file_path stringlengths 20 202 | content stringlengths 9 3.85M | size int64 9 3.85M | lang stringclasses 9
values | avg_line_length float64 3.33 100 | max_line_length int64 8 993 | alphanum_fraction float64 0.26 0.93 |
|---|---|---|---|---|---|---|
NVlabs/ACID/ACID/src/training.py | import numpy as np
from collections import defaultdict
from tqdm import tqdm
class BaseTrainer(object):
''' Base trainer class.
'''
def evaluate(self, val_loader):
''' Performs an evaluation.
Args:
val_loader (dataloader): pytorch dataloader
'''
eval_list = def... | 988 | Python | 23.724999 | 65 | 0.571862 |
NVlabs/ACID/ACID/src/common.py | # import multiprocessing
import torch
import numpy as np
import math
import numpy as np
def compute_iou(occ1, occ2):
''' Computes the Intersection over Union (IoU) value for two sets of
occupancy values.
Args:
occ1 (tensor): first set of occupancy values
occ2 (tensor): second set of occupa... | 11,186 | Python | 29.399456 | 109 | 0.562846 |
NVlabs/ACID/ACID/src/config.py | import yaml
from torchvision import transforms
from src import data
from src import conv_onet
method_dict = {
'conv_onet': conv_onet
}
# General config
def load_config(path, default_path=None):
''' Loads config file.
Args:
path (str): path to config file
default_path (bool): whether t... | 2,573 | Python | 23.990291 | 76 | 0.624563 |
NVlabs/ACID/ACID/src/checkpoints.py | import os
import urllib
import torch
from torch.utils import model_zoo
class CheckpointIO(object):
''' CheckpointIO class.
It handles saving and loading checkpoints.
Args:
checkpoint_dir (str): path where checkpoints are saved
'''
def __init__(self, checkpoint_dir='./chkpts', **kwargs):
... | 2,962 | Python | 28.63 | 70 | 0.568535 |
NVlabs/ACID/ACID/src/layers.py | import torch
import torch.nn as nn
# Resnet Blocks
class ResnetBlockFC(nn.Module):
''' Fully connected ResNet Block class.
Args:
size_in (int): input dimension
size_out (int): output dimension
size_h (int): hidden dimension
'''
def __init__(self, size_in, size_out=None, size_... | 1,203 | Python | 24.083333 | 68 | 0.532835 |
NVlabs/ACID/ACID/src/conv_onet/training.py | import os
import numpy as np
import torch
from torch.nn import functional as F
from src.common import compute_iou
from src.utils import common_util, plushsim_util
from src.training import BaseTrainer
from sklearn.metrics import roc_curve
from scipy import interp
import matplotlib.pyplot as plt
from collections import d... | 15,474 | Python | 42.105849 | 109 | 0.511439 |
NVlabs/ACID/ACID/src/conv_onet/config.py | import os
from src.encoder import encoder_dict
from src.conv_onet import models, training
from src.conv_onet import generation
from src import data
def get_model(cfg,device=None, dataset=None, **kwargs):
if cfg['model']['type'] == 'geom':
return get_geom_model(cfg,device,dataset)
elif cfg['model']['typ... | 4,514 | Python | 29.1 | 77 | 0.597475 |
NVlabs/ACID/ACID/src/conv_onet/__init__.py | from src.conv_onet import (
config, generation, training, models
)
__all__ = [
config, generation, training, models
]
| 127 | Python | 14.999998 | 40 | 0.661417 |
NVlabs/ACID/ACID/src/conv_onet/generation.py | import torch
import torch.optim as optim
from torch import autograd
import numpy as np
from tqdm import trange, tqdm
import trimesh
from src.utils import libmcubes, common_util
from src.common import make_3d_grid, normalize_coord, add_key, coord2index
from src.utils.libmise import MISE
import time
import math
counter ... | 14,928 | Python | 36.044665 | 126 | 0.536509 |
NVlabs/ACID/ACID/src/conv_onet/models/decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from src.layers import ResnetBlockFC
from src.common import normalize_coordinate, normalize_3d_coordinate, map2local
class GeomDecoder(nn.Module):
''' Decoder.
Instead of conditioning on global features, on plane/volume local features.
... | 8,333 | Python | 35.876106 | 114 | 0.554062 |
NVlabs/ACID/ACID/src/conv_onet/models/__init__.py | import torch
import numpy as np
import torch.nn as nn
from torch import distributions as dist
from src.conv_onet.models import decoder
from src.utils import plushsim_util
# Decoder dictionary
decoder_dict = {
'geom_decoder': decoder.GeomDecoder,
'combined_decoder': decoder.CombinedDecoder,
}
class ConvImpDyn(n... | 17,056 | Python | 39.80622 | 113 | 0.525797 |
NVlabs/ACID/ACID/src/encoder/__init__.py | from src.encoder import (
pointnet
)
encoder_dict = {
'geom_encoder': pointnet.GeomEncoder,
}
| 104 | Python | 10.666665 | 41 | 0.663462 |
NVlabs/ACID/ACID/src/encoder/pointnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from src.layers import ResnetBlockFC
from torch_scatter import scatter_mean, scatter_max
from src.common import coordinate2index, normalize_coordinate
from src.encoder.unet import UNet
class GeomEncoder(nn.Module):
''' PointNet-based encoder networ... | 4,654 | Python | 37.791666 | 134 | 0.592609 |
NVlabs/ACID/ACID/src/encoder/unet.py | '''
Codes are from:
https://github.com/jaxony/unet-pytorch/blob/master/model.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import numpy as np
def conv3x3(in_channels, out_channels, stride=1,
... | 8,696 | Python | 32.57915 | 80 | 0.575092 |
NVlabs/ACID/ACID/src/utils/common_util.py | import os
import glob
import json
import scipy
import itertools
import numpy as np
from PIL import Image
from scipy.spatial.transform import Rotation
from sklearn.neighbors import NearestNeighbors
from sklearn.manifold import TSNE
from matplotlib import pyplot as plt
def get_color_map(x):
colours = plt.cm.Spectral... | 12,618 | Python | 36.005865 | 116 | 0.560628 |
NVlabs/ACID/ACID/src/utils/io.py | import os
from plyfile import PlyElement, PlyData
import numpy as np
def export_pointcloud(vertices, out_file, as_text=True):
assert(vertices.shape[1] == 3)
vertices = vertices.astype(np.float32)
vertices = np.ascontiguousarray(vertices)
vector_dtype = [('x', 'f4'), ('y', 'f4'), ('z', 'f4')]
verti... | 3,415 | Python | 29.230088 | 78 | 0.513616 |
NVlabs/ACID/ACID/src/utils/visualize.py | import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import src.common as common
def visualize_data(data, data_type, out_file):
r''' Visualizes the data with regard to its type.
Args:
data (tensor): batch of data
data_type (string): data type (img, v... | 2,378 | Python | 26.66279 | 65 | 0.585786 |
NVlabs/ACID/ACID/src/utils/mentalsim_util.py | import os
import glob
import json
import scipy
import itertools
import numpy as np
from PIL import Image
from scipy.spatial.transform import Rotation
from sklearn.neighbors import NearestNeighbors
########################################################################
# Viewpoint transform
###########################... | 19,039 | Python | 44.118483 | 116 | 0.564578 |
NVlabs/ACID/ACID/src/utils/plushsim_util.py | import os
import glob
import json
import scipy
import itertools
import numpy as np
from PIL import Image
from scipy.spatial.transform import Rotation
from sklearn.neighbors import NearestNeighbors
from .common_util import *
########################################################################
# Some file getters
#... | 20,693 | Python | 43.407725 | 124 | 0.605905 |
NVlabs/ACID/ACID/src/utils/libmise/__init__.py | from .mise import MISE
__all__ = [
MISE
]
| 47 | Python | 6.999999 | 22 | 0.531915 |
NVlabs/ACID/ACID/src/utils/libmise/test.py | import numpy as np
from mise import MISE
import time
t0 = time.time()
extractor = MISE(1, 2, 0.)
p = extractor.query()
i = 0
while p.shape[0] != 0:
print(i)
print(p)
v = 2 * (p.sum(axis=-1) > 2).astype(np.float64) - 1
extractor.update(p, v)
p = extractor.query()
i += 1
if (i >= 8):
... | 456 | Python | 16.576922 | 55 | 0.570175 |
NVlabs/ACID/ACID/src/utils/libsimplify/__init__.py | from .simplify_mesh import (
mesh_simplify
)
import trimesh
def simplify_mesh(mesh, f_target=10000, agressiveness=7.):
vertices = mesh.vertices
faces = mesh.faces
vertices, faces = mesh_simplify(vertices, faces, f_target, agressiveness)
mesh_simplified = trimesh.Trimesh(vertices, faces, process=... | 355 | Python | 21.249999 | 77 | 0.723944 |
NVlabs/ACID/ACID/src/utils/libsimplify/test.py | from simplify_mesh import mesh_simplify
import numpy as np
v = np.random.rand(100, 3)
f = np.random.choice(range(100), (50, 3))
mesh_simplify(v, f, 50) | 153 | Python | 20.999997 | 41 | 0.705882 |
NVlabs/ACID/ACID/src/utils/libsimplify/Simplify.h | /////////////////////////////////////////////
//
// Mesh Simplification Tutorial
//
// (C) by Sven Forstmann in 2014
//
// License : MIT
// http://opensource.org/licenses/MIT
//
//https://github.com/sp4cerat/Fast-Quadric-Mesh-Simplification
//
// 5/2016: Chris Rorden created minimal version for OSX/Linux/Windows compil... | 25,295 | C | 23.58309 | 142 | 0.567108 |
NVlabs/ACID/ACID/src/utils/libmcubes/pyarray_symbol.h |
#define PY_ARRAY_UNIQUE_SYMBOL mcubes_PyArray_API
| 51 | C | 16.333328 | 49 | 0.803922 |
NVlabs/ACID/ACID/src/utils/libmcubes/README.rst | ========
PyMCubes
========
PyMCubes is an implementation of the marching cubes algorithm to extract
isosurfaces from volumetric data. The volumetric data can be given as a
three-dimensional NumPy array or as a Python function ``f(x, y, z)``. The first
option is much faster, but it requires more memory and becomes unfe... | 1,939 | reStructuredText | 28.846153 | 81 | 0.682826 |
NVlabs/ACID/ACID/src/utils/libmcubes/marchingcubes.h |
#ifndef _MARCHING_CUBES_H
#define _MARCHING_CUBES_H
#include <stddef.h>
#include <vector>
namespace mc
{
extern int edge_table[256];
extern int triangle_table[256][16];
namespace private_
{
double mc_isovalue_interpolation(double isovalue, double f1, double f2,
double x1, double x2);
void mc_add_vertex(double... | 20,843 | C | 37.457565 | 92 | 0.372931 |
NVlabs/ACID/ACID/src/utils/libmcubes/pyarraymodule.h |
#ifndef _EXTMODULE_H
#define _EXTMODULE_H
#include <Python.h>
#include <stdexcept>
// #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define PY_ARRAY_UNIQUE_SYMBOL mcubes_PyArray_API
#define NO_IMPORT_ARRAY
#include "numpy/arrayobject.h"
#include <complex>
template<class T>
struct numpy_typemap;
#define define... | 4,645 | C | 32.666666 | 93 | 0.655328 |
NVlabs/ACID/ACID/src/utils/libmcubes/__init__.py | from src.utils.libmcubes.mcubes import (
marching_cubes, marching_cubes_func
)
from src.utils.libmcubes.exporter import (
export_mesh, export_obj, export_off
)
__all__ = [
marching_cubes, marching_cubes_func,
export_mesh, export_obj, export_off
]
| 265 | Python | 19.461537 | 42 | 0.70566 |
NVlabs/ACID/ACID/src/utils/libmcubes/exporter.py |
import numpy as np
def export_obj(vertices, triangles, filename):
"""
Exports a mesh in the (.obj) format.
"""
with open(filename, 'w') as fh:
for v in vertices:
fh.write("v {} {} {}\n".format(*v))
for f in triangles:
fh.write("f {} {... | 1,697 | Python | 25.53125 | 81 | 0.570418 |
NVlabs/ACID/ACID/src/utils/libmcubes/marchingcubes.cpp |
#include "marchingcubes.h"
namespace mc
{
int edge_table[256] =
{
0x000, 0x109, 0x203, 0x30a, 0x406, 0x50f, 0x605, 0x70c, 0x80c, 0x905, 0xa0f, 0xb06, 0xc0a, 0xd03, 0xe09, 0xf00,
0x190, 0x099, 0x393, 0x29a, 0x596, 0x49f, 0x795, 0x69c, 0x99c, 0x895, 0xb9f, 0xa96, 0xd9a, 0xc93, 0xf99, 0xe90,
0x230, 0x339,... | 18,889 | C++ | 56.069486 | 116 | 0.339827 |
NVlabs/ACID/ACID/src/utils/libmcubes/pywrapper.cpp |
#include "pywrapper.h"
#include "marchingcubes.h"
#include <stdexcept>
struct PythonToCFunc
{
PyObject* func;
PythonToCFunc(PyObject* func) {this->func = func;}
double operator()(double x, double y, double z)
{
PyObject* res = PyObject_CallFunction(func, "(d,d,d)", x, y, z); // py::extract<d... | 7,565 | C++ | 35.907317 | 120 | 0.624455 |
NVlabs/ACID/ACID/src/utils/libmcubes/pywrapper.h |
#ifndef _PYWRAPPER_H
#define _PYWRAPPER_H
#include <Python.h>
#include "pyarraymodule.h"
#include <vector>
PyObject* marching_cubes(PyArrayObject* arr, double isovalue);
PyObject* marching_cubes2(PyArrayObject* arr, double isovalue);
PyObject* marching_cubes3(PyArrayObject* arr, double isovalue);
PyObject* marching... | 455 | C | 25.823528 | 64 | 0.758242 |
NVlabs/ACID/ACID/src/data/__init__.py |
from src.data.core import (
PlushEnvGeom, collate_remove_none, worker_init_fn, get_plush_loader
)
from src.data.transforms import (
PointcloudNoise, SubsamplePointcloud,
SubsamplePoints,
)
__all__ = [
# Core
PlushEnvGeom,
get_plush_loader,
collate_remove_none,
worker_init_fn,
Pointc... | 379 | Python | 18.999999 | 71 | 0.693931 |
NVlabs/ACID/ACID/src/data/core.py | import os
import yaml
import pickle
import torch
import logging
import numpy as np
from torch.utils import data
from torch.utils.data.dataloader import default_collate
from src.utils import plushsim_util, common_util
scene_range = plushsim_util.SCENE_RANGE.copy()
to_range = np.array([[-1.1,-1.1,-1.1],[1.1,1.1,1.1]])... | 26,177 | Python | 42.557404 | 133 | 0.593154 |
NVlabs/ACID/ACID/src/data/transforms.py | import numpy as np
# Transforms
class PointcloudNoise(object):
''' Point cloud noise transformation class.
It adds noise to point cloud data.
Args:
stddev (int): standard deviation
'''
def __init__(self, stddev):
self.stddev = stddev
def __call__(self, data):
''' Ca... | 3,578 | Python | 25.708955 | 67 | 0.507546 |
NVlabs/ACID/ACID/configs/default.yaml | method: conv_onet
data:
train_split: train
val_split: val
test_split: test
dim: 3
act_dim: 6
padding: 0.1
type: geom
model:
decoder: simple
encoder: resnet18
decoder_kwargs: {}
encoder_kwargs: {}
multi_gpu: false
c_dim: 512
training:
out_dir: out/default
batch_size: 64
pos_weight: 5
p... | 1,121 | YAML | 18.68421 | 51 | 0.702944 |
NVlabs/ACID/ACID/configs/plush_dyn_geodesics.yaml | method: conv_onet
data:
flow_path: train_data/flow
pair_path: train_data/pair
pointcloud_n_obj: 5000
pointcloud_n_env: 1000
pointcloud_noise: 0.005
points_subsample: 3000
model:
type: combined
obj_encoder_kwargs:
f_dim: 3
hidden_dim: 64
plane_resolution: 128
unet_kwargs:
depth: 4
... | 1,175 | YAML | 18.932203 | 46 | 0.67234 |
NVlabs/ACID/ACID/preprocess/gen_data_flow_plush.py | import numpy as np
import os
import time, datetime
import sys
import os.path as osp
ACID_dir = osp.dirname(osp.dirname(osp.realpath(__file__)))
sys.path.insert(0,ACID_dir)
import json
from src.utils import plushsim_util
from src.utils import common_util
import glob
import tqdm
from multiprocessing import Pool
impor... | 4,353 | Python | 39.691588 | 143 | 0.64668 |
NVlabs/ACID/ACID/preprocess/gen_data_contrastive_pairs_flow.py | import os
import sys
import glob
import tqdm
import random
import argparse
import numpy as np
import os.path as osp
import time
from multiprocessing import Pool
ACID_dir = osp.dirname(osp.dirname(osp.realpath(__file__)))
sys.path.insert(0,ACID_dir)
parser = argparse.ArgumentParser("Training Contrastive Pair Data Gener... | 3,584 | Python | 35.212121 | 100 | 0.66183 |
NVlabs/ACID/ACID/preprocess/gen_data_flow_splits.py | import os
import sys
import os.path as osp
ACID_dir = osp.dirname(osp.dirname(osp.realpath(__file__)))
sys.path.insert(0,ACID_dir)
import glob
import argparse
flow_default = osp.join(ACID_dir, "train_data", "flow")
parser = argparse.ArgumentParser("Making training / testing splits...")
parser.add_argument("--flow_roo... | 1,972 | Python | 28.447761 | 76 | 0.625761 |
erasromani/isaac-sim-python/simulate_grasp.py | import os
import argparse
from grasp.grasp_sim import GraspSimulator
from omni.isaac.motion_planning import _motion_planning
from omni.isaac.dynamic_control import _dynamic_control
from omni.isaac.synthetic_utils import OmniKitHelper
def main(args):
kit = OmniKitHelper(
{"renderer": "RayTracedLighting"... | 2,835 | Python | 39.514285 | 197 | 0.662434 |
erasromani/isaac-sim-python/README.md | # isaac-sim-python: Python wrapper for NVIDIA Omniverse Isaac-Sim
## Overview
This repository contains a collection of python wrappers for NVIDIA Omniverse Isaac-Sim simulations. `grasp` package simulates a planar grasp execution of a Panda arm in a scene with various rigid objects place in a bin.
## Installation
Thi... | 2,201 | Markdown | 56.947367 | 550 | 0.79055 |
erasromani/isaac-sim-python/grasp/grasp_sim.py | import os
import numpy as np
import tempfile
import omni.kit
from omni.isaac.synthetic_utils import SyntheticDataHelper
from grasp.utils.isaac_utils import RigidBody
from grasp.grasping_scenarios.grasp_object import GraspObject
from grasp.utils.visualize import screenshot, img2vid
default_camera_pose = {
'posit... | 5,666 | Python | 32.532544 | 148 | 0.59107 |
erasromani/isaac-sim-python/grasp/grasping_scenarios/scenario.py | # Credits: The majority of this code is taken from build code associated with nvidia/isaac-sim:2020.2.2_ea with minor modifications.
import gc
import carb
import omni.usd
from omni.isaac.utils.scripts.nucleus_utils import find_nucleus_server
from grasp.utils.isaac_utils import set_up_z_axis
class Scenario:
""" ... | 3,963 | Python | 32.880342 | 132 | 0.609134 |
erasromani/isaac-sim-python/grasp/grasping_scenarios/grasp_object.py | # Credits: Starter code taken from build code associated with nvidia/isaac-sim:2020.2.2_ea.
import os
import random
import numpy as np
import glob
import omni
import carb
from enum import Enum
from collections import deque
from pxr import Gf, UsdGeom
from copy import copy
from omni.physx.scripts.physicsUtils import ... | 27,230 | Python | 35.502681 | 137 | 0.573265 |
erasromani/isaac-sim-python/grasp/grasping_scenarios/franka.py | # Credits: The majority of this code is taken from build code associated with nvidia/isaac-sim:2020.2.2_ea with minor modifications.
import time
import os
import numpy as np
import carb.tokens
import omni.kit.settings
from pxr import Usd, UsdGeom, Gf
from collections import deque
from omni.isaac.dynamic_control impo... | 13,794 | Python | 34.371795 | 132 | 0.582935 |
erasromani/isaac-sim-python/grasp/utils/isaac_utils.py | # Credits: All code except class RigidBody and Camera is taken from build code associated with nvidia/isaac-sim:2020.2.2_ea.
import numpy as np
import omni.kit
from pxr import Usd, UsdGeom, Gf, PhysicsSchema, PhysxSchema
def create_prim_from_usd(stage, prim_env_path, prim_usd_path, location):
"""
Create pri... | 9,822 | Python | 32.640411 | 124 | 0.633069 |
erasromani/isaac-sim-python/grasp/utils/visualize.py | import os
import ffmpeg
import matplotlib.pyplot as plt
def screenshot(sd_helper, suffix="", prefix="image", directory="images/"):
"""
Take a screenshot of the current time step of a running NVIDIA Omniverse Isaac-Sim simulation.
Args:
sd_helper (omni.isaac.synthetic_utils.SyntheticDataHelper): h... | 1,647 | Python | 30.692307 | 115 | 0.649059 |
pantelis-classes/omniverse-ai/README.md | # Learning in Simulated Worlds in Omniverse.
Please go to the wiki tab.
https://github.com/pantelis-classes/omniverse-ai/wiki
<hr />
# Wiki Navigation
* [Home][home]
* [Isaac-Sim-SDK-Omniverse-Installatio... | 3,133 | Markdown | 57.037036 | 256 | 0.785828 |
pantelis-classes/omniverse-ai/Images/images.md | # A markdown file containing all the images in the wiki. (Saved in github's cloud)
.md | # Synthetic Data in Omniverse from Isaac Sim
Omniverse comes with synthetic data generation samples in Python. These can be found in (home/.local/share/ov/pkg/isaac_sim-2021.2.0/python_samples)
## Offline Dataset Generation
This example will demonstrate how to generate synthetic dataset offline which can be used for ... | 3,918 | Markdown | 49.243589 | 218 | 0.782797 |
pantelis-classes/omniverse-ai/Wikipages/Isaac Sim SDK Omniverse Installation.md | ## Prerequisites
Ubuntu 18.04 LTS required
Nvidia drivers 470 or higher
### Installing Nvidia Drivers on Ubuntu 18.04 LTS
sudo apt-add-repository -r ppa:graphics-drivers/ppa
sudo apt updat... | 7,828 | Markdown | 38.741117 | 234 | 0.774527 |
pantelis-classes/omniverse-ai/Wikipages/TAO (NVIDIA Train, Adapt, and Optimize).md | All instructions stem from this <a href="https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html">Nvidia Doc</a>.
# Requirements
### Hardware Requirements (Recommended)
32 GB system RAM
32 GB of GPU RAM
8 core CPU
1 NVIDIA GPU
100 GB of SSD space
### Hardware Requirem... | 5,654 | Markdown | 40.580882 | 408 | 0.759816 |
pantelis-classes/omniverse-ai/Wikipages/_Sidebar.md | # Isaac Sim in Omniverse
* [Home][home]
* [Isaac-Sim-SDK-Omniverse-Installation][Omniverse]
* [Synthetic-Data-Generation][SDG]
* [NVIDIA Transfer Learning Toolkit (TLT) Installation][TLT]
* [NVIDIA TAO][TAO]
* [detectnet_v2 Installation][detectnet_v2]
* [Jupyter Notebook][Jupyter-Notebook]
[home]: https://github.com/... | 1,061 | Markdown | 57.999997 | 112 | 0.782281 |
pantelis-classes/omniverse-ai/Wikipages/home.md | # Learning in Simulated Worlds in Omniverse.
## Wiki Navigation
* [Home][home]
* [Isaac-Sim-SDK-Omniverse-Installation][Omniverse]
* [Synthetic-Data-Generation][SDG]
* [NVIDIA Transfer Learning Toolkit (TLT) Installation][TLT]
* [NVIDIA TAO][TAO]
* [detectnet_v2 Installation][detectnet_v2]
* [Jupyter Notebook][Jupyter... | 1,834 | Markdown | 64.535712 | 247 | 0.794984 |
pantelis-classes/omniverse-ai/Wikipages/NVIDIA Transfer Learning Toolkit (TLT) Installation.md | # Installing the Pre-requisites
## 1. Install docker-ce:
### * Set up repository:
Update apt package index and install packages.
sudo apt-get update
sudo apt-get install \
ca-certif... | 6,554 | Markdown | 38.727272 | 247 | 0.754043 |
pantelis-classes/omniverse-ai/Wikipages/_Footer.md | ## Authors
### <a href="https://github.com/dfsanchez999">Diego Sanchez</a> | <a href="https://harp.njit.edu/~jga26/">Jibran Absarulislam</a> | <a href="https://github.com/markkcruz">Mark Cruz</a> | <a href="https://github.com/sppatel2112">Sapan Patel</a>
## Supervisor
### <a href="https://pantelis.github.io/">Dr. P... | 446 | Markdown | 39.63636 | 244 | 0.686099 |
pantelis-classes/omniverse-ai/Wikipages/detectnet_v2 Installation.md | # Installing running detectnet_v2 in a jupyter notebook
## Setup File Structures.
- Run these commands to create the correct file structure.
cd ~
mkdir tao
mv cv_samples_v1.2.0/ tao
cd tao/cv_samples_v1.2.0/
rm -r detectnet_v2
 Toolkit is a si... | 3,749 | Markdown | 45.874999 | 243 | 0.787143 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/CODE_OF_CONDUCT.md | ## Code of Conduct
This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
opensource-codeofconduct@amazon.com with any additional questions or comments.
| 309 | Markdown | 60.999988 | 105 | 0.789644 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/CONTRIBUTING.md | # Contributing Guidelines
Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional
documentation, we greatly value feedback and contributions from our community.
Please read through this document before submitting any issues or pull requests to ensu... | 3,160 | Markdown | 51.683332 | 275 | 0.792405 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/README.md | # NVIDIA Omniverse Nucleus on Amazon EC2
NVIDIA Omniverse is a scalable, multi-GPU, real-time platform for building and operating metaverse applications, based on Pixar's Universal Scene Description (USD) and NVIDIA RTX technology. USD is a powerful, extensible 3D framework and ecosystem that enables 3D designers and d... | 8,456 | Markdown | 53.211538 | 581 | 0.786542 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/tools/nucleusServer/setup.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
from setuptools import setup
with open("README.md", "r") as fh:
long_description = fh.read()
setup(
... | 576 | Python | 21.192307 | 73 | 0.609375 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/tools/nucleusServer/nst_cli.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
# Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRe... | 2,391 | Python | 25.876404 | 143 | 0.677123 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/tools/nucleusServer/README.md | # Tools for configuring Nuclues Server
The contents of this directory are zipped and then deployed to the nuclues server | 121 | Markdown | 39.666653 | 81 | 0.826446 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/tools/nucleusServer/nst/__init__.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
| 210 | Python | 41.199992 | 73 | 0.766667 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/tools/nucleusServer/nst/logger.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
import os
import logging
LOG_LEVEL = os.getenv('LOG_LEVEL', 'DEBUG')
logger = logging.getLogger()
logger.setL... | 480 | Python | 20.863635 | 73 | 0.708333 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/tools/reverseProxy/rpt_cli.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
"""
helper tools for reverse proxy nginx configuration
"""
# std lib modules
import os
import logging
from pa... | 2,373 | Python | 24.526881 | 105 | 0.659503 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/tools/reverseProxy/setup.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
from setuptools import setup
with open("README.md", "r") as fh:
long_description = fh.read()
setup(
... | 532 | Python | 25.649999 | 73 | 0.657895 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/tools/reverseProxy/README.md | # Tools for configuring Nginx Reverse Proxy
The contents of this directory are zipped and then deployed to the reverse proxy server | 132 | Markdown | 43.333319 | 87 | 0.825758 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/tools/reverseProxy/templates/acm.yaml | # Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
---
# ACM for Nitro Enclaves config.
#
# This is an example of setting up ACM, with Nitro Enclaves and nginx.
# You can take this file and then:
# - copy it to /etc/nitro_enclaves/acm.yaml;
# - fill in your A... | 1,689 | YAML | 39.238094 | 83 | 0.68206 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/lambda/customResources/reverseProxyConfig/index.py | import os
import logging
import json
from crhelper import CfnResource
import aws_utils.ssm as ssm
import aws_utils.ec2 as ec2
import config.reverseProxy as config
LOG_LEVEL = os.getenv("LOG_LEVEL", "DEBUG")
logger = logging.getLogger()
logger.setLevel(LOG_LEVEL)
helper = CfnResource(
json_logging=False, log_lev... | 2,776 | Python | 26.495049 | 78 | 0.667147 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/lambda/customResources/nucleusServerConfig/index.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
import os
import logging
import json
from crhelper import CfnResource
import aws_utils.ssm as ssm
import aw... | 3,303 | Python | 30.169811 | 107 | 0.718438 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/lambda/asgLifeCycleHooks/reverseProxy/index.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
import boto3
import os
import json
import logging
import traceback
from botocore.exceptions import ClientErro... | 3,116 | Python | 28.40566 | 75 | 0.662067 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/lambda/common/aws_utils/ec2.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
import os
import logging
import boto3
from botocore.exceptions import ClientError
LOG_LEVEL = os.getenv("LO... | 4,068 | Python | 21.605555 | 76 | 0.630285 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/lambda/common/aws_utils/ssm.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
import os
import time
import logging
import boto3
from botocore.exceptions import ClientError
LOG_LEVEL = os... | 4,304 | Python | 30.195652 | 99 | 0.574814 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/lambda/common/aws_utils/r53.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
import boto3
client = boto3.client("route53")
def update_hosted_zone_cname_record(hostedZoneID, rootDomain... | 1,989 | Python | 33.310344 | 144 | 0.553042 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/lambda/common/aws_utils/sm.py | # Copyright 2022 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
# Licensed under the Amazon Software License http://aws.amazon.com/asl/
import json
import boto3
SM = boto3.client("secretsmanager")
def get_secret(secret_name):
response = S... | 429 | Python | 25.874998 | 73 | 0.745921 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/lambda/common/config/nucleus.py |
def start_nucleus_config() -> list[str]:
return '''
cd /opt/ove/base_stack || exit 1
echo "STARTING NUCLEUS STACK ----------------------------------"
docker-compose --env-file nucleus-stack.env -f nucleus-stack-ssl.yml start
'''.splitlines()
def stop_nucleus_config() -> list[str]:
... | 4,176 | Python | 48.72619 | 247 | 0.582136 |
aws-samples/nvidia-omniverse-nucleus-on-amazon-ec2/src/lambda/common/config/reverseProxy.py | def get_config(artifacts_bucket_name: str, nucleus_address: str, full_domain: str) -> list[str]:
return f'''
echo "------------------------ REVERSE PROXY CONFIG ------------------------"
echo "UPDATING PACKAGES ----------------------------------"
sudo yum update -y
echo "INSTALLING... | 1,670 | Python | 44.162161 | 99 | 0.511976 |
arhix52/Strelka/conanfile.py | import os
from conan import ConanFile
from conan.tools.cmake import cmake_layout
from conan.tools.files import copy
class StrelkaRecipe(ConanFile):
settings = "os", "compiler", "build_type", "arch"
generators = "CMakeToolchain", "CMakeDeps"
def requirements(self):
self.requires("glm/cci.20230113... | 1,294 | Python | 37.088234 | 87 | 0.619784 |
arhix52/Strelka/BuildOpenUSD.md | USD building:
VS2019 + python 3.10
To build debug on windows:
python USD\build_scripts\build_usd.py "C:\work\USD_build_debug" --python --materialx --build-variant debug
For USD 23.03 you could use VS2022
Linux:
* python3 ./OpenUSD/build_scripts/build_usd.py /home/<user>/work/OpenUSD_build/ --python --materialx
| 315 | Markdown | 30.599997 | 106 | 0.746032 |
arhix52/Strelka/README.md | # Strelka
Path tracing render based on NVIDIA OptiX + NVIDIA MDL and Apple Metal
## OpenUSD Hydra render delegate
## Basis curves support
## Project... | 2,849 | Markdown | 33.337349 | 197 | 0.713935 |
arhix52/Strelka/src/HdStrelka/RenderParam.h | #pragma once
#include "pxr/pxr.h"
#include "pxr/imaging/hd/renderDelegate.h"
#include "pxr/imaging/hd/renderThread.h"
#include <scene/scene.h>
PXR_NAMESPACE_OPEN_SCOPE
class HdStrelkaRenderParam final : public HdRenderParam
{
public:
HdStrelkaRenderParam(oka::Scene* scene, HdRenderThread* renderThread, std::atom... | 901 | C | 24.055555 | 105 | 0.694784 |
arhix52/Strelka/src/HdStrelka/BasisCurves.h | #pragma once
#include <pxr/pxr.h>
#include <pxr/imaging/hd/basisCurves.h>
#include <scene/scene.h>
#include <pxr/base/gf/vec2f.h>
PXR_NAMESPACE_OPEN_SCOPE
class HdStrelkaBasisCurves final : public HdBasisCurves
{
public:
HF_MALLOC_TAG_NEW("new HdStrelkaBasicCurves");
HdStrelkaBasisCurves(const SdfPath& id,... | 2,048 | C | 28.271428 | 120 | 0.67334 |
arhix52/Strelka/src/HdStrelka/Tokens.cpp | #include "Tokens.h"
PXR_NAMESPACE_OPEN_SCOPE
TF_DEFINE_PUBLIC_TOKENS(HdStrelkaSettingsTokens, HD_STRELKA_SETTINGS_TOKENS);
TF_DEFINE_PUBLIC_TOKENS(HdStrelkaNodeIdentifiers, HD_STRELKA_NODE_IDENTIFIER_TOKENS);
TF_DEFINE_PUBLIC_TOKENS(HdStrelkaSourceTypes, HD_STRELKA_SOURCE_TYPE_TOKENS);
TF_DEFINE_PUBLIC_TOKENS(HdStrel... | 564 | C++ | 42.461535 | 85 | 0.833333 |
arhix52/Strelka/src/HdStrelka/MdlDiscoveryPlugin.h | #pragma once
#include <pxr/usd/ndr/discoveryPlugin.h>
PXR_NAMESPACE_OPEN_SCOPE
class HdStrelkaMdlDiscoveryPlugin final : public NdrDiscoveryPlugin
{
public:
NdrNodeDiscoveryResultVec DiscoverNodes(const Context& ctx) override;
const NdrStringVec& GetSearchURIs() const override;
};
PXR_NAMESPACE_CLOSE_SCOPE
| 317 | C | 18.874999 | 71 | 0.807571 |
arhix52/Strelka/src/HdStrelka/Material.h | #pragma once
#include "materialmanager.h"
#include "MaterialNetworkTranslator.h"
#include <pxr/imaging/hd/material.h>
#include <pxr/imaging/hd/sceneDelegate.h>
PXR_NAMESPACE_OPEN_SCOPE
class HdStrelkaMaterial final : public HdMaterial
{
public:
HF_MALLOC_TAG_NEW("new HdStrelkaMaterial");
HdStrelkaMaterial(... | 1,258 | C | 21.890909 | 107 | 0.709062 |
arhix52/Strelka/src/HdStrelka/Light.cpp | #include "Light.h"
#include <glm/glm.hpp>
#include <glm/gtc/matrix_transform.hpp>
#include <glm/gtc/type_ptr.hpp>
#include <glm/gtx/compatibility.hpp>
#include <pxr/imaging/hd/instancer.h>
#include <pxr/imaging/hd/meshUtil.h>
#include <pxr/imaging/hd/smoothNormals.h>
#include <pxr/imaging/hd/vertexAdjacency.h>
PXR_NA... | 8,162 | C++ | 35.936651 | 117 | 0.636486 |
arhix52/Strelka/src/HdStrelka/MdlParserPlugin.cpp | // Copyright (C) 2021 Pablo Delgado Krämer
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (a... | 2,153 | C++ | 34.311475 | 119 | 0.681839 |
arhix52/Strelka/src/HdStrelka/Instancer.cpp | // Copyright (C) 2021 Pablo Delgado Krämer
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (a... | 5,927 | C++ | 29.556701 | 131 | 0.639615 |
arhix52/Strelka/src/HdStrelka/RenderDelegate.h | #pragma once
#include <pxr/imaging/hd/renderDelegate.h>
#include "MaterialNetworkTranslator.h"
#include <render/common.h>
#include <scene/scene.h>
#include <render/render.h>
PXR_NAMESPACE_OPEN_SCOPE
class HdStrelkaRenderDelegate final : public HdRenderDelegate
{
public:
HdStrelkaRenderDelegate(const HdRenderSe... | 3,149 | C | 33.23913 | 113 | 0.768498 |
arhix52/Strelka/src/HdStrelka/RenderDelegate.cpp | #include "RenderDelegate.h"
#include "Camera.h"
#include "Instancer.h"
#include "Light.h"
#include "Material.h"
#include "Mesh.h"
#include "BasisCurves.h"
#include "RenderBuffer.h"
#include "RenderPass.h"
#include "Tokens.h"
#include <pxr/base/gf/vec4f.h>
#include <pxr/imaging/hd/resourceRegistry.h>
#include <log.h>... | 6,338 | C++ | 25.634454 | 122 | 0.718523 |
arhix52/Strelka/src/HdStrelka/BasisCurves.cpp | #include "BasisCurves.h"
#include <log.h>
PXR_NAMESPACE_OPEN_SCOPE
void HdStrelkaBasisCurves::Sync(HdSceneDelegate* sceneDelegate,
HdRenderParam* renderParam,
HdDirtyBits* dirtyBits,
const TfToken& reprToken)
{
TF_UNUSE... | 8,047 | C++ | 30.685039 | 121 | 0.644215 |
arhix52/Strelka/src/HdStrelka/RenderBuffer.cpp | #include "RenderBuffer.h"
#include "render.h"
#include <pxr/base/gf/vec3i.h>
PXR_NAMESPACE_OPEN_SCOPE
HdStrelkaRenderBuffer::HdStrelkaRenderBuffer(const SdfPath& id, oka::SharedContext* ctx) : HdRenderBuffer(id), mCtx(ctx)
{
m_isMapped = false;
m_isConverged = false;
m_bufferMem = nullptr;
}
HdStrelkaR... | 2,264 | C++ | 16.558139 | 120 | 0.671378 |
arhix52/Strelka/src/HdStrelka/Tokens.h | #pragma once
#include <pxr/base/tf/staticTokens.h>
PXR_NAMESPACE_OPEN_SCOPE
#define HD_STRELKA_SETTINGS_TOKENS \
((spp, "spp"))((max_bounces, "max-bounces"))
// mtlx node identifier is given by usdMtlx.
#define HD_STRELKA_NODE_IDENTIFIER_TOKENS \
(mtlx)(mdl)
#define HD_STRELKA_SOURCE_TYPE_TOKENS \
(mtl... | 1,175 | C | 29.947368 | 86 | 0.771064 |
arhix52/Strelka/src/HdStrelka/Camera.h | #pragma once
#include <pxr/imaging/hd/camera.h>
#include <scene/scene.h>
PXR_NAMESPACE_OPEN_SCOPE
class HdStrelkaCamera final : public HdCamera
{
public:
HdStrelkaCamera(const SdfPath& id, oka::Scene& scene);
~HdStrelkaCamera() override;
public:
float GetVFov() const;
uint32_t GetCameraIndex() co... | 687 | C | 17.594594 | 58 | 0.697234 |
arhix52/Strelka/src/HdStrelka/MdlDiscoveryPlugin.cpp | #include "MdlDiscoveryPlugin.h"
#include <pxr/base/tf/staticTokens.h>
//#include "Tokens.h"
PXR_NAMESPACE_OPEN_SCOPE
// clang-format off
TF_DEFINE_PRIVATE_TOKENS(_tokens,
(mdl)
);
// clang-format on
NDR_REGISTER_DISCOVERY_PLUGIN(HdStrelkaMdlDiscoveryPlugin);
NdrNodeDiscoveryResultVec HdStrelkaMdlDiscoveryP... | 996 | C++ | 23.317073 | 88 | 0.646586 |
arhix52/Strelka/src/HdStrelka/Material.cpp | #include "Material.h"
#include <pxr/base/gf/vec2f.h>
#include <pxr/usd/sdr/registry.h>
#include <pxr/usdImaging/usdImaging/tokens.h>
#include <log.h>
PXR_NAMESPACE_OPEN_SCOPE
HdStrelkaMaterial::HdStrelkaMaterial(const SdfPath& id, const MaterialNetworkTranslator& translator)
: HdMaterial(id), m_translator(trans... | 7,130 | C++ | 35.015151 | 112 | 0.551192 |
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