_pick]
target = [target[j] for j in is_pick]
if len(pred) > 0:
iou["grasp"] = compute_iou(pred, target)
else:
iou["grasp"] = {
"scene": torch.tensor(torch.nan).to(rank),
"object": torch.tensor(torch.nan).to(rank),
}
if "placement_masks" in outputs:
pred = outputs["placement_masks"][data["task_is_place"]]
target = data["placement_masks"][data["task_is_place"]]
loss_masks = data["placement_region"][data["task_is_place"]] > 0
if len(pred) > 0:
iou["place"] = compute_iou(pred, target, loss_masks=loss_masks)
else:
iou["place"] = {"scene": torch.tensor(torch.nan).to(rank)}
for key in iou:
for subkey in iou[key]:
write_scalar_ddp(
writer,
f"train/IoU_{key}_{subkey}",
iou[key][subkey],
global_step,
rank,
ws,
use_ddp,
)
for key in ["pred", "gt"]:
for task in ["grasps", "placements"]:
if f"num_{key}_{task}" in outputs:
write_scalar_ddp(
writer,
f"train/num_{key}_{task}",
outputs[f"num_{key}_{task}"],
global_step,
rank,
ws,
use_ddp,
)
keys = [
f"{key}_{subkey}_ratio"
for key in ["grasping", "placement"]
for subkey in ["topk_pred_pos", "topk_gt_pos", "topk_hard_neg"]
]
for key in keys:
if key in outputs:
write_scalar_ddp(
writer, f"train/{key}", outputs[key], global_step, rank, ws, use_ddp
)
if "grasps" in outputs:
non_empty, num_obj = 0, 0
for masks in outputs["matched_grasping_masks"]:
non_empty += ((masks > 0).sum(dim=1) > 0).sum()
num_obj += masks.shape[0]
write_scalar_ddp(
writer,
"train/object_recall",
torch.div(non_empty, num_obj).to(rank),
global_step,
rank,
ws,
use_ddp,
)
# if (i + 1) % cfg.train.plot_freq == 0 and rank == 0:
# to_cpu(outputs)
# to_cpu(data)
# figs = plot_fn(outputs, data)
# for key in figs:
# writer.add_figure(f"train/{key}", figs[key], global_step)
# plt.close(figs[key])
log_time += time() - start
start = time()
if (i + 1) % cfg.train.print_freq == 0:
if use_ddp:
dist.reduce(data_time, 0)
dist.reduce(step_time, 0)
dist.reduce(log_time, 0)
data_time = data_time.item() / ws / cfg.train.print_freq
step_time = step_time.item() / ws / cfg.train.print_freq
log_time = log_time.item() / ws / cfg.train.print_freq
if rank == 0:
logger.info(
f"Train Epoch {epoch:02d} {(i+1):04d}/{num_steps:04d} "
f"Data time {data_time:.4f} Forward time {step_time:.4f}"
f" Logging time {log_time:.4f} Loss {loss.detach():.4f}"
)
data_time = torch.tensor(0.0, device=rank)
step_time = torch.tensor(0.0, device=rank)
log_time = torch.tensor(0.0, device=rank)
return global_step
def eval_one_epoch(loader, model, writer, epoch, global_step, plot_fn, cfg):
global handler_called
rank = 0
ws = 1
use_ddp = dist.is_available() and cfg.train.num_gpus > 1
if use_ddp:
rank = dist.get_rank()
ws = dist.get_world_size()
num_steps = len(loader)
num_plots = num_steps // cfg.train.plot_freq
plot_ids = torch.randperm(num_steps)[:num_plots]
data_time = torch.tensor(0.0, device=rank)
step_time = torch.tensor(0.0, device=rank)
log_time = torch.tensor(0.0, device=rank)
total = {
"objects": torch.tensor(0.0, device=rank),
"task_is_pick": torch.tensor(0.0, device=rank),
"task_is_place": torch.tensor(0.0, device=rank),
"both_has_grasp": torch.tensor(0.0, device=rank),
"pred_has_grasp": torch.tensor(0.0, device=rank),
"gt_has_grasp": torch.tensor(0.0, device=rank),
}
loss_dict, stats = {}, {}
start = time()
outputs = {}
for i, data in enumerate(loader):
# lock.acquire()
if handler_called:
logger.info("Terminating training, sayonara!")
sys.exit(0)
# lock.release()
if data is None:
continue
if "grasping_masks" in data:
num_obj = sum([len(masks) for masks in data["grasping_masks"]])
total["objects"] += num_obj
if "task_is_pick" in data:
num_pick = data["task_is_pick"].sum()
total["task_is_pick"] += num_pick
else:
num_pick = len(data["grasping_masks"])
total["task_is_pick"] += num_pick
if "placement_masks" in data:
if "task_is_place" in data:
num_place = data["task_is_place"].sum()
total["task_is_place"] += num_place
else:
num_place = data["placement_masks"].shape[0]
total["task_is_place"] += num_place
to_gpu(data)
data_time += time() - start
start = time()
with torch.no_grad():
outputs, losses = model(data, cfg.train)
loss = sum([w * v for w, v in losses.values()])
losses["all_loss"] = (1, loss.detach())
step_time += time() - start
start = time()
for key in losses:
add_to_dict(loss_dict, key, losses[key][1])
if "grasping_masks" in outputs:
pred = outputs["matched_grasping_masks"]
target = data["grasping_masks"]
is_pick = torch.where(data["task_is_pick"])[0]
pred = [pred[j] for j in is_pick]
target = [target[j] for j in is_pick]
if len(pred) > 0:
iou = compute_iou(pred, target)
for key in iou:
num = num_obj if key == "object" else num_pick
add_to_dict(stats, f"IoU_{key}", iou[key] * num)
if "placement_masks" in outputs:
pred = outputs["placement_masks"][data["task_is_place"]]
target = data["placement_masks"][data["task_is_place"]]
loss_masks = data["placement_region"][data["task_is_place"]] > 0
if len(pred) > 0:
iou = compute_iou(pred, target, loss_masks=loss_masks)
add_to_dict(stats, "IoU_placement_scene", iou["scene"] * num_place)
for task in ["grasps", "placements"]:
for key in ["pred", "gt"]:
if f"num_{key}_{task}" in outputs:
num = num_place if task == "placements" else num_obj
add_to_dict(
stats, f"num_{key}_{task}", outputs[f"num_{key}_{task}"] * num
)
keys = [
f"{key}_mask_{subkey}_ratio"
for key in ["grasping", "placement"]
for subkey in ["topk_pred_pos", "topk_gt_pos", "topk_true_neg"]
]
for key in keys:
if key in outputs:
num = num_place if "placement" in key else num_obj
add_to_dict(stats, key, outputs[key] * num)
if "grasps" in outputs:
for masks, gt_grasps in zip(
outputs["matched_grasping_masks"], data["grasps"]
):
for mask, gt_grasp in zip(masks, gt_grasps):
if (mask > 0).sum() > 0:
total["pred_has_grasp"] += 1
if gt_grasp.shape[0] > 0:
total["both_has_grasp"] += 1
if gt_grasp.shape[0] > 0:
total["gt_has_grasp"] += 1
# if i in plot_ids and rank == 0:
# to_cpu(outputs)
# to_cpu(data)
# figs = plot_fn(outputs, data)
# for key in figs:
# writer.add_figure(f"valid/{key}", figs[key], global_step + i)
# plt.close(figs[key])
log_time += time() - start
start = time()
if (i + 1) % cfg.train.print_freq == 0:
if use_ddp:
dist.reduce(data_time, 0)
dist.reduce(step_time, 0)
dist.reduce(log_time, 0)
if rank == 0:
data_time = data_time.item() / ws / cfg.train.print_freq
step_time = step_time.item() / ws / cfg.train.print_freq
log_time = log_time.item() / ws / cfg.train.print_freq
logger.info(
f"Valid Epoch {epoch:02d} {(i+1):04d}/{num_steps:04d} "
f"Data time {data_time:.4f} Forward time {step_time:.4f}"
f" Logging time {log_time:.4f}"
)
data_time = torch.tensor(0.0, device=rank)
step_time = torch.tensor(0.0, device=rank)
log_time = torch.tensor(0.0, device=rank)
total["steps"] = torch.tensor(i + 1, device=rank)
if use_ddp:
for key in total:
dist.reduce(total[key], 0)
if "grasps" in outputs:
write_scalar_ddp(
writer,
"valid/object_precision",
total["both_has_grasp"],
global_step,
rank,
total["pred_has_grasp"],
)
write_scalar_ddp(
writer,
"valid/object_recall",
total["both_has_grasp"],
global_step,
rank,
total["gt_has_grasp"],
)
for key, val in loss_dict.items():
write_scalar_ddp(
writer, f"valid/{key}", val, global_step, rank, total["steps"], use_ddp
)
for key, val in stats.items():
if "placement" in key:
num = total["task_is_place"]
elif "scene" in key:
num = total["task_is_pick"]
else:
num = total["objects"]
write_scalar_ddp(writer, f"valid/{key}", val, global_step, rank, num, use_ddp)
def init_seeds(seed):
# refer to https://pytorch.org/docs/stable/notes/randomness.html
np.random.seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
torch.use_deterministic_algorithms(mode=True, warn_only=True)
def train(rank, cfg):
if cfg.data.random_seed != -1:
seed = cfg.data.random_seed
logger.info(f"Setting seed to {seed}")
init_seeds(seed)
torch.backends.cudnn.benchmark = True
torch.set_num_threads(1)
use_ddp = dist.is_available() and cfg.train.num_gpus > 1
if use_ddp:
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = cfg.train.port
dist.init_process_group("nccl", rank=rank, world_size=cfg.train.num_gpus)
torch.cuda.set_device(rank)
scenes = None
if cfg.train.num_scenes is not None:
scenes = np.arange(cfg.train.num_scenes)
train_sampler, train_loader = get_data_loader(
cfg.train, cfg.data, "train", scenes, use_ddp, training=True
)
valid_sampler, valid_loader = get_data_loader(
cfg.train, cfg.data, "valid", scenes, use_ddp, training=False
)
plot_fn = partial(plot_3D, cam_coord=cfg.data.cam_coord)
model = M2T2.from_config(cfg.m2t2).to(rank)
optimizer = build_optimizer(cfg, model)
total_params = sum(p.numel() for p in model.parameters())
logger.info(f"Number of parameters in {cfg.train.model_name} model: {total_params}")
init_epoch = 0
init_batch_idx = 0
logger.info(f"Attempting to load checkpoint from {cfg.train.checkpoint}")
try:
if cfg.train.checkpoint is not None:
if os.path.exists(cfg.train.checkpoint):
ckpt = torch.load(cfg.train.checkpoint, map_location="cpu")
init_epoch = ckpt["epoch"]
model.load_state_dict(ckpt["model"])
optimizer.load_state_dict(ckpt["optimizer"])
logger.info(f"Loading from checkpoint {cfg.train.checkpoint}")
init_batch_idx = ckpt["batch_idx"] if "batch_idx" in ckpt else 0
else:
logger.warning(f"Checkpoint file not found {cfg.train.checkpoint}")
except (RuntimeError, EOFError) as e:
logger.error(e)
logger.error("Checkpoint last.pth is most likly corrupted")
import glob
ckpt_dir = cfg.train.log_dir
if ckpt_dir.endswith("/"):
ckpt_dir = ckpt_dir.rstrip("/")
ckpt_list = [
ckpt for ckpt in glob.glob(ckpt_dir + "/*.pth") if ckpt.find("last") < 0
]
highest_ckpt_idx = sorted(
[
int(os.path.basename(ckpt_file).split("epoch_")[1].split(".pth")[0])
for ckpt_file in ckpt_list
]
)[-1]
ckpt_file = os.path.join(ckpt_dir, f"epoch_{str(highest_ckpt_idx)}.pth")
ckpt = torch.load(ckpt_file, map_location="cpu")
init_epoch = ckpt["epoch"]
model.load_state_dict(ckpt["model"])
optimizer.load_state_dict(ckpt["optimizer"])
logger.info(f"Loading from checkpoint {ckpt_file}")
init_batch_idx = ckpt["batch_idx"] if "batch_idx" in ckpt else 0
batch_idx = init_batch_idx
if use_ddp:
# https://github.com/Lightning-AI/lightning/issues/6789
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = DDP(model, device_ids=[rank])
clip_grad = None
if cfg.optimizer.grad_clip > 0:
clip_grad = partial(clip_grad_norm_, max_norm=cfg.optimizer.grad_clip)
writer = None
if rank == 0:
writer = SummaryWriter(cfg.train.log_dir)
if rank == 0:
with open(f"{cfg.train.log_dir}/config.yaml", "w") as f:
OmegaConf.save(cfg, f)
global_step = init_epoch * len(train_loader) + batch_idx
start = time()
logger.info(
f"Training starting at epoch {init_epoch+1} and batch index {batch_idx}"
)
for epoch in range(init_epoch, cfg.train.num_epochs):
model.train()
if use_ddp:
train_sampler.set_epoch(epoch)
global_step = train_one_epoch(
train_loader,
model,
optimizer,
clip_grad,
writer,
epoch + 1,
global_step,
plot_fn,
cfg,
batch_idx,
rank,
)
batch_idx = 0 # Reset to 0 after first (and every) epoch
if (epoch + 1) % cfg.train.save_freq == 0 and rank == 0:
save_model(epoch + 1, model, optimizer, cfg.train.log_dir, use_ddp)
os.system(f"rm -rf {os.path.join(cfg.train.log_dir, 'last.pth')}")
save_model(
epoch + 1, model, optimizer, cfg.train.log_dir, use_ddp, name="last"
)
model.eval()
if use_ddp:
valid_sampler.set_epoch(epoch)
eval_one_epoch(
valid_loader, model, writer, epoch + 1, global_step, plot_fn, cfg
)
total_time = torch.tensor(time() - start).to(rank)
if use_ddp:
dist.reduce(total_time, 0)
total_time = total_time.item() / cfg.train.num_gpus
if rank == 0:
logger.info("Total training time", timedelta(seconds=total_time))
@hydra.main(config_path=".", config_name="config", version_base="1.3")
def main(cfg: DictConfig) -> None:
# TODO - Unify this later to a single gripper name and variable
assert (
cfg.data.gripper_name
== cfg.m2t2.action_decoder.gripper_name
== cfg.m2t2.grasp_loss.gripper_name
), "Gripper names must be the same for action decoder, grasp loss, and data"
if cfg.train.debug:
assert cfg.train.num_gpus <= 1
train(0, cfg)
else:
mp.spawn(train, args=(cfg,), nprocs=cfg.train.num_gpus, join=True)
if __name__ == "__main__":
main()
================================================
FILE: setup.py
================================================
# Copyright (c) 2025, 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.
from setuptools import setup, find_packages
setup(
name="grasp_gen",
version="1.0.0",
packages=find_packages(),
include_package_data=True,
install_requires=[
line for line in open('requirements.txt').readlines()
if "@" not in line
],
description="GraspGen",
author="",
author_email="",
license="",
url="",
keywords="robotics manipulation learning computer-vision",
classifiers=[
"Programming Language :: Python",
"Natural Language :: English",
"Topic :: Scientific/Engineering",
],
)
================================================
FILE: tests/conftest.py
================================================
import pytest
import torch
import numpy as np
def pytest_addoption(parser):
parser.addoption(
"--mesh",
action="store",
default=None,
help="Path to .obj mesh file (absolute or relative to repo root). "
"Defaults to assets/objects/box.obj.",
)
@pytest.fixture
def device():
"""Fixture to provide the device to use for tests."""
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
@pytest.fixture
def random_seed():
"""Fixture to set random seeds for reproducibility."""
torch.manual_seed(42)
np.random.seed(42)
return 42
@pytest.fixture
def sample_point_cloud():
"""Fixture to provide a sample point cloud for testing."""
points = torch.tensor(
[
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
[1.0, 1.0, 1.0],
]
)
return points
@pytest.fixture
def sample_rotation_matrix():
"""Fixture to provide a sample rotation matrix for testing."""
# Create a rotation matrix for 90 degrees around z-axis
matrix = torch.tensor([[0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
return matrix
@pytest.fixture
def sample_pose():
"""Fixture to provide a sample 4x4 pose matrix for testing."""
pose = torch.eye(4)
pose[:3, 3] = torch.tensor([1.0, 2.0, 3.0]) # Translation
return pose
================================================
FILE: tests/test_dataset_format.py
================================================
import pytest
import json
import os
import numpy as np
from pathlib import Path
from grasp_gen.dataset.webdataset_utils import is_webdataset, GraspWebDatasetReader
from grasp_gen.dataset.dataset_utils import GraspJsonDatasetReader
def test_dataset_format_convention():
"""Test that the dataset follows the GraspGen dataset convention."""
# Check if datasets are mounted
object_dataset_path = "/object_dataset"
grasp_dataset_path = "/grasp_dataset"
if not os.path.exists(object_dataset_path):
pytest.skip(f"Object dataset not mounted at {object_dataset_path}")
if not os.path.exists(grasp_dataset_path):
pytest.skip(f"Grasp dataset not mounted at {grasp_dataset_path}")
# Test 1: Check splits exist (for franka_panda gripper)
splits_path = os.path.join(grasp_dataset_path, "splits", "franka_panda")
train_split_path = os.path.join(splits_path, "train.txt")
valid_split_path = os.path.join(splits_path, "valid.txt")
assert os.path.exists(splits_path), f"Splits directory not found at {splits_path}"
assert os.path.exists(
train_split_path
), f"train.txt not found at {train_split_path}"
assert os.path.exists(
valid_split_path
), f"valid.txt not found at {valid_split_path}"
# Test 2: Check splits are not empty
with open(train_split_path, "r") as f:
train_objects = [line.strip() for line in f.readlines() if line.strip()]
with open(valid_split_path, "r") as f:
valid_objects = [line.strip() for line in f.readlines() if line.strip()]
assert len(train_objects) > 0, "train.txt is empty"
assert len(valid_objects) > 0, "valid.txt is empty"
# Test 4: Check grasp dataset format and initialize appropriate reader
grasp_data_path = os.path.join(grasp_dataset_path, "grasp_data", "franka_panda")
assert os.path.exists(
grasp_data_path
), f"Grasp data directory not found at {grasp_data_path}"
# Determine if this is a WebDataset or JSON dataset
if is_webdataset(grasp_data_path):
print(f"Detected WebDataset format in {grasp_data_path}")
# Check for WebDataset format: tar shards and uuid_index.json
tar_shards = list(Path(grasp_data_path).glob("shard_*.tar"))
uuid_index_path = os.path.join(grasp_data_path, "uuid_index.json")
assert len(tar_shards) > 0, f"No tar shards found in {grasp_data_path}"
assert os.path.exists(
uuid_index_path
), f"uuid_index.json not found at {uuid_index_path}"
# Load and validate uuid_index.json
with open(uuid_index_path, "r") as f:
uuid_index = json.load(f)
assert isinstance(uuid_index, dict), "uuid_index.json should be a dictionary"
assert len(uuid_index) > 0, "uuid_index.json is empty"
# Initialize WebDataset reader
grasp_dataset_reader = GraspWebDatasetReader(grasp_data_path)
else:
print(f"Detected JSON dataset format in {grasp_data_path}")
# Check for JSON files
json_files = list(Path(grasp_data_path).glob("*.json"))
assert len(json_files) > 0, f"No JSON files found in {grasp_data_path}"
# Initialize JSON dataset reader
grasp_dataset_reader = GraspJsonDatasetReader(grasp_data_path)
# Test 5: Validate grasp data structure using the appropriate reader
# Sample a few objects from the splits to validate
sample_objects = (
train_objects[:3] + valid_objects[:3]
) # Test first 3 from each split
for obj_id in sample_objects:
try:
# Use the appropriate reader to get grasp data
grasps_dict = grasp_dataset_reader.read_grasps_by_uuid(obj_id)
if grasps_dict is None:
print(f"Warning: No grasp data found for object {obj_id}")
continue
# Check required top-level keys
assert "object" in grasps_dict, f"Missing 'object' key for object {obj_id}"
assert "grasps" in grasps_dict, f"Missing 'grasps' key for object {obj_id}"
# Check object section
object_section = grasps_dict["object"]
assert (
"file" in object_section
), f"Missing 'file' key in object section for {obj_id}"
assert (
"scale" in object_section
), f"Missing 'scale' key in object section for {obj_id}"
# Check grasps section
grasps_section = grasps_dict["grasps"]
assert (
"transforms" in grasps_section
), f"Missing 'transforms' key in grasps section for {obj_id}"
assert (
"object_in_gripper" in grasps_section
), f"Missing 'object_in_gripper' key in grasps section for {obj_id}"
# Check data types and shapes
transforms = np.array(grasps_section["transforms"])
grasp_mask = np.array(grasps_section["object_in_gripper"])
# Validate transforms are 4x4 matrices
assert transforms.ndim == 3, f"Transforms should be 3D array for {obj_id}"
assert transforms.shape[1:] == (
4,
4,
), f"Transforms should be 4x4 matrices for {obj_id}"
# Validate mask length matches number of grasps
assert len(grasp_mask) == len(
transforms
), f"Mask length doesn't match number of grasps for {obj_id}"
# Validate mask contains only boolean values
assert grasp_mask.dtype == bool or np.all(
np.isin(grasp_mask, [0, 1])
), f"Mask should be boolean for {obj_id}"
# # Check that referenced object file exists (if it's a mesh file path)
# object_file = object_section["file"]
# if object_file.lower().endswith(('.obj', '.stl')):
# full_object_path = os.path.join(object_dataset_path, object_file)
# assert os.path.exists(full_object_path), f"Referenced object file not found: {full_object_path}"
# Check scale is a positive number
scale = object_section["scale"]
assert (
isinstance(scale, (int, float)) and scale > 0
), f"Scale should be positive number for {obj_id}"
except Exception as e:
pytest.fail(f"Error processing object {obj_id}: {e}")
# Test 6: Check that we can extract positive and negative grasps
for obj_id in sample_objects:
grasps_dict = grasp_dataset_reader.read_grasps_by_uuid(obj_id)
if grasps_dict is None:
continue
grasps = np.array(grasps_dict["grasps"]["transforms"])
grasp_mask = np.array(grasps_dict["grasps"]["object_in_gripper"])
positive_grasps = grasps[grasp_mask]
negative_grasps = grasps[~grasp_mask]
# Check that we have some grasps (at least one positive or negative)
assert (
len(positive_grasps) > 0 or len(negative_grasps) > 0
), f"No grasps found for {obj_id}"
# Check that positive grasps are valid 4x4 matrices
for grasp in positive_grasps:
assert grasp.shape == (
4,
4,
), f"Positive grasp should be 4x4 matrix for {obj_id}"
# Check that it's a valid transformation matrix (bottom row should be [0,0,0,1])
assert np.allclose(
grasp[3, :], [0, 0, 0, 1]
), f"Invalid transformation matrix for {obj_id}"
# Check that negative grasps are valid 4x4 matrices
for grasp in negative_grasps:
assert grasp.shape == (
4,
4,
), f"Negative grasp should be 4x4 matrix for {obj_id}"
# Check that it's a valid transformation matrix (bottom row should be [0,0,0,1])
assert np.allclose(
grasp[3, :], [0, 0, 0, 1]
), f"Invalid transformation matrix for {obj_id}"
print(f"✅ Dataset format validation passed!")
print(
f" - Object dataset: {len(train_objects)} train objects, {len(valid_objects)} valid objects"
)
print(
f" - Grasp dataset: {'WebDataset' if is_webdataset(grasp_data_path) else 'JSON'} format"
)
print(f" - All files follow GraspGen convention")
================================================
FILE: tests/test_grasp_server.py
================================================
import pytest
import torch
import numpy as np
import os
from grasp_gen.grasp_server import GraspGenSampler, load_grasp_cfg
from grasp_gen.utils.point_cloud_utils import point_cloud_outlier_removal
@pytest.fixture
def sample_point_cloud():
"""Create a sample point cloud for testing."""
# Randomly sample points in a unit cube
np.random.seed(42) # For reproducible tests
num_points = 3000
points = np.random.uniform(-0.5, 0.5, size=(num_points, 3))
return points
@pytest.fixture
def franka_config_path():
"""Path to franka gripper configuration."""
config_path = "/models/checkpoints/graspgen_franka_panda.yml"
if not os.path.exists(config_path):
pytest.skip(f"Franka config not found at {config_path}")
return config_path
@pytest.fixture
def robotiq_config_path():
"""Path to robotiq gripper configuration."""
config_path = "/models/checkpoints/graspgen_robotiq_2f_140.yml"
if not os.path.exists(config_path):
pytest.skip(f"Robotiq config not found at {config_path}")
return config_path
def test_grasp_sampler_initialization_franka(franka_config_path):
"""Test initialization of GraspGenSampler with franka gripper."""
# This test will be skipped if no GPU is available
if not torch.cuda.is_available():
pytest.skip("No GPU available for testing")
try:
grasp_cfg = load_grasp_cfg(franka_config_path)
sampler = GraspGenSampler(grasp_cfg)
assert sampler is not None
assert sampler.model is not None
assert grasp_cfg.data.gripper_name == "franka_panda"
except Exception as e:
pytest.skip(f"Franka model initialization failed: {str(e)}")
def test_grasp_sampler_initialization_robotiq(robotiq_config_path):
"""Test initialization of GraspGenSampler with robotiq gripper."""
# This test will be skipped if no GPU is available
if not torch.cuda.is_available():
pytest.skip("No GPU available for testing")
try:
grasp_cfg = load_grasp_cfg(robotiq_config_path)
sampler = GraspGenSampler(grasp_cfg)
assert sampler is not None
assert sampler.model is not None
assert grasp_cfg.data.gripper_name == "robotiq_2f_140"
except Exception as e:
pytest.skip(f"Robotiq model initialization failed: {str(e)}")
def test_grasp_sampling_basic_franka(sample_point_cloud, franka_config_path):
"""Test basic grasp sampling functionality with franka gripper."""
if not torch.cuda.is_available():
pytest.skip("No GPU available for testing")
try:
grasp_cfg = load_grasp_cfg(franka_config_path)
sampler = GraspGenSampler(grasp_cfg)
assert sample_point_cloud.shape == (3000, 3)
# Test with a small number of grasps
grasps, confidences = GraspGenSampler.run_inference(
sample_point_cloud,
sampler,
grasp_threshold=0.7,
num_grasps=10,
remove_outliers=False,
)
# Check return types
assert isinstance(grasps, (list, np.ndarray, torch.Tensor))
assert isinstance(confidences, (list, np.ndarray, torch.Tensor))
# If grasps were found, check their properties
if len(grasps) > 0:
# Check grasp poses are 4x4 matrices
assert grasps[0].shape == (4, 4)
# Check confidences are between 0 and 1
if isinstance(confidences, (np.ndarray, torch.Tensor)):
for conf in confidences:
assert conf >= 0 and conf <= 1
except Exception as e:
pytest.skip(f"Franka grasp sampling failed: {str(e)}")
def test_grasp_sampling_basic_robotiq(sample_point_cloud, robotiq_config_path):
"""Test basic grasp sampling functionality with robotiq gripper."""
if not torch.cuda.is_available():
pytest.skip("No GPU available for testing")
try:
grasp_cfg = load_grasp_cfg(robotiq_config_path)
sampler = GraspGenSampler(grasp_cfg)
# Test with a small number of grasps
grasps, confidences = GraspGenSampler.run_inference(
sample_point_cloud,
sampler,
grasp_threshold=0.7,
num_grasps=10,
remove_outliers=False,
)
# Check return types
assert isinstance(grasps, (list, np.ndarray, torch.Tensor))
assert isinstance(confidences, (list, np.ndarray, torch.Tensor))
# If grasps were found, check their properties
if len(grasps) > 0:
# Check grasp poses are 4x4 matrices
assert grasps[0].shape == (4, 4)
# Check confidences are between 0 and 1
if isinstance(confidences, (np.ndarray, torch.Tensor)):
for conf in confidences:
assert conf >= 0 and conf <= 1
except Exception as e:
pytest.skip(f"Robotiq grasp sampling failed: {str(e)}")
def test_grasp_sampling_parameters_franka(sample_point_cloud, franka_config_path):
"""Test grasp sampling with different parameters using franka gripper."""
if not torch.cuda.is_available():
pytest.skip("No GPU available for testing")
try:
grasp_cfg = load_grasp_cfg(franka_config_path)
sampler = GraspGenSampler(grasp_cfg)
# Test with different thresholds
for threshold in [0.5, 0.7]:
grasps, confidences = GraspGenSampler.run_inference(
sample_point_cloud,
sampler,
grasp_threshold=threshold,
num_grasps=1000,
remove_outliers=False,
)
if len(grasps) > 0 and isinstance(confidences, (np.ndarray, torch.Tensor)):
# Check that confidences meet threshold
for conf in confidences:
assert conf >= threshold
# Test with different numbers of grasps
for num_grasps in [500, 1000, 2000]:
grasps, _ = GraspGenSampler.run_inference(
sample_point_cloud,
sampler,
grasp_threshold=0.7,
num_grasps=num_grasps,
remove_outliers=False,
)
# Check that we don't get more grasps than requested
assert len(grasps) <= num_grasps
except Exception as e:
pytest.skip(f"Franka parameter testing failed: {str(e)}")
def test_grasp_sampling_parameters_robotiq(sample_point_cloud, robotiq_config_path):
"""Test grasp sampling with different parameters using robotiq gripper."""
if not torch.cuda.is_available():
pytest.skip("No GPU available for testing")
try:
grasp_cfg = load_grasp_cfg(robotiq_config_path)
sampler = GraspGenSampler(grasp_cfg)
# Test with different thresholds
for threshold in [0.5, 0.7]:
grasps, confidences = GraspGenSampler.run_inference(
sample_point_cloud,
sampler,
grasp_threshold=threshold,
num_grasps=1000,
remove_outliers=False,
)
if len(grasps) > 0 and isinstance(confidences, (np.ndarray, torch.Tensor)):
# Check that confidences meet threshold
for conf in confidences:
assert conf >= threshold
# Test with different numbers of grasps
for num_grasps in [500, 1000, 2000]:
grasps, _ = GraspGenSampler.run_inference(
sample_point_cloud,
sampler,
grasp_threshold=0.7,
num_grasps=num_grasps,
remove_outliers=False,
)
# Check that we don't get more grasps than requested
assert len(grasps) <= num_grasps
except Exception as e:
pytest.skip(f"Robotiq parameter testing failed: {str(e)}")
================================================
FILE: tests/test_inference_installation.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""
Test inference installation by checking if GraspGen can run end-to-end inference
with random weights (no checkpoints needed). Validates that:
1. All dependencies are correctly installed (torch, pointnet2_ops, etc.)
2. Models can be initialized with random weights for both pointnet and ptv3 backbones
3. Inference pipeline runs correctly end-to-end
4. Expected number of grasps (100) are generated
"""
import pytest
import torch
import numpy as np
import trimesh
from pathlib import Path
from omegaconf import DictConfig
from grasp_gen.models.grasp_gen import GraspGen
from grasp_gen.dataset.dataset import collate
# ─── Fixtures ───────────────────────────────────────────────────────────────
@pytest.fixture
def box_mesh_path():
"""Path to the test box mesh in assets/."""
assets_dir = Path(__file__).parent.parent / "assets" / "objects"
box_path = assets_dir / "box.obj"
if not box_path.exists():
pytest.skip(f"Test mesh not found at {box_path}")
return str(box_path)
@pytest.fixture
def point_cloud_from_box(box_mesh_path):
"""Load box.obj and sample 2000 centered points from its surface."""
mesh = trimesh.load(box_mesh_path)
mesh.apply_translation(-mesh.center_mass)
points, _ = trimesh.sample.sample_surface(mesh, 2000)
return torch.tensor(points, dtype=torch.float32)
def _make_generator_cfg(backbone: str) -> DictConfig:
"""Build a minimal generator config for the given backbone."""
return DictConfig({
"num_embed_dim": 256,
"num_obs_dim": 512,
"diffusion_embed_dim": 512,
"image_size": 256,
"num_diffusion_iters": 10, # small for fast testing
"num_diffusion_iters_eval": 10,
"obs_backbone": backbone,
"compositional_schedular": False,
"loss_pointmatching": True,
"loss_l1_pos": False,
"loss_l1_rot": False,
"grasp_repr": "r3_6d",
"kappa": -1.0,
"clip_sample": True,
"beta_schedule": "squaredcos_cap_v2",
"attention": "cat",
"grid_size": 0.02,
"gripper_name": "franka_panda",
"pose_repr": "mlp",
"num_grasps_per_object": 100,
"checkpoint_object_encoder_pretrained": None,
"ptv3": DictConfig({"grid_size": 0.02}),
})
def _make_discriminator_cfg(backbone: str) -> DictConfig:
"""Build a minimal discriminator config for the given backbone."""
return DictConfig({
"num_obs_dim": 512,
"obs_backbone": backbone,
"grasp_repr": "r3_6d",
"grid_size": 0.01,
"sample_embed_dim": 512,
"pose_repr": "mlp",
"topk_ratio": 0.40,
"checkpoint_object_encoder_pretrained": None,
"kappa": 3.30,
"gripper_name": "franka_panda",
"ptv3": DictConfig({"grid_size": 0.01}),
})
def _prepare_batch(point_cloud: torch.Tensor, device: torch.device) -> dict:
"""Prepare a collated batch dict from a point cloud, matching grasp_server.sample()."""
pc = point_cloud.to(device)
pc_center = pc.mean(dim=0)
pc_centered = pc - pc_center[None]
pc_color = torch.zeros_like(pc)
data = {
"task": "pick",
"inputs": torch.cat([pc_centered, pc_color[:, :3]], dim=-1).float(),
"points": pc_centered,
}
return collate([data])
def _run_inference(model, data_batch, num_grasps: int = 100):
"""Run model inference and return the predicted grasps tensor."""
model.grasp_generator.num_grasps_per_object = num_grasps
with torch.inference_mode():
outputs, _, _ = model.infer(data_batch)
assert "grasps_pred" in outputs, "Missing 'grasps_pred' key in model outputs"
grasps = outputs["grasps_pred"][0] # first (only) batch element
return grasps
# ─── Tests ──────────────────────────────────────────────────────────────────
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
@pytest.mark.parametrize("backbone", ["pointnet", "ptv3"])
def test_inference_100_grasps(backbone, point_cloud_from_box):
"""
End-to-end inference test: create a model with random weights, feed in
the box point cloud, and verify we get back exactly 100 grasps that are
valid 4×4 homogeneous matrices.
"""
device = torch.device("cuda")
# Build model with random weights
gen_cfg = _make_generator_cfg(backbone)
disc_cfg = _make_discriminator_cfg(backbone)
model = GraspGen.from_config(gen_cfg, disc_cfg).to(device).eval()
# Prepare data & run inference
data_batch = _prepare_batch(point_cloud_from_box, device)
grasps = _run_inference(model, data_batch, num_grasps=100)
# ── Check count ──
assert len(grasps) == 100, (
f"[{backbone}] Expected 100 grasps, got {len(grasps)}"
)
# ── Check shape: each grasp is a 4×4 matrix ──
assert grasps.shape == torch.Size([100, 4, 4]), (
f"[{backbone}] Expected shape [100, 4, 4], got {list(grasps.shape)}"
)
# ── Check valid homogeneous matrices ──
bottom_rows = grasps[:, 3, :]
expected_bottom = torch.tensor([0.0, 0.0, 0.0, 1.0], device=device)
assert torch.allclose(bottom_rows, expected_bottom.expand_as(bottom_rows), atol=1e-5), (
f"[{backbone}] Grasp matrices have invalid bottom row"
)
print(f"✅ [{backbone}] Generated {len(grasps)} valid 4×4 grasps")
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
@pytest.mark.parametrize("backbone", ["pointnet", "ptv3"])
def test_model_components(backbone):
"""Verify generator and discriminator are properly initialised."""
gen_cfg = _make_generator_cfg(backbone)
disc_cfg = _make_discriminator_cfg(backbone)
model = GraspGen.from_config(gen_cfg, disc_cfg)
assert hasattr(model, "grasp_generator"), "Missing grasp_generator"
assert hasattr(model, "grasp_discriminator"), "Missing grasp_discriminator"
assert model.grasp_generator.obs_backbone == backbone
assert model.grasp_discriminator.obs_backbone == backbone
assert model.grasp_generator.num_grasps_per_object == 100
print(f"✅ [{backbone}] Model components OK")
if __name__ == "__main__":
pytest.main([__file__, "-v"])
================================================
FILE: tests/test_inference_perf.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""
Inference performance benchmark for GraspGen.
Loads the model and a test object, benchmarks inference across acceleration
modes (None / torch.compile), and reports a comparison table with latency,
frequency, and speedup.
Usage:
# Default mesh (assets/objects/box.obj):
pytest tests/test_inference_perf.py -v -s
# Custom mesh:
pytest tests/test_inference_perf.py -v -s --mesh assets/objects/banana.obj
pytest tests/test_inference_perf.py -v -s --mesh /absolute/path/to/object.obj
Benchmark parameters:
WARMUP_SECONDS = 30.0 Wall-clock warmup time per mode (seconds).
WARMUP_MIN_ITERS = 100 Minimum warmup iterations before timing starts.
BENCH_ITERS = 500 Number of timed iterations per mode.
Backbones = pointnet, ptv3
Acceleration = None (eager baseline)
Num grasps = 100
Diffusion steps = 10
Each timed iteration is bracketed by torch.cuda.synchronize() calls
for accurate GPU measurement.
Reference numbers (NVIDIA GeForce RTX 3090, random weights, 100 grasps, 10 diffusion steps):
box.obj:
Backbone | Latency (ms) | Frequency (Hz)
---------|---------------------|--------------------
pointnet | 13.22 +/- 0.63 | 75.66 +/- 3.61
ptv3 | 36.74 +/- 0.75 | 27.22 +/- 0.55
banana.obj:
Backbone | Latency (ms) | Frequency (Hz)
---------|---------------------|--------------------
pointnet | 12.35 +/- 0.39 | 80.94 +/- 2.55
ptv3 | 35.82 +/- 1.48 | 27.92 +/- 1.16
Note: torch.compile silently falls back to eager for both backbones due to
extensive graph breaks (45+ each). Root causes:
- pointnet: pointnet2_ops custom autograd Functions (grouping_operation,
ball_query, etc.) cannot be traced by Dynamo.
- ptv3: spconv PyCapsule C++ ops and addict dynamic __setattr__.
"""
import time
from dataclasses import dataclass
import numpy as np
import pytest
import torch
import trimesh
from omegaconf import DictConfig
from pathlib import Path
from grasp_gen.models.grasp_gen import GraspGen
from grasp_gen.dataset.dataset import collate
# ─── Helpers ─────────────────────────────────────────────────────────────────
def _make_generator_cfg(backbone: str) -> DictConfig:
return DictConfig({
"num_embed_dim": 256,
"num_obs_dim": 512,
"diffusion_embed_dim": 512,
"image_size": 256,
"num_diffusion_iters": 10,
"num_diffusion_iters_eval": 10,
"obs_backbone": backbone,
"compositional_schedular": False,
"loss_pointmatching": True,
"loss_l1_pos": False,
"loss_l1_rot": False,
"grasp_repr": "r3_6d",
"kappa": -1.0,
"clip_sample": True,
"beta_schedule": "squaredcos_cap_v2",
"attention": "cat",
"grid_size": 0.02,
"gripper_name": "franka_panda",
"pose_repr": "mlp",
"num_grasps_per_object": 100,
"checkpoint_object_encoder_pretrained": None,
"ptv3": DictConfig({"grid_size": 0.02}),
})
def _make_discriminator_cfg(backbone: str) -> DictConfig:
return DictConfig({
"num_obs_dim": 512,
"obs_backbone": backbone,
"grasp_repr": "r3_6d",
"grid_size": 0.01,
"sample_embed_dim": 512,
"pose_repr": "mlp",
"topk_ratio": 0.40,
"checkpoint_object_encoder_pretrained": None,
"kappa": 3.30,
"gripper_name": "franka_panda",
"ptv3": DictConfig({"grid_size": 0.01}),
})
def _prepare_batch(point_cloud: torch.Tensor, device: torch.device) -> dict:
pc = point_cloud.to(device)
pc_center = pc.mean(dim=0)
pc_centered = pc - pc_center[None]
pc_color = torch.zeros_like(pc)
data = {
"task": "pick",
"inputs": torch.cat([pc_centered, pc_color[:, :3]], dim=-1).float(),
"points": pc_centered,
}
return collate([data])
@dataclass
class BenchResult:
latencies_s: np.ndarray
warmup_iters: int
warmup_elapsed: float
@property
def mean_ms(self) -> float:
return self.latencies_s.mean() * 1000.0
@property
def std_ms(self) -> float:
return self.latencies_s.std() * 1000.0
@property
def hz_mean(self) -> float:
return 1.0 / self.latencies_s.mean()
@property
def hz_std(self) -> float:
return self.latencies_s.std() / (self.latencies_s.mean() ** 2)
def _bench_loop(model, data_batch, warmup_seconds, warmup_min_iters, bench_iters) -> BenchResult:
warmup_start = time.perf_counter()
warmup_iters = 0
while True:
with torch.inference_mode():
model.infer(data_batch)
torch.cuda.synchronize()
warmup_iters += 1
if warmup_iters >= warmup_min_iters and (time.perf_counter() - warmup_start) >= warmup_seconds:
break
warmup_elapsed = time.perf_counter() - warmup_start
latencies = []
for _ in range(bench_iters):
torch.cuda.synchronize()
t0 = time.perf_counter()
with torch.inference_mode():
model.infer(data_batch)
torch.cuda.synchronize()
latencies.append(time.perf_counter() - t0)
return BenchResult(np.array(latencies), warmup_iters, warmup_elapsed)
ACCEL_MODES = [
("None", None),
]
def _print_table(backbone: str, mesh_name: str, rows):
"""Print a formatted comparison table."""
col_w = [17, 22, 22, 9]
sep = " "
header = sep.join([
f"{'Backbone':<10}",
f"{'Acceleration':<{col_w[0]}}",
f"{'Latency (ms)':>{col_w[1]}}",
f"{'Frequency (Hz)':>{col_w[2]}}",
f"{'Speedup':>{col_w[3]}}",
])
divider = sep.join([
"-" * 10,
"-" * col_w[0],
"-" * col_w[1],
"-" * col_w[2],
"-" * col_w[3],
])
lines = [
f"\n{'=' * len(header)}",
f" Mesh: {mesh_name}",
header,
divider,
]
for name, result, speedup in rows:
lat_str = f"{result.mean_ms:.2f} +/- {result.std_ms:.2f}"
hz_str = f"{result.hz_mean:.2f} +/- {result.hz_std:.2f}"
sp_str = f"{speedup:.2f}x"
lines.append(sep.join([
f"{backbone:<10}",
f"{name:<{col_w[0]}}",
f"{lat_str:>{col_w[1]}}",
f"{hz_str:>{col_w[2]}}",
f"{sp_str:>{col_w[3]}}",
]))
lines.append("=" * len(header))
print("\n".join(lines))
# ─── Fixtures ────────────────────────────────────────────────────────────────
@pytest.fixture
def mesh_path(request):
"""Resolve the mesh path from --mesh or fall back to assets/objects/box.obj."""
custom = request.config.getoption("--mesh")
if custom is not None:
p = Path(custom)
if not p.is_absolute():
p = Path(__file__).parent.parent / p
if not p.exists():
pytest.skip(f"Mesh not found at {p}")
return p
default = Path(__file__).parent.parent / "assets" / "objects" / "box.obj"
if not default.exists():
pytest.skip(f"Default mesh not found at {default}")
return default
@pytest.fixture
def point_cloud_from_mesh(mesh_path):
mesh = trimesh.load(str(mesh_path))
mesh.apply_translation(-mesh.center_mass)
points, _ = trimesh.sample.sample_surface(mesh, 2000)
return torch.tensor(points, dtype=torch.float32)
# ─── Benchmark ───────────────────────────────────────────────────────────────
WARMUP_SECONDS = 30.0
WARMUP_MIN_ITERS = 100
BENCH_ITERS = 500
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
@pytest.mark.parametrize("backbone", ["pointnet", "ptv3"])
def test_inference_perf(backbone, point_cloud_from_mesh, mesh_path):
"""
Benchmark model.infer() across acceleration modes.
For each mode (None, torch.compile) on the given backbone:
1. Build / transform the model.
2. Warmup for >= WARMUP_MIN_ITERS and >= WARMUP_SECONDS.
3. Time BENCH_ITERS calls with torch.cuda.synchronize().
Then print a comparison table with latency, frequency, and speedup
relative to the None (baseline) mode.
"""
device = torch.device("cuda")
gen_cfg = _make_generator_cfg(backbone)
disc_cfg = _make_discriminator_cfg(backbone)
base_model = GraspGen.from_config(gen_cfg, disc_cfg).to(device).eval()
base_model.grasp_generator.num_grasps_per_object = 100
data_batch = _prepare_batch(point_cloud_from_mesh, device)
rows = []
baseline_mean_s = None
for name, transform in ACCEL_MODES:
model = transform(base_model) if transform is not None else base_model
result = _bench_loop(model, data_batch, WARMUP_SECONDS, WARMUP_MIN_ITERS, BENCH_ITERS)
if baseline_mean_s is None:
baseline_mean_s = result.latencies_s.mean()
speedup = baseline_mean_s / result.latencies_s.mean()
rows.append((name, result, speedup))
_print_table(backbone, mesh_path.name, rows)
for name, result, _ in rows:
assert result.mean_ms > 0, f"{name}: latency must be positive"
if __name__ == "__main__":
pytest.main([__file__, "-v", "-s"])
================================================
FILE: tests/test_math_utils.py
================================================
import pytest
import torch
import numpy as np
from grasp_gen.utils.math_utils import (
rotation_6d_to_matrix,
matrix_to_rotation_6d,
compute_pose_distance_batch,
compute_pose_emd,
)
from grasp_gen.utils.so3 import so3_log_map, so3_exp_map
def test_rotation_so3_conversion():
# Test conversion between SO(3) rotation representation and matrix
# Create a random SO(3) log representation (3D vector) with batch dimension
so3_log = torch.randn(1, 3)
so3_log = (
so3_log / torch.norm(so3_log, dim=1, keepdim=True) * torch.pi * 0.5
) # Scale to reasonable rotation angle
# Convert to matrix and back
matrix = so3_exp_map(so3_log)
so3_log_recovered = so3_log_map(matrix)
# Check if the recovered SO(3) representation matches the original
assert torch.allclose(so3_log, so3_log_recovered, atol=1e-6)
def test_rotation_so3_batch():
# Test batch processing of SO(3) rotation conversion
batch_size = 5
so3_log_batch = torch.randn(batch_size, 3)
so3_log_batch = (
so3_log_batch / torch.norm(so3_log_batch, dim=1, keepdim=True) * torch.pi * 0.5
)
matrix_batch = so3_exp_map(so3_log_batch)
so3_log_batch_recovered = so3_log_map(matrix_batch)
assert torch.allclose(so3_log_batch, so3_log_batch_recovered, atol=1e-6)
def test_compute_pose_distance_batch():
# Create two sets of poses
poses1 = torch.eye(4).unsqueeze(0) # Identity pose
poses2 = torch.eye(4).unsqueeze(0) # Same identity pose
# Compute distance
distances = compute_pose_distance_batch(poses1, poses2)
# Distance should be 0 for identical poses
assert torch.allclose(distances, torch.zeros(1, 1), atol=1e-6)
# Test with different poses
poses2 = torch.eye(4).unsqueeze(0)
poses2[0, :3, 3] = torch.tensor([1.0, 0.0, 0.0]) # Translate by 1 unit in x
distances = compute_pose_distance_batch(poses1, poses2)
assert distances[0, 0] > 0 # Should have non-zero distance
def test_compute_pose_emd():
# Create two sets of identical poses
poses1 = torch.eye(4).unsqueeze(0)
poses2 = torch.eye(4).unsqueeze(0)
# EMD should be 0 for identical sets
emd = compute_pose_emd(poses1, poses2)
assert abs(emd) < 1e-6
# Test with different poses
poses2 = torch.eye(4).unsqueeze(0)
poses2[0, :3, 3] = torch.tensor([1.0, 0.0, 0.0])
emd = compute_pose_emd(poses1, poses2)
assert emd > 0 # Should have non-zero EMD
def test_rotation_orthogonality():
# Test that rotation matrices are orthogonal
d6 = torch.randn(6)
d6 = d6 / torch.norm(d6)
matrix = rotation_6d_to_matrix(d6)
# Check orthogonality: R * R^T should be identity
identity = torch.eye(3)
assert torch.allclose(torch.matmul(matrix, matrix.T), identity, atol=1e-6)
================================================
FILE: tests/test_point_cloud_utils.py
================================================
import pytest
import torch
import numpy as np
from grasp_gen.utils.point_cloud_utils import knn_points, point_cloud_outlier_removal
def test_knn_points_basic():
# Create a simple point cloud with known distances
points = torch.tensor(
[
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
[2.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 2.0, 0.0],
]
)
K = 2
dists, idxs = knn_points(points, K=K, norm=2)
# Check shapes
assert dists.shape == (5, K)
assert idxs.shape == (5, K)
# Check that distances are sorted
assert torch.all(dists[:, 0] <= dists[:, 1])
# Check that self-distances are not included
for i in range(5):
assert i not in idxs[i]
def test_knn_points_identical_points():
# Test with identical points
points = torch.ones((5, 3)) # All points at (1,1,1)
K = 2
dists, idxs = knn_points(points, K=K, norm=2)
# All distances should be 0 since points are identical
assert torch.allclose(dists, torch.zeros(5, K))
# Indices should be different from self
for i in range(5):
assert i not in idxs[i]
def test_point_cloud_outlier_removal():
# Create a point cloud with some obvious outliers
points = torch.tensor(
[
[0.0, 0.0, 0.0],
[0.1, 0.0, 0.0],
[0.0, 0.1, 0.0],
[0.0, 0.0, 0.1],
[10.0, 10.0, 10.0], # Obvious outlier
[20.0, 20.0, 20.0], # Another obvious outlier
]
)
filtered_pc, removed_pc = point_cloud_outlier_removal(points, threshold=0.5, K=3)
# Check that outliers were removed
assert filtered_pc.shape[0] < points.shape[0]
assert removed_pc.shape[0] > 0
# Check that the filtered points are within reasonable distance
center = torch.mean(filtered_pc, dim=0)
max_dist = torch.max(torch.norm(filtered_pc - center, dim=1))
assert max_dist < 1.0 # All points should be within 1 unit of center
def test_point_cloud_outlier_removal_no_outliers():
# Create a dense point cloud with no obvious outliers
points = torch.randn(100, 3) * 0.1 # Random points close to origin
filtered_pc, removed_pc = point_cloud_outlier_removal(points, threshold=0.5, K=10)
# Most points should be kept
assert filtered_pc.shape[0] > points.shape[0] * 0.9
assert removed_pc.shape[0] < points.shape[0] * 0.1
def test_point_cloud_outlier_removal_identical_points():
# Create a point cloud where all points are identical
identical_point = torch.tensor([1.0, 2.0, 3.0])
points = identical_point.repeat(50, 1) # 50 identical points
filtered_pc, removed_pc = point_cloud_outlier_removal(points, threshold=0.5, K=10)
# All points should be kept since they are identical (no outliers)
assert filtered_pc.shape[0] == points.shape[0]
assert removed_pc.shape[0] == 0
# All filtered points should still be identical to the original
assert torch.allclose(filtered_pc, points)
================================================
FILE: tests/test_robot.py
================================================
import pytest
import torch
import numpy as np
import trimesh
from pathlib import Path
from grasp_gen.robot import (
GripperInfo,
get_canonical_gripper_control_points,
generate_circle_points,
load_visualize_control_points_multi_suction,
parse_offset_transform_from_yaml,
load_gripper_yaml_file,
load_default_gripper_config,
get_gripper_info,
get_gripper_depth,
)
def test_canonical_gripper_control_points():
"""Test generation of canonical gripper control points."""
w = 0.1 # width
d = 0.05 # depth
control_points = get_canonical_gripper_control_points(w, d)
# Check shape
assert control_points.shape == (4, 3)
# Check points are in correct positions
expected_points = np.array(
[
[w / 2, 0, d / 2], # right_front
[-w / 2, 0, d / 2], # left_front
[w / 2, 0, d], # right_back
[-w / 2, 0, d], # left_back
]
)
assert np.allclose(control_points, expected_points)
def test_load_visualize_control_points_multi_suction():
"""Test generation of visualization points for multiple suction cups."""
# Create test suction center points
suction_centers = [[0.0, 0.0, 0.1], [0.1, 0.1, 0.1], [-0.1, -0.1, 0.1]]
points = load_visualize_control_points_multi_suction(suction_centers)
# Check shape (N suction cups, M points per cup, 3 coordinates)
assert points.shape[0] == len(suction_centers)
assert points.shape[2] == 3
# Check z-coordinates are all the same
z_coords = points[:, :, 2]
assert np.allclose(z_coords, 0.1)
# Check points form circles around centers
for i, center in enumerate(suction_centers):
cup_points = points[i]
distances = np.sqrt(np.sum((cup_points[:, :2] - center[:2]) ** 2, axis=1))
assert np.allclose(distances, 0.005, atol=1e-6) # radius=0.005
def test_parse_offset_transform_from_yaml():
"""Test parsing of offset transform from YAML format."""
# Create a sample transform in YAML format: [translation (xyz), quaternion (xyzw)]
yaml_transform = [
[0.1, 0.2, 0.3], # translation (x, y, z)
[0.0, 0.0, 0.0, 1.0], # quaternion (x, y, z, w) - identity rotation
]
transform = parse_offset_transform_from_yaml(yaml_transform)
# Check shape
assert transform.shape == (4, 4)
# Check values - should be identity rotation with translation
expected_transform = np.array(
[
[1.0, 0.0, 0.0, 0.1],
[0.0, 1.0, 0.0, 0.2],
[0.0, 0.0, 1.0, 0.3],
[0.0, 0.0, 0.0, 1.0],
]
)
assert np.allclose(transform, expected_transform)
def test_gripper_info_dataclass():
"""Test GripperInfo dataclass initialization and attributes."""
# Create sample meshes
collision_mesh = trimesh.creation.box(extents=[0.1, 0.1, 0.1])
visual_mesh = trimesh.creation.box(extents=[0.1, 0.1, 0.1])
offset_transform = np.eye(4)
control_points = np.array([[0.1, 0.0, 0.0], [-0.1, 0.0, 0.0]])
# Create GripperInfo instance
gripper_info = GripperInfo(
gripper_name="test_gripper",
collision_mesh=collision_mesh,
visual_mesh=visual_mesh,
offset_transform=offset_transform,
control_points=control_points,
depth=0.1,
symmetric=True,
)
# Check attributes
assert gripper_info.gripper_name == "test_gripper"
assert isinstance(gripper_info.collision_mesh, trimesh.base.Trimesh)
assert isinstance(gripper_info.visual_mesh, trimesh.base.Trimesh)
assert np.allclose(gripper_info.offset_transform, offset_transform)
assert np.allclose(gripper_info.control_points, control_points)
assert gripper_info.depth == 0.1
assert gripper_info.symmetric == True
@pytest.mark.parametrize("width,depth", [(0.1, 0.05), (0.2, 0.1), (0.05, 0.02)])
def test_canonical_gripper_control_points_parameterized(width, depth):
"""Test canonical gripper control points with different parameters."""
control_points = get_canonical_gripper_control_points(width, depth)
# Check shape
assert control_points.shape == (4, 3)
# Check points are in correct positions
expected_points = np.array(
[
[width / 2, 0, depth / 2], # right_front
[-width / 2, 0, depth / 2], # left_front
[width / 2, 0, depth], # right_back
[-width / 2, 0, depth], # left_back
]
)
assert np.allclose(control_points, expected_points)
def test_load_gripper_yaml_file(tmp_path):
"""Test loading gripper configuration from YAML file."""
# Create a temporary YAML file
yaml_content = """
gripper_name: test_gripper
width: 0.1
depth: 0.05
symmetric: true
offset_transform:
- [1.0, 0.0, 0.0, 0.1]
- [0.0, 1.0, 0.0, 0.2]
- [0.0, 0.0, 1.0, 0.3]
- [0.0, 0.0, 0.0, 1.0]
"""
yaml_file = tmp_path / "test_gripper.yaml"
yaml_file.write_text(yaml_content)
# Load the configuration
config = load_gripper_yaml_file(yaml_file)
# Check configuration values
assert config["gripper_name"] == "test_gripper"
assert config["width"] == 0.1
assert config["depth"] == 0.05
assert config["symmetric"] == True
assert len(config["offset_transform"]) == 4
def test_load_default_gripper_config_franka_panda():
"""Test loading default gripper configuration."""
# Test with a known gripper name
config = load_default_gripper_config("franka_panda")
assert "width" in config
assert "depth" in config
assert "transform_offset_from_asset_to_graspgen_convention" in config
def test_get_gripper_info():
"""Test getting gripper information."""
# Test with a known gripper name
gripper_info = get_gripper_info("franka_panda")
# Check that it returns a GripperInfo instance
assert isinstance(gripper_info, GripperInfo)
# Check essential attributes
assert gripper_info.gripper_name == "franka_panda"
assert isinstance(gripper_info.collision_mesh, trimesh.base.Trimesh)
assert isinstance(gripper_info.visual_mesh, trimesh.base.Trimesh)
assert isinstance(gripper_info.control_points, torch.Tensor)
def test_get_gripper_depth():
"""Test getting gripper depth."""
# Test with a known gripper name
depth = get_gripper_depth("franka_panda")
# Check that depth is a positive number
assert isinstance(depth, float)
assert depth > 0
@pytest.mark.parametrize("gripper_name", ["franka_panda", "robotiq_2f_140"])
def test_gripper_info_consistency(gripper_name):
"""Test consistency of gripper information across different grippers."""
gripper_info = get_gripper_info(gripper_name)
# Check that all required attributes are present
assert hasattr(gripper_info, "gripper_name")
assert hasattr(gripper_info, "collision_mesh")
assert hasattr(gripper_info, "visual_mesh")
assert hasattr(gripper_info, "control_points")
assert hasattr(gripper_info, "offset_transform")
assert hasattr(gripper_info, "transform_from_base_link_to_tool_tcp")
# Check that the transform from base link to tool TCP is a 4x4 matrix
assert gripper_info.transform_from_base_link_to_tool_tcp.shape == (4, 4)
# Check that the offset transform is a 4x4 matrix
assert gripper_info.offset_transform.shape == (4, 4)
# Check that meshes are valid
assert gripper_info.collision_mesh.is_watertight
assert gripper_info.visual_mesh.is_watertight
================================================
FILE: tests/test_rotation_conversions.py
================================================
import pytest
import torch
import numpy as np
from grasp_gen.utils.rotation_conversions import (
matrix_to_euler_angles,
euler_angles_to_matrix,
matrix_to_quaternion,
quaternion_to_matrix,
)
def test_euler_matrix_conversion(sample_rotation_matrix):
"""Test conversion between Euler angles and rotation matrix."""
# Convert matrix to Euler angles
euler = matrix_to_euler_angles(sample_rotation_matrix, convention="XYZ")
# Convert back to matrix
matrix_recovered = euler_angles_to_matrix(euler, convention="XYZ")
# Check if the recovered matrix matches the original
assert torch.allclose(sample_rotation_matrix, matrix_recovered, atol=1e-6)
def test_quaternion_matrix_conversion(sample_rotation_matrix):
"""Test conversion between quaternion and rotation matrix."""
# Convert matrix to quaternion
quat = matrix_to_quaternion(sample_rotation_matrix)
# Convert back to matrix
matrix_recovered = quaternion_to_matrix(quat)
# Check if the recovered matrix matches the original
assert torch.allclose(sample_rotation_matrix, matrix_recovered, atol=1e-6)
def test_euler_angles_consistency():
"""Test consistency of Euler angle conversions with different conventions."""
# Create a rotation matrix
matrix = torch.tensor([[0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
# Convert to Euler angles with different conventions
euler_xyz = matrix_to_euler_angles(matrix, convention="XYZ")
euler_zyx = matrix_to_euler_angles(matrix, convention="ZYX")
# Convert back to matrix
matrix_xyz = euler_angles_to_matrix(euler_xyz, convention="XYZ")
matrix_zyx = euler_angles_to_matrix(euler_zyx, convention="ZYX")
# All should give the same rotation matrix
assert torch.allclose(matrix, matrix_xyz, atol=1e-6)
assert torch.allclose(matrix, matrix_zyx, atol=1e-6)
def test_rotation_identity():
"""Test that identity rotation is preserved through conversions."""
# Identity rotation matrix
identity = torch.eye(3)
# Convert to Euler angles and back
euler = matrix_to_euler_angles(identity, convention="XYZ")
matrix_euler = euler_angles_to_matrix(euler, convention="XYZ")
# Convert to quaternion and back
quat = matrix_to_quaternion(identity)
matrix_quat = quaternion_to_matrix(quat)
# All should give identity matrix
assert torch.allclose(identity, matrix_euler, atol=1e-6)
assert torch.allclose(identity, matrix_quat, atol=1e-6)
def test_rotation_composition():
"""Test that rotation compositions work correctly."""
# Create two rotation matrices
matrix1 = torch.tensor(
[[0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]]
) # 90 degrees around z
matrix2 = torch.tensor(
[[1.0, 0.0, 0.0], [0.0, 0.0, -1.0], [0.0, 1.0, 0.0]]
) # 90 degrees around x
# Compose rotations
composed = torch.matmul(matrix2, matrix1)
# Convert to Euler angles
euler = matrix_to_euler_angles(composed, convention="XYZ")
# Convert back to matrix
matrix_recovered = euler_angles_to_matrix(euler, convention="XYZ")
# Check if composition is preserved
assert torch.allclose(composed, matrix_recovered, atol=1e-6)
================================================
FILE: tests/test_serving.py
================================================
"""Tests for the GraspGen ZMQ server/client architecture.
Three test categories:
1. Unit tests for client-side point cloud loading (no server needed).
2. Protocol tests using a lightweight mock ZMQ server in a thread.
3. Integration test against a live server (skipped unless --run-integration).
"""
import os
import threading
import time
import tempfile
import numpy as np
import pytest
import zmq
import msgpack
import msgpack_numpy
msgpack_numpy.patch()
# ---------------------------------------------------------------------------
# Paths
# ---------------------------------------------------------------------------
ASSETS_DIR = os.path.join(os.path.dirname(__file__), "..", "assets", "objects")
EXAMPLE_PCD = os.path.join(ASSETS_DIR, "example_object.pcd")
EXAMPLE_MESH = os.path.join(ASSETS_DIR, "box.obj")
# ===================================================================
# 1. Unit tests — client-side point cloud loaders
# ===================================================================
class TestPCDReader:
"""Tests for the minimal ASCII PCD reader."""
def test_read_example_pcd(self):
from tools.graspgen_client import _read_pcd_ascii
if not os.path.exists(EXAMPLE_PCD):
pytest.skip("Example PCD not found")
pts = _read_pcd_ascii(EXAMPLE_PCD)
assert pts.ndim == 2
assert pts.shape[1] == 3
assert pts.dtype == np.float32
assert len(pts) > 100
def test_read_synthetic_pcd(self, tmp_path):
from tools.graspgen_client import _read_pcd_ascii
pcd_file = tmp_path / "test.pcd"
pcd_file.write_text(
"# .PCD v0.7\n"
"VERSION 0.7\n"
"FIELDS x y z\n"
"SIZE 4 4 4\n"
"TYPE F F F\n"
"COUNT 1 1 1\n"
"WIDTH 3\n"
"HEIGHT 1\n"
"VIEWPOINT 0 0 0 1 0 0 0\n"
"POINTS 3\n"
"DATA ascii\n"
"1.0 2.0 3.0\n"
"4.0 5.0 6.0\n"
"7.0 8.0 9.0\n"
)
pts = _read_pcd_ascii(str(pcd_file))
np.testing.assert_array_almost_equal(
pts, [[1, 2, 3], [4, 5, 6], [7, 8, 9]], decimal=5
)
class TestLoadPointCloudFromFile:
"""Tests for load_point_cloud_from_file across supported formats."""
def test_load_pcd(self):
from tools.graspgen_client import load_point_cloud_from_file
if not os.path.exists(EXAMPLE_PCD):
pytest.skip("Example PCD not found")
pts = load_point_cloud_from_file(EXAMPLE_PCD)
assert pts.ndim == 2
assert pts.shape[1] == 3
# Centering: mean should be ~0
np.testing.assert_array_almost_equal(pts.mean(axis=0), [0, 0, 0], decimal=4)
def test_load_npy(self, tmp_path):
from tools.graspgen_client import load_point_cloud_from_file
arr = np.random.randn(500, 3).astype(np.float64)
npy_file = tmp_path / "cloud.npy"
np.save(str(npy_file), arr)
pts = load_point_cloud_from_file(str(npy_file))
assert pts.dtype == np.float32
assert pts.shape == (500, 3)
np.testing.assert_array_almost_equal(pts.mean(axis=0), [0, 0, 0], decimal=4)
def test_load_xyz(self, tmp_path):
from tools.graspgen_client import load_point_cloud_from_file
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
xyz_file = tmp_path / "cloud.xyz"
np.savetxt(str(xyz_file), arr)
pts = load_point_cloud_from_file(str(xyz_file))
assert pts.shape == (3, 3)
np.testing.assert_array_almost_equal(pts.mean(axis=0), [0, 0, 0], decimal=4)
def test_unsupported_format_raises(self, tmp_path):
from tools.graspgen_client import load_point_cloud_from_file
bad = tmp_path / "cloud.csv"
bad.write_text("1,2,3\n")
with pytest.raises(ValueError, match="Unsupported"):
load_point_cloud_from_file(str(bad))
def test_bad_shape_raises(self, tmp_path):
from tools.graspgen_client import load_point_cloud_from_file
arr = np.random.randn(10, 2).astype(np.float32)
npy_file = tmp_path / "bad.npy"
np.save(str(npy_file), arr)
with pytest.raises(ValueError, match="Expected"):
load_point_cloud_from_file(str(npy_file))
class TestLoadPointCloudFromMesh:
def test_load_box_mesh(self):
from tools.graspgen_client import load_point_cloud_from_mesh
if not os.path.exists(EXAMPLE_MESH):
pytest.skip("Example mesh not found")
pts = load_point_cloud_from_mesh(EXAMPLE_MESH, scale=1.0, num_points=500)
assert pts.shape == (500, 3)
assert pts.dtype == np.float32
np.testing.assert_array_almost_equal(pts.mean(axis=0), [0, 0, 0], decimal=3)
def test_mesh_scale(self):
from tools.graspgen_client import load_point_cloud_from_mesh
if not os.path.exists(EXAMPLE_MESH):
pytest.skip("Example mesh not found")
pts1 = load_point_cloud_from_mesh(EXAMPLE_MESH, scale=1.0, num_points=1000)
pts2 = load_point_cloud_from_mesh(EXAMPLE_MESH, scale=2.0, num_points=1000)
extent1 = pts1.max(axis=0) - pts1.min(axis=0)
extent2 = pts2.max(axis=0) - pts2.min(axis=0)
# Scaled mesh should have ~2x extent
ratio = extent2 / (extent1 + 1e-8)
assert np.all(ratio > 1.5), f"Scale ratio unexpectedly low: {ratio}"
# ===================================================================
# 2. Mock ZMQ server tests — protocol correctness
# ===================================================================
def _make_fake_grasps(n: int):
"""Return fake (n, 4, 4) grasps and (n,) confidences."""
grasps = np.tile(np.eye(4, dtype=np.float32), (n, 1, 1))
for i in range(n):
grasps[i, :3, 3] = np.random.randn(3).astype(np.float32) * 0.1
confs = np.linspace(0.3, 0.95, n).astype(np.float32)
return grasps, confs
class MockGraspGenServer:
"""Lightweight ZMQ REP server for testing the client protocol."""
def __init__(self, port: int, num_fake_grasps: int = 10):
self._port = port
self._num_fake_grasps = num_fake_grasps
self._ctx = zmq.Context()
self._socket = self._ctx.socket(zmq.REP)
self._socket.bind(f"tcp://127.0.0.1:{port}")
self._running = True
self._thread = threading.Thread(target=self._loop, daemon=True)
self._thread.start()
def _loop(self):
poller = zmq.Poller()
poller.register(self._socket, zmq.POLLIN)
while self._running:
socks = dict(poller.poll(timeout=200))
if self._socket in socks:
raw = self._socket.recv()
req = msgpack.unpackb(raw, raw=False)
resp = self._handle(req)
self._socket.send(msgpack.packb(resp, use_bin_type=True))
def _handle(self, req: dict) -> dict:
action = req.get("action")
if action == "health":
return {"status": "ok"}
if action == "metadata":
return {
"gripper_name": "mock_gripper",
"model_name": "mock_model",
"gripper_config": "/mock/config.yml",
}
if action == "infer":
pc = np.asarray(req.get("point_cloud", []), dtype=np.float32)
if pc.ndim != 2 or pc.shape[1] != 3:
return {"error": f"point_cloud must be (N, 3), got {pc.shape}"}
n = min(self._num_fake_grasps, int(req.get("topk_num_grasps", self._num_fake_grasps)))
if n <= 0:
n = self._num_fake_grasps
grasps, confs = _make_fake_grasps(n)
return {
"grasps": grasps,
"confidences": confs,
"num_grasps": n,
"timing": {"infer_ms": 42.0},
}
return {"error": f"Unknown action: {action}"}
def stop(self):
self._running = False
self._thread.join(timeout=3)
self._socket.close()
self._ctx.term()
@pytest.fixture(scope="module")
def mock_server():
"""Start a mock ZMQ server on a random port for the test module."""
port = 15556 + os.getpid() % 1000
server = MockGraspGenServer(port=port, num_fake_grasps=10)
time.sleep(0.3)
yield port
server.stop()
class TestClientProtocol:
"""Test the GraspGenClient against the mock server."""
def test_health_check(self, mock_server):
from grasp_gen.serving.zmq_client import GraspGenClient
client = GraspGenClient(
host="127.0.0.1", port=mock_server,
wait_for_server=False, timeout_ms=5000,
)
assert client.health_check() is True
client.close()
def test_get_metadata(self, mock_server):
from grasp_gen.serving.zmq_client import GraspGenClient
client = GraspGenClient(
host="127.0.0.1", port=mock_server,
wait_for_server=False, timeout_ms=5000,
)
meta = client.get_metadata()
assert meta["gripper_name"] == "mock_gripper"
assert meta["model_name"] == "mock_model"
client.close()
def test_infer_returns_correct_shapes(self, mock_server):
from grasp_gen.serving.zmq_client import GraspGenClient
client = GraspGenClient(
host="127.0.0.1", port=mock_server,
wait_for_server=False, timeout_ms=5000,
)
pc = np.random.randn(500, 3).astype(np.float32)
grasps, confs = client.infer(pc, num_grasps=50, topk_num_grasps=10)
assert grasps.ndim == 3
assert grasps.shape[1:] == (4, 4)
assert grasps.dtype == np.float32
assert confs.ndim == 1
assert len(confs) == len(grasps)
assert confs.dtype == np.float32
client.close()
def test_infer_validates_bad_input(self, mock_server):
from grasp_gen.serving.zmq_client import GraspGenClient
client = GraspGenClient(
host="127.0.0.1", port=mock_server,
wait_for_server=False, timeout_ms=5000,
)
with pytest.raises(ValueError, match=r"\(N, 3\)"):
client.infer(np.random.randn(10, 2).astype(np.float32))
client.close()
def test_context_manager(self, mock_server):
from grasp_gen.serving.zmq_client import GraspGenClient
with GraspGenClient(
host="127.0.0.1", port=mock_server,
wait_for_server=False, timeout_ms=5000,
) as client:
assert client.health_check() is True
def test_wait_for_server_connects(self, mock_server):
from grasp_gen.serving.zmq_client import GraspGenClient
client = GraspGenClient(
host="127.0.0.1", port=mock_server,
wait_for_server=True, timeout_ms=5000,
)
assert client.server_metadata is not None
assert client.server_metadata["gripper_name"] == "mock_gripper"
client.close()
def test_multiple_infer_calls(self, mock_server):
"""Verify the REQ/REP pattern works for sequential requests."""
from grasp_gen.serving.zmq_client import GraspGenClient
with GraspGenClient(
host="127.0.0.1", port=mock_server,
wait_for_server=False, timeout_ms=5000,
) as client:
for _ in range(5):
pc = np.random.randn(200, 3).astype(np.float32)
grasps, confs = client.infer(pc, topk_num_grasps=5)
assert len(grasps) == 5
assert len(confs) == 5
# ===================================================================
# 3. Integration test — live server
# ===================================================================
def _live_server_available(host: str = "localhost", port: int = 5556) -> bool:
"""Quick probe: can we connect and get a health response?"""
ctx = zmq.Context()
sock = ctx.socket(zmq.REQ)
sock.setsockopt(zmq.RCVTIMEO, 2000)
sock.setsockopt(zmq.SNDTIMEO, 2000)
sock.setsockopt(zmq.LINGER, 0)
try:
sock.connect(f"tcp://{host}:{port}")
sock.send(msgpack.packb({"action": "health"}, use_bin_type=True))
resp = msgpack.unpackb(sock.recv(), raw=False)
return resp.get("status") == "ok"
except Exception:
return False
finally:
sock.close()
ctx.term()
@pytest.mark.integration
class TestLiveServer:
"""Tests that require a running GraspGen server on localhost:5556.
Run with: pytest tests/test_serving.py -m integration
These are skipped by default unless the server is reachable.
"""
@pytest.fixture(autouse=True)
def _require_live_server(self):
if not _live_server_available():
pytest.skip("Live GraspGen server not available on localhost:5556")
def test_live_health(self):
from grasp_gen.serving.zmq_client import GraspGenClient
with GraspGenClient(host="localhost", port=5556, wait_for_server=False, timeout_ms=10000) as client:
assert client.health_check() is True
def test_live_metadata(self):
from grasp_gen.serving.zmq_client import GraspGenClient
with GraspGenClient(host="localhost", port=5556, wait_for_server=True, timeout_ms=10000) as client:
meta = client.server_metadata
assert "gripper_name" in meta
assert "model_name" in meta
def test_live_infer_random_cloud(self):
from grasp_gen.serving.zmq_client import GraspGenClient
pc = np.random.randn(2000, 3).astype(np.float32) * 0.05
with GraspGenClient(host="localhost", port=5556, wait_for_server=True, timeout_ms=60000) as client:
grasps, confs = client.infer(
pc, num_grasps=50, topk_num_grasps=10,
)
assert grasps.ndim == 3
assert grasps.shape[1:] == (4, 4)
assert len(confs) == len(grasps)
def test_live_infer_example_pcd(self):
from grasp_gen.serving.zmq_client import GraspGenClient
from tools.graspgen_client import load_point_cloud_from_file
if not os.path.exists(EXAMPLE_PCD):
pytest.skip("Example PCD not found")
pc = load_point_cloud_from_file(EXAMPLE_PCD)
with GraspGenClient(host="localhost", port=5556, wait_for_server=True, timeout_ms=60000) as client:
grasps, confs = client.infer(
pc, num_grasps=100, topk_num_grasps=20,
)
assert grasps.shape[1:] == (4, 4)
assert len(confs) == len(grasps)
if len(confs) > 0:
assert confs.min() >= 0.0
assert confs.max() <= 1.0
def test_live_infer_example_mesh(self):
from grasp_gen.serving.zmq_client import GraspGenClient
from tools.graspgen_client import load_point_cloud_from_mesh
if not os.path.exists(EXAMPLE_MESH):
pytest.skip("Example mesh not found")
pc = load_point_cloud_from_mesh(EXAMPLE_MESH, scale=1.0, num_points=2000)
with GraspGenClient(host="localhost", port=5556, wait_for_server=True, timeout_ms=60000) as client:
grasps, confs = client.infer(
pc, num_grasps=100, topk_num_grasps=20,
)
assert grasps.shape[1:] == (4, 4)
assert len(confs) == len(grasps)
================================================
FILE: tests/test_usd_grasp_pipeline.py
================================================
#!/usr/bin/env python3
# Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""
End-to-end test for the USD grasp pipeline:
1. Convert assets/objects/box.obj to a temporary USD file.
2. Load the USD, sample a point cloud, and generate synthetic grasps.
3. Save those grasps as Xform poses back into the USD file.
4. Re-open the USD and verify the grasp poses and confidences round-trip correctly.
"""
import sys
import os
import tempfile
from pathlib import Path
import numpy as np
import pytest
import trimesh
import trimesh.transformations as tra
sys.path.insert(0, str(Path(__file__).parent.parent / "scripts"))
from convert_obj_to_usd import convert_mesh_to_usd
from save_grasps_to_usd import (
GRASPS_ROOT_PATH,
load_grasps_from_usd,
save_grasps_to_usd,
)
ASSETS_DIR = Path(__file__).parent.parent / "assets" / "objects"
BOX_OBJ = ASSETS_DIR / "box.obj"
@pytest.fixture
def box_obj_path():
if not BOX_OBJ.exists():
pytest.skip(f"Test mesh not found at {BOX_OBJ}")
return str(BOX_OBJ)
@pytest.fixture
def tmp_dir():
with tempfile.TemporaryDirectory() as td:
yield td
@pytest.fixture
def box_usd_path(box_obj_path, tmp_dir):
"""Convert box.obj to box.usd in a temp directory."""
usd_path = os.path.join(tmp_dir, "box.usd")
convert_mesh_to_usd(box_obj_path, usd_path, scale=1.0)
assert os.path.exists(usd_path), "USD conversion failed"
return usd_path
def _make_synthetic_grasps(n: int = 10, seed: int = 42):
"""Create N random but valid 4x4 grasp transforms and confidences."""
rng = np.random.RandomState(seed)
grasps = np.zeros((n, 4, 4))
for i in range(n):
angle = rng.uniform(-np.pi, np.pi)
axis = rng.randn(3)
axis /= np.linalg.norm(axis)
R = tra.rotation_matrix(angle, axis)
T = tra.translation_matrix(rng.uniform(-0.1, 0.1, size=3))
grasps[i] = T @ R
confidences = rng.uniform(0.0, 1.0, size=n).astype(np.float32)
return grasps, confidences
# ─── Tests ──────────────────────────────────────────────────────────────────
class TestObjToUsdConversion:
"""Verify OBJ -> USD conversion preserves geometry."""
def test_usd_file_created(self, box_usd_path):
assert os.path.exists(box_usd_path)
assert os.path.getsize(box_usd_path) > 0
def test_usd_mesh_matches_obj(self, box_obj_path, box_usd_path):
import scene_synthesizer as synth
obj_mesh = trimesh.load(box_obj_path)
usd_asset = synth.Asset(box_usd_path)
usd_mesh = usd_asset.mesh()
assert obj_mesh.vertices.shape == usd_mesh.vertices.shape, (
f"Vertex count mismatch: OBJ {obj_mesh.vertices.shape} vs USD {usd_mesh.vertices.shape}"
)
assert obj_mesh.faces.shape == usd_mesh.faces.shape, (
f"Face count mismatch: OBJ {obj_mesh.faces.shape} vs USD {usd_mesh.faces.shape}"
)
np.testing.assert_allclose(
np.sort(obj_mesh.vertices, axis=0),
np.sort(usd_mesh.vertices, axis=0),
atol=1e-5,
err_msg="Vertex positions don't match between OBJ and USD",
)
class TestSaveGraspsToUsd:
"""Verify grasps can be written to and read from a USD file."""
def test_save_and_load_roundtrip(self, box_usd_path):
grasps, confs = _make_synthetic_grasps(n=15)
save_grasps_to_usd(box_usd_path, grasps, confs)
loaded_grasps, loaded_confs = load_grasps_from_usd(box_usd_path)
assert loaded_grasps.shape == grasps.shape, (
f"Grasp shape mismatch: expected {grasps.shape}, got {loaded_grasps.shape}"
)
assert loaded_confs.shape == confs.shape, (
f"Confidence shape mismatch: expected {confs.shape}, got {loaded_confs.shape}"
)
np.testing.assert_allclose(
loaded_grasps, grasps, atol=1e-6,
err_msg="Grasp transforms don't round-trip through USD",
)
np.testing.assert_allclose(
loaded_confs, confs, atol=1e-6,
err_msg="Grasp confidences don't round-trip through USD",
)
def test_grasp_transforms_are_valid_homogeneous(self, box_usd_path):
grasps, confs = _make_synthetic_grasps(n=5)
save_grasps_to_usd(box_usd_path, grasps, confs)
loaded_grasps, _ = load_grasps_from_usd(box_usd_path)
for i, g in enumerate(loaded_grasps):
np.testing.assert_allclose(
g[3, :], [0, 0, 0, 1], atol=1e-7,
err_msg=f"Grasp {i} has invalid bottom row: {g[3, :]}",
)
R = g[:3, :3]
np.testing.assert_allclose(
R @ R.T, np.eye(3), atol=1e-5,
err_msg=f"Grasp {i} rotation is not orthogonal",
)
np.testing.assert_allclose(
np.linalg.det(R), 1.0, atol=1e-5,
err_msg=f"Grasp {i} rotation determinant != 1",
)
def test_overwrite_replaces_old_grasps(self, box_usd_path):
grasps1, confs1 = _make_synthetic_grasps(n=10, seed=1)
save_grasps_to_usd(box_usd_path, grasps1, confs1)
grasps2, confs2 = _make_synthetic_grasps(n=3, seed=2)
save_grasps_to_usd(box_usd_path, grasps2, confs2)
loaded, _ = load_grasps_from_usd(box_usd_path)
assert len(loaded) == 3, (
f"Expected 3 grasps after overwrite, got {len(loaded)}"
)
def test_save_to_separate_output(self, box_usd_path, tmp_dir):
grasps, confs = _make_synthetic_grasps(n=5)
output_path = os.path.join(tmp_dir, "box_with_grasps.usd")
save_grasps_to_usd(box_usd_path, grasps, confs, output_path=output_path)
assert os.path.exists(output_path)
loaded, _ = load_grasps_from_usd(output_path)
assert len(loaded) == 5
original, _ = load_grasps_from_usd(box_usd_path)
assert len(original) == 0, "Original USD should be unchanged"
class TestUsdGraspPrimStructure:
"""Verify the USD prim hierarchy is correct when grasps are saved."""
def test_grasps_root_exists(self, box_usd_path):
from pxr import Usd
grasps, confs = _make_synthetic_grasps(n=3)
save_grasps_to_usd(box_usd_path, grasps, confs)
stage = Usd.Stage.Open(box_usd_path)
root_prim = stage.GetPrimAtPath(GRASPS_ROOT_PATH)
assert root_prim, f"Grasps root prim not found at {GRASPS_ROOT_PATH}"
assert root_prim.GetTypeName() == "Xform"
def test_each_grasp_is_xform_with_confidence(self, box_usd_path):
from pxr import Usd, UsdGeom
grasps, confs = _make_synthetic_grasps(n=5)
save_grasps_to_usd(box_usd_path, grasps, confs)
stage = Usd.Stage.Open(box_usd_path)
root_prim = stage.GetPrimAtPath(GRASPS_ROOT_PATH)
children = list(root_prim.GetChildren())
assert len(children) == 5
for i, child in enumerate(children):
assert child.GetTypeName() == "Xform", (
f"Grasp prim {child.GetPath()} is {child.GetTypeName()}, expected Xform"
)
conf_attr = child.GetAttribute("graspgen:confidence")
assert conf_attr, f"Missing confidence attribute on {child.GetPath()}"
assert isinstance(conf_attr.Get(), float)
def test_object_mesh_preserved(self, box_usd_path):
"""Saving grasps should not destroy the existing mesh geometry."""
import scene_synthesizer as synth
mesh_before = synth.Asset(box_usd_path).mesh()
grasps, confs = _make_synthetic_grasps(n=10)
save_grasps_to_usd(box_usd_path, grasps, confs)
mesh_after = synth.Asset(box_usd_path).mesh()
np.testing.assert_allclose(
np.sort(mesh_before.vertices, axis=0),
np.sort(mesh_after.vertices, axis=0),
atol=1e-6,
err_msg="Mesh geometry was corrupted by grasp saving",
)
class TestEndToEndPipeline:
"""Full pipeline: OBJ -> USD -> synthetic grasps -> save -> verify."""
def test_full_pipeline(self, box_obj_path, tmp_dir):
import scene_synthesizer as synth
# Step 1: Convert OBJ to USD
usd_path = os.path.join(tmp_dir, "box_pipeline.usd")
convert_mesh_to_usd(box_obj_path, usd_path)
# Step 2: Load mesh from USD and sample point cloud
asset = synth.Asset(usd_path)
mesh = asset.mesh()
xyz, _ = trimesh.sample.sample_surface(mesh, 2000)
xyz = np.array(xyz)
assert xyz.shape == (2000, 3)
# Step 3: Generate synthetic grasps (standing in for real inference)
grasps, confs = _make_synthetic_grasps(n=20, seed=99)
# Step 4: Save grasps into the USD
output_usd = os.path.join(tmp_dir, "box_pipeline_grasps.usd")
save_grasps_to_usd(usd_path, grasps, confs, output_path=output_usd)
# Step 5: Re-open and verify everything
loaded_grasps, loaded_confs = load_grasps_from_usd(output_usd)
assert loaded_grasps.shape == (20, 4, 4)
assert loaded_confs.shape == (20,)
np.testing.assert_allclose(loaded_grasps, grasps, atol=1e-6)
np.testing.assert_allclose(loaded_confs, confs, atol=1e-6)
# Verify mesh still intact
mesh_after = synth.Asset(output_usd).mesh()
np.testing.assert_allclose(
np.sort(mesh.vertices, axis=0),
np.sort(mesh_after.vertices, axis=0),
atol=1e-5,
)
print(
f"Pipeline OK: {len(loaded_grasps)} grasps saved, "
f"confidences [{loaded_confs.min():.3f}, {loaded_confs.max():.3f}], "
f"mesh {mesh_after.vertices.shape[0]} vertices preserved"
)
if __name__ == "__main__":
pytest.main([__file__, "-v"])
================================================
FILE: tutorials/TUTORIAL.md
================================================
# Tutorial: Training a GraspGen model on a single object
This is a minimal tutorial demonstrating how to generate a dataset and train a GraspGen (generator and discriminator) models for a single object using a suction cup gripper. This tutorial skips the advanced concepts such as On-Generator training. The model trained in this tutorial will generalize to unknown poses of the same object, but will be overfit to just the one object used for training.
## 📋 Contents
1. [Prerequisites](#prerequisites)
2. [Step 1: Generate Dataset](#step-1-generate-dataset)
3. [Step 2: Train Generator Model](#step-2-train-generator-model)
4. [Step 3: Train Discriminator Model](#step-3-train-discriminator-model)
5. [Step 4: Create Model Checkpoints for Inference](#step-4-create-model-checkpoints-for-inference)
6. [Step 5: Visualize Predictions](#step-5-visualize-predictions)
7. [Complete Workflow Example](#complete-workflow-example)
8. [File Structure](#file-structure)
## Prerequisites
1. **Docker Setup**: Follow the prerequisites in the [main README](../README.md) to start the Docker container suitable for training. Make sure to have [downloaded the model checkpoints](#download-checkpoints) as this repo (located at ``) contains the sample mesh data used in this tutorial:
```bash
# Create results directory
mkdir -p
# Start Docker container
./docker/run.sh --grasp_dataset --object_dataset --results --models
```
2. **Object Mesh**: In this tutorial we use the object `/models/sample_data/meshes/box.obj`. But you can use any other 3D mesh file (a `.obj`, `.stl` file).
## Step 1: Generate Dataset
Use the `generate_dataset_suction_single_object.py` script to create a dataset for your single object.
### Basic Usage:
```bash
cd /code && python tutorials/generate_dataset_suction_single_object.py \
--object_path /models/sample_data/meshes/box.obj \
--object_scale 1.0 \
--output_dir /results/tutorial \
--no_visualization
```
### Parameters:
- `--object_path`: Path to your object mesh file (required)
- `--output_dir`: Directory to save datasets (default: `/results/tutorial`)
- `--num_grasps`: Total number of grasps to generate (default: 2000)
- `--object_scale`: Scale factor for the object (default: 1.0)
- `--gripper_config`: Gripper configuration file (default: `single_suction_cup_30mm.yaml`)
- `--num_disturbances`: Number of disturbance samples for evaluation (default: 10)
- `--no_visualization`: Disable visualization
### Output:
The script creates the following dataset with the directory structure. It follows the convention in the [GraspGen Dataset Format](../docs/GRASP_DATASET_FORMAT.md).
```
/results/tutorial/
├── tutorial_object_dataset/
│ ├── .obj
│ ├── train.txt
│ └── valid.txt
└── tutorial_grasp_dataset/
└── _grasps.json
```
## Step 2: Train Generator Model
Train a diffusion model to generate grasp poses for your object.
### Command:
```bash
cd /code && bash tutorials/tutorial_train_gen.sh
```
### Expected Output:
- Training logs in `/results/tutorial/logs/single_suction_cup_30mm_gen_test/`
- Model checkpoints saved as `last.pth`
- Cache files in `/results/tutorial/cache/`
### Monitoring Training:
- For best perf, the validation the grasp reconstruction error `reconstruction/error_trans_l2` should be a few cm
- This run may take at least 1K epochs to converge. However, for a large object dataset (e.g. set of 8K objects), it takes about 3-5K epochs to converge.
## Step 3: Train Discriminator Model
Train a discriminator model to evaluate grasp quality.
### Command:
```bash
cd /code && bash tutorials/tutorial_train_dis.sh
```
### Expected Output:
- Training logs in `/results/tutorial/logs/single_suction_cup_30mm_dis_test/`
- Model checkpoints saved as `last.pth`
- For best perf, Validation AP score should be > 0.8; `bce_topk` loss should go down
### Monitoring Training:
- Watch for validation AP score (should be > 0.8)
- This run may take at least 1K epochs to converge. However, for a larger object dataset (e.g. set of 8K objects), it takes about 3-5K epochs to converge.
- Monitor losses for different grasp types (pos, neg, freespace, onpolicy)
- Check that all loss curves are decreasing
## Step 4: Create Final Model config file for Inference
After training both models, create standardized checkpoints for inference:
### Command:
```bash
cd /code && python tutorials/generate_model_inference_config.py \
--gen_log_dir /results/tutorial/logs/single_suction_cup_30mm_gen_test \
--dis_log_dir /results/tutorial/logs/single_suction_cup_30mm_dis_test
```
### Expected Output:
- Models directory: `/results/tutorial/models/`
- Generator checkpoint: `gen.pth`
- Discriminator checkpoint: `dis.pth`
## Step 5: Visualize Predictions
Visualize grasp predictions from your trained model on the sample object mesh.
### Command:
```bash
cd /code && python scripts/demo_object_mesh.py \
--mesh_file /models/sample_data/meshes/box.obj \
--mesh_scale 1.0 \
--gripper_config /results/tutorial/models/tutorial_model_config.yaml
```
### Parameters:
- `--mesh_file`: Path to the object mesh file (using the same box.obj from the example)
- `--mesh_scale`: Scale factor for the object mesh (default: 1.0)
- `--gripper_config`: Path to the config file created in Step 4 (default: `/results/tutorial/models/tutorial_model_config.yaml`)
### Expected Output:
- Interactive 3D visualization showing the object mesh
- Generated grasp poses overlaid on the object
- Color-coded grasp quality scores
- Best grasps highlighted
## Complete Workflow Example
Here's a complete example for training on a mug object:
```bash
# 1. Generate dataset
cd /code && python tutorials/generate_dataset_suction_single_object.py \
--object_path /path/to/mug.obj \
--output_dir /results/tutorial \
--num_grasps 2000 \
--object_scale 1.0
# 2. Train generator
cd /code && bash tutorials/tutorial_gen.sh
# 3. Train discriminator
cd /code && bash tutorials/tutorial_dis.sh
# 4. Create model checkpoints for inference
cd /code && python tutorials/create_model_checkpoints_for_inference.py \
--gen_log_dir /results/tutorial/logs/single_suction_cup_30mm_gen_test \
--dis_log_dir /results/tutorial/logs/single_suction_cup_30mm_dis_test
# 5. Visualize predictions
cd /code && python scripts/demo_object_mesh.py \
--mesh_file /models/sample_data/meshes/box.obj \
--mesh_scale 1.0 \
--gripper_config /results/tutorial/models/tutorial_model_config.yaml
```
## File Structure
After running all scripts, you'll have:
```
/results/tutorial/
├── tutorial_object_dataset/
│ ├── mug.obj
│ ├── train.txt
│ └── valid.txt
├── tutorial_grasp_dataset/
│ └── mug_grasps.json
├── logs/
│ ├── single_suction_cup_30mm_gen_test/
│ │ ├── last.pth
│ │ └── training_logs/
│ └── single_suction_cup_30mm_dis_test/
│ ├── last.pth
│ └── training_logs/
├── models/
│ ├── gen.pth
│ ├── dis.pth
│ └── tutorial_model_config.yaml
└── cache/
└── tutorial_object_dataset/
├── cache_train_meshandpc_gen.h5
└── cache_valid_meshandpc_gen.h5
```
================================================
FILE: tutorials/generate_dataset_suction_single_object.py
================================================
# Copyright (c) 2025, 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.
"""
Tutorial: Generate Dataset for Single Object Suction Grasp Training
This script demonstrates how to generate a dataset for training a generator and discriminator
model for a single object using a suction cup gripper. The dataset follows the GraspGen
convention and can be used for training neural networks to predict grasp poses.
Usage:
python generate_dataset_suction_single_object.py \
--object_path /path/to/object.obj \
--output_dir /results \
--num_grasps 2000
"""
import os
import sys
import json
import argparse
import numpy as np
import trimesh
import trimesh.transformations as tra
from pathlib import Path
# Add the parent directory to the path to import grasp_gen modules
sys.path.append(str(Path(__file__).parent.parent))
from grasp_gen.dataset.suction import SuctionCupArray
def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(
description="Generate dataset for single object suction grasp training",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--object_path",
type=str,
required=True,
help="Path to the object mesh file (obj, stl, or ply)",
)
parser.add_argument(
"--output_dir",
type=str,
default="/results/tutorial",
help="Directory to save the tutorial datasets",
)
parser.add_argument(
"--num_grasps",
type=int,
default=2000,
help="Total number of grasps to generate (positive and negative)",
)
parser.add_argument(
"--object_scale",
type=float,
default=1.0,
help="Scale factor to apply to the object mesh",
)
parser.add_argument(
"--gripper_config",
type=str,
default="config/grippers/single_suction_cup_30mm.yaml",
help="Path to gripper configuration file",
)
parser.add_argument(
"--num_disturbances",
type=int,
default=10,
help="Number of random disturbance samples for evaluation",
)
parser.add_argument(
"--qp_solver",
type=str,
default="clarabel",
choices=("clarabel", "cvxopt", "daqp", "ecos", "osqp", "scs"),
help="QP solver to use for wrench resistance evaluation",
)
parser.add_argument(
"--random_seed", type=int, default=42, help="Random seed for reproducibility"
)
parser.add_argument(
"--no_visualization", action="store_true", help="Disable visualization"
)
return parser.parse_args()
def setup_directories(output_dir, object_basename):
"""Create the required directory structure for the tutorial datasets."""
# Create tutorial object dataset directory
tutorial_object_dir = Path(output_dir) / "tutorial_object_dataset"
tutorial_object_dir.mkdir(parents=True, exist_ok=True)
# Create tutorial grasp dataset directory
tutorial_grasp_dir = Path(output_dir) / "tutorial_grasp_dataset"
tutorial_grasp_dir.mkdir(parents=True, exist_ok=True)
return tutorial_object_dir, tutorial_grasp_dir
def copy_object_to_dataset(
object_path, tutorial_object_dir, object_basename, scale=1.0
):
"""Copy and scale the object mesh to the tutorial dataset directory."""
# Load the object mesh
obj_mesh = trimesh.load(object_path)
# Apply scale if needed
if scale != 1.0:
obj_mesh.apply_scale(scale)
# Save to tutorial object dataset
output_object_path = tutorial_object_dir / f"{object_basename}.obj"
obj_mesh.export(str(output_object_path))
print(f"Object saved to: {output_object_path}")
return str(output_object_path), obj_mesh
def generate_grasps(obj_mesh, suction_gripper, num_grasps, num_disturbances, qp_solver):
"""Generate and evaluate grasps for the object."""
print(f"Generating {num_grasps} grasps...")
# Sample grasps
points_on_surface, approach_vectors, grasp_transforms = (
suction_gripper.sample_grasps(obj_mesh=obj_mesh, num_grasps=num_grasps)
)
# Evaluate grasps
print("Evaluating grasps...")
points, approach_vectors, contact_transforms, sealed, success, in_collision = (
suction_gripper.evaluate_grasps(
obj_mesh=obj_mesh,
points_on_surface=points_on_surface,
approach_vectors=approach_vectors,
grasp_transforms=grasp_transforms,
num_disturbances=num_disturbances,
qp_solver=qp_solver,
tqdm_disable=False,
)
)
return points, approach_vectors, contact_transforms, sealed, success, in_collision
def create_grasp_dataset_json(
object_file,
object_scale,
contact_transforms,
sealed,
success,
in_collision,
tutorial_grasp_dir,
object_basename,
obj_center_mass,
):
"""Create the grasp dataset JSON file following GraspGen convention."""
# Assert that obj_center_mass is a 3D vector
assert (
len(obj_center_mass) == 3
), f"obj_center_mass should be length 3, got {len(obj_center_mass)}"
# Determine successful grasps (sealed, high success rate, and collision-free)
# We'll use a threshold for success rate to create a mix of positive and negative grasps
success_threshold = 0.5 # Grasps with >50% success rate are considered positive
# Create object_in_gripper mask
object_in_gripper = np.logical_and.reduce(
[
sealed, # Must be sealed
# ~in_collision # Must not be in collision; TODO - check if the collision mesh is specified correctly
]
)
# Transform grasps back to original object frame (add back center of mass)
output_transforms = np.copy(contact_transforms)
output_transforms[:, :3, 3] += obj_center_mass
# Create the dataset dictionary
dataset_dict = {
"object": {
"file": object_file, # Relative path to object in tutorial dataset
"scale": object_scale,
},
"grasps": {
"transforms": output_transforms.tolist(),
"object_in_gripper": object_in_gripper.tolist(),
},
}
# Save to JSON file
output_json_path = tutorial_grasp_dir / f"{object_basename}_grasps.json"
with open(output_json_path, "w") as f:
json.dump(dataset_dict, f, indent=2)
print(f"Grasp dataset saved to: {output_json_path}")
print(f"Total grasps: {len(object_in_gripper)}")
print(f"Positive grasps: {sum(object_in_gripper)}")
print(f"Negative grasps: {sum(~object_in_gripper)}")
return str(output_json_path)
def create_splits_file(tutorial_object_dir, tutorial_grasp_dir, object_basename):
"""Create train.txt and valid.txt files for the tutorial dataset."""
# Create train.txt (include the object)
train_txt_path = tutorial_object_dir / "train.txt"
with open(train_txt_path, "w") as f:
f.write(f"{object_basename}.obj\n")
# Create valid.txt (include the same object for validation)
valid_txt_path = tutorial_object_dir / "valid.txt"
with open(valid_txt_path, "w") as f:
f.write(f"{object_basename}.obj\n")
print(f"Split files created:")
print(f" Train: {train_txt_path}")
print(f" Valid: {valid_txt_path}")
def main():
"""Main function to generate the tutorial dataset."""
args = parse_args()
# Set random seed for reproducibility
np.random.seed(args.random_seed)
# Validate inputs
if not os.path.exists(args.object_path):
raise FileNotFoundError(f"Object file not found: {args.object_path}")
if not os.path.exists(args.gripper_config):
raise FileNotFoundError(f"Gripper config not found: {args.gripper_config}")
# Get object basename
object_basename = Path(args.object_path).stem
print(f"Generating dataset for object: {object_basename}")
print(f"Object path: {args.object_path}")
print(f"Output directory: {args.output_dir}")
print(f"Number of grasps: {args.num_grasps}")
print(f"Object scale: {args.object_scale}")
# Setup directories
tutorial_object_dir, tutorial_grasp_dir = setup_directories(
args.output_dir, object_basename
)
# Copy object to dataset
object_file, obj_mesh = copy_object_to_dataset(
args.object_path, tutorial_object_dir, object_basename, args.object_scale
)
# Make object_file a relative path with respect to tutorial_object_dir
object_file = f"{object_basename}.obj"
# Center the object at origin
obj_center_mass = obj_mesh.center_mass
obj_mesh.apply_translation(-obj_center_mass)
print(f"Object centered at origin. Original CoM: {obj_center_mass}")
# Initialize suction gripper
print(f"Loading gripper configuration: {args.gripper_config}")
suction_gripper = SuctionCupArray.from_file(fname=args.gripper_config)
# Generate grasps
points, approach_vectors, contact_transforms, sealed, success, in_collision = (
generate_grasps(
obj_mesh=obj_mesh,
suction_gripper=suction_gripper,
num_grasps=args.num_grasps,
num_disturbances=args.num_disturbances,
qp_solver=args.qp_solver,
)
)
# Create grasp dataset JSON
grasp_json_path = create_grasp_dataset_json(
object_file=object_file,
object_scale=args.object_scale,
contact_transforms=contact_transforms,
sealed=sealed,
success=success,
in_collision=in_collision,
tutorial_grasp_dir=tutorial_grasp_dir,
object_basename=object_basename,
obj_center_mass=obj_center_mass,
)
# Create split files
create_splits_file(tutorial_object_dir, tutorial_grasp_dir, object_basename)
# Optional visualization
if not args.no_visualization:
print("\nVisualizing results...")
from grasp_gen.utils.meshcat_utils import (
create_visualizer,
visualize_mesh,
visualize_pointcloud,
visualize_grasp,
)
vis = create_visualizer()
# Visualize object
visualize_mesh(vis, "object", obj_mesh, color=[169, 169, 169])
# Visualize grasp points
from grasp_gen.dataset.suction import colorize_for_meshcat
grasp_colors = colorize_for_meshcat(success)
visualize_pointcloud(vis, "grasp_points", points, grasp_colors, size=0.005)
# Visualize top 10 grasps
combined_criteria = sealed * success
top_indices = np.argsort(combined_criteria)[-10:][::-1]
for i, idx in enumerate(top_indices):
if combined_criteria[idx] > 0:
color = [255, 0, 0] if i == 0 else [255, 165, 0]
visualize_grasp(
vis,
f"top_grasps/grasp_{i:02d}",
contact_transforms[idx],
color=color,
gripper_name="single_suction_cup_30mm",
)
print("Visualization complete. Check meshcat browser window.")
input("Press Enter to continue...")
print(f"\nTutorial dataset generation complete!")
print(f"Object dataset: {tutorial_object_dir}")
print(f"Grasp dataset: {tutorial_grasp_dir}")
print(f"Ready for training generator and discriminator models!")
if __name__ == "__main__":
main()
================================================
FILE: tutorials/generate_model_inference_config.py
================================================
# Copyright (c) 2025, 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.
"""
Tutorial: Create Model Checkpoints for Inference
This script copies the trained generator and discriminator checkpoints to a models directory
for easy access during inference. It organizes the checkpoints with standardized names and
creates a config file with updated checkpoint paths.
Usage:
python create_model_checkpoints_for_inference.py \
--gen_log_dir /results/tutorial/logs/single_suction_cup_30mm_gen_test \
--dis_log_dir /results/tutorial/logs/single_suction_cup_30mm_dis_test \
--models_dir /results/tutorial/models
"""
import os
import shutil
import argparse
import yaml
from pathlib import Path
def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(
description="Create model checkpoints for inference",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"--gen_log_dir",
type=str,
required=True,
help="Path to generator training log directory",
)
parser.add_argument(
"--dis_log_dir",
type=str,
required=True,
help="Path to discriminator training log directory",
)
parser.add_argument(
"--models_dir",
type=str,
default="/results/tutorial/models",
help="Output directory for model checkpoints and config",
)
return parser.parse_args()
def copy_checkpoint(src_path, dst_path, checkpoint_name):
"""Copy a checkpoint file from source to destination."""
if not os.path.exists(src_path):
raise FileNotFoundError(f"Checkpoint not found: {src_path}")
# Create destination directory if it doesn't exist
os.makedirs(os.path.dirname(dst_path), exist_ok=True)
# Copy the file
shutil.copy2(src_path, dst_path)
print(f"Copied {checkpoint_name} checkpoint:")
print(f" From: {src_path}")
print(f" To: {dst_path}")
def create_config_file(gen_log_dir, dis_log_dir, models_dir):
"""Create config file with updated checkpoint paths and generator diffusion settings."""
# Source config file paths
gen_config_path = os.path.join(gen_log_dir, "config.yaml")
dis_config_path = os.path.join(dis_log_dir, "config.yaml")
if not os.path.exists(gen_config_path):
raise FileNotFoundError(f"Generator config file not found: {gen_config_path}")
if not os.path.exists(dis_config_path):
raise FileNotFoundError(
f"Discriminator config file not found: {dis_config_path}"
)
# Destination config file path
dst_config_path = os.path.join(models_dir, "tutorial_model_config.yaml")
# Read the generator config to get diffusion settings
with open(gen_config_path, "r") as f:
gen_config = yaml.safe_load(f)
# Read the discriminator config (base config)
with open(dis_config_path, "r") as f:
config = yaml.safe_load(f)
# Copy diffusion settings from generator config
if "diffusion" in gen_config:
config["diffusion"] = gen_config["diffusion"]
print(f"Copied diffusion settings from generator config")
else:
print(f"Warning: No diffusion settings found in generator config")
# Override the checkpoint paths
if "eval" not in config:
config["eval"] = {}
config["eval"]["checkpoint"] = "gen.pth"
config["eval"]["model_name"] = "diffusion-discriminator"
if "discriminator" not in config:
config["discriminator"] = {}
config["discriminator"]["checkpoint"] = "dis.pth"
# Create models directory if it doesn't exist
os.makedirs(models_dir, exist_ok=True)
# Write the updated config
with open(dst_config_path, "w") as f:
yaml.dump(config, f, default_flow_style=False, sort_keys=False)
print(f"Created config file:")
print(f" Generator config: {gen_config_path}")
print(f" Discriminator config: {dis_config_path}")
print(f" Output: {dst_config_path}")
print(f" Updated: eval.checkpoint = 'gen.pth'")
print(f" Updated: discriminator.checkpoint = 'dis.pth'")
print(f" Updated: diffusion settings from generator config")
def main():
"""Main function to create model checkpoints for inference."""
args = parse_args()
# Define source and destination paths
gen_src_path = os.path.join(args.gen_log_dir, "last.pth")
dis_src_path = os.path.join(args.dis_log_dir, "last.pth")
gen_dst_path = os.path.join(args.models_dir, "gen.pth")
dis_dst_path = os.path.join(args.models_dir, "dis.pth")
print("Creating model checkpoints for inference...")
print(f"Models directory: {args.models_dir}")
print()
# Copy generator checkpoint
try:
copy_checkpoint(gen_src_path, gen_dst_path, "Generator")
except FileNotFoundError as e:
print(f"Warning: {e}")
print("Make sure the generator training has completed successfully.")
print()
# Copy discriminator checkpoint
try:
copy_checkpoint(dis_src_path, dis_dst_path, "Discriminator")
except FileNotFoundError as e:
print(f"Warning: {e}")
print("Make sure the discriminator training has completed successfully.")
print()
# Create config file
try:
create_config_file(args.gen_log_dir, args.dis_log_dir, args.models_dir)
except FileNotFoundError as e:
print(f"Warning: {e}")
print("Make sure the discriminator training has completed successfully.")
print()
# Check if all files exist
gen_exists = os.path.exists(gen_dst_path)
dis_exists = os.path.exists(dis_dst_path)
config_exists = os.path.exists(
os.path.join(args.models_dir, "tutorial_model_config.yaml")
)
print("Summary:")
print(f" Generator checkpoint: {'✓' if gen_exists else '✗'}")
print(f" Discriminator checkpoint: {'✓' if dis_exists else '✗'}")
print(f" Config file: {'✓' if config_exists else '✗'}")
if gen_exists and dis_exists and config_exists:
print("\n✓ All files are ready for inference!")
print(f"Models directory: {args.models_dir}")
print("Files:")
print(f" - gen.pth (Generator)")
print(f" - dis.pth (Discriminator)")
print(f" - tutorial_model_config.yaml (Config with updated paths)")
else:
print("\n⚠ Some files are missing. Please ensure training has completed.")
print("\nNext steps:")
print("1. Use gen.pth for grasp generation inference")
print("2. Use dis.pth for grasp quality evaluation")
print("3. Use tutorial_model_config.yaml for inference configuration")
print("4. All models and config are ready for deployment")
if __name__ == "__main__":
main()
================================================
FILE: tutorials/tutorial_train_dis.sh
================================================
#!/usr/bin/env bash
# Fixed parameters
export NGPU=1
export NWORKER=4
export NEPOCH=5000
export BATCH=8
export PRINT_FREQ=10
export PLOT_FREQ=10
export SAVE_FREQ=500
export DATASET_NAME="objaverse"
export DATASET_VERSION="v2"
export NUM_GRASPS_PER_OBJ=300
export POSE_REPR="mlp"
export BACKBONE="ptv3"
export ROTATION_REPR="r3_so3"
export TOPK_RATIO=0.75
export NUM_POINTS=2048
export NOISE_SCALE=1.0
export NAME="meshandpc"
export P_PC=0.50
export RED=7
export GRIPPER_NAME="single_suction_cup_30mm"
export OBJECT_DATASET_DIR="/results/tutorial/tutorial_object_dataset"
export GRASP_DIR="/results/tutorial/tutorial_grasp_dataset"
export CODE_DIR="/code"
export RESULTS_DIR="/results/tutorial"
export GRASP_DATASET_DIR="$GRASP_DIR"
export SPLIT_DATASET_DIR="$OBJECT_DATASET_DIR"
export METHOD="grasp_gen"
export RATIO="[0.25,0.24,0.00,0.01,0.00,0.25,0.25]"
export PYOPENGL_PLATFORM="osmesa"
export LOG_DIR="$RESULTS_DIR/logs/${GRIPPER_NAME}_dis_test"
export CACHE_DIR="$RESULTS_DIR/cache"
export CHECKPOINT="$LOG_DIR/last.pth"
echo "Running Training for $GRIPPER_NAME"
rm -rf $LOG_DIR
mkdir -p $LOG_DIR
mkdir -p $CACHE_DIR
cd $CODE_DIR && pip install -e . && cd $CODE_DIR/scripts && \
python train_graspgen.py \
data.num_points=$NUM_POINTS \
data.load_contact=False \
data.dataset_cls="ObjectPickDataset" \
data.rotation_augmentation=True \
data.cache_dir=$CACHE_DIR \
data.root_dir=$SPLIT_DATASET_DIR \
data.object_root_dir=$OBJECT_DATASET_DIR \
data.grasp_root_dir=$GRASP_DATASET_DIR \
data.dataset_name=$DATASET_NAME \
data.dataset_version=$DATASET_VERSION \
data.prob_point_cloud=$P_PC \
data.redundancy=$RED \
data.gripper_name=$GRIPPER_NAME \
train.log_dir=$LOG_DIR \
train.batch_size=$BATCH \
train.num_gpus=$NGPU \
train.num_epochs=$NEPOCH \
train.num_workers=$NWORKER \
train.print_freq=$PRINT_FREQ \
train.plot_freq=$PLOT_FREQ \
train.save_freq=$SAVE_FREQ \
train.checkpoint=$CHECKPOINT \
train.model_name='discriminator' \
train.debug=True \
optimizer.type="ADAMW" \
optimizer.grad_clip=-1 \
optimizer.lr=0.00001 \
discriminator.gripper_name=$GRIPPER_NAME \
discriminator.topk_ratio=$TOPK_RATIO \
discriminator.obs_backbone=$BACKBONE \
discriminator.grasp_repr=$ROTATION_REPR \
discriminator.pose_repr=$POSE_REPR \
discriminator.kappa=$NOISE_SCALE \
discriminator.ptv3.grid_size=0.01 \
data.num_grasps_per_object=$NUM_GRASPS_PER_OBJ \
data.load_discriminator_dataset=True \
data.discriminator_ratio=$RATIO
================================================
FILE: tutorials/tutorial_train_gen.sh
================================================
#!/usr/bin/env bash
# Fixed parameters
export NGPU=1
export NWORKER=4
export NEPOCH=5000
export BATCH=8
export PRINT_FREQ=10
export PLOT_FREQ=10
export SAVE_FREQ=500
export DATASET_NAME="objaverse"
export DATASET_VERSION="v2"
export TIMESTEPS=10
export NUM_GRASPS_PER_OBJ=500
export BACKBONE="ptv3"
export NUM_POINTS=3500
export NAME="meshandpc"
export P_PC=0.50
export RED=7
export GRIPPER_NAME="single_suction_cup_30mm"
export OBJECT_DATASET_DIR="/results/tutorial/tutorial_object_dataset"
export GRASP_DIR="/results/tutorial/tutorial_grasp_dataset"
export CODE_DIR="/code"
export RESULTS_DIR="/results/tutorial"
export GRASP_DATASET_DIR="$GRASP_DIR"
export SPLIT_DATASET_DIR="$OBJECT_DATASET_DIR"
export METHOD="grasp_gen"
export PYRENDER_INSTALL_PREFIX="apt-get update -y && apt-get install -y tmux libosmesa6-dev && pip install pyrender && pip install PyOpenGL==3.1.5 && export PYOPENGL_PLATFORM=osmesa && "
export ROTATION_REPR="r3_so3"
export PYOPENGL_PLATFORM="osmesa"
export NOISE_SCALE=1.0
export LOG_DIR="$RESULTS_DIR/logs/${GRIPPER_NAME}_gen_test"
export CHECKPOINT="$LOG_DIR/last.pth"
export CACHE_DIR="$RESULTS_DIR/cache"
echo "Running Training for $GRIPPER_NAME"
rm -rf $LOG_DIR
mkdir -p $LOG_DIR
mkdir -p $CACHE_DIR
cd $CODE_DIR && pip install -e . && cd $CODE_DIR/scripts && \
python train_graspgen.py \
data.num_points=$NUM_POINTS \
data.load_contact=False \
data.dataset_cls="ObjectPickDataset" \
data.rotation_augmentation=True \
data.cache_dir=$CACHE_DIR \
data.root_dir=$SPLIT_DATASET_DIR \
data.object_root_dir=$OBJECT_DATASET_DIR \
data.grasp_root_dir=$GRASP_DATASET_DIR \
data.dataset_name=$DATASET_NAME \
data.dataset_version=$DATASET_VERSION \
data.prob_point_cloud=$P_PC \
data.redundancy=$RED \
data.gripper_name=$GRIPPER_NAME \
train.log_dir=$LOG_DIR \
train.batch_size=$BATCH \
train.num_gpus=$NGPU \
train.num_epochs=$NEPOCH \
train.num_workers=$NWORKER \
train.print_freq=$PRINT_FREQ \
train.plot_freq=$PLOT_FREQ \
train.save_freq=$SAVE_FREQ \
train.checkpoint=$CHECKPOINT \
train.model_name='diffusion' \
train.debug=True \
optimizer.type="ADAMW" \
optimizer.lr=0.00001 \
optimizer.grad_clip=-1 \
diffusion.gripper_name=$GRIPPER_NAME \
diffusion.num_diffusion_iters=$TIMESTEPS \
diffusion.num_diffusion_iters_eval=$TIMESTEPS \
diffusion.obs_backbone=$BACKBONE \
diffusion.grasp_repr=$ROTATION_REPR \
diffusion.attention='cat_attn' \
diffusion.compositional_schedular=True \
diffusion.loss_pointmatching=False \
diffusion.loss_l1_pos=True \
diffusion.loss_l1_rot=True \
diffusion.ptv3.grid_size=0.01 \
diffusion.pose_repr='mlp' \
data.num_grasps_per_object=$NUM_GRASPS_PER_OBJ \
data.load_discriminator_dataset=False \
data.visualize_batch=False \
diffusion.kappa=$NOISE_SCALE