torch.zeros(
1, faces_gt.shape[1], 2, 1, dtype=torch.float32,
device=device),
torch.zeros(
1, faces_gt.shape[1], 2, 1, dtype=torch.float32,
device=device),
),
dim=-1,
)
# Initialize the renderer.
renderer = SoftRenderer(camera_mode="look_at", device=device)
camera_distance = 8.0
elevation = 0.0
azimuth = 0.0
renderer.set_eye_from_angles(camera_distance, elevation, azimuth)
pendulum_gt = SimplePendulum(args.mass, args.radius, args.gravity,
args.length, args.damping).to(device)
theta_init = torch.tensor([0.0, 3.0], device=device)
times = torch.arange(
args.starttime,
args.starttime + args.simsteps * args.dtime,
args.dtime,
device=device,
)
numsteps = times.numel()
ret = odeint(pendulum_gt, theta_init, times)
theta1_gt = ret[:, 0]
theta2_gt = ret[:, 1]
# os.path.join(os.path.dirname(os.path.abspath(__file__)), "../gradsim")
# ),
# os.path.join(args.out_dir, "gradsim"),
# )
shutil.copy(os.path.abspath(__file__), args.out_dir)
os.makedirs(os.path.join(args.out_dir, "obj"), exist_ok=True)
for obj in glob.glob(os.path.join(args.data_dir, "*.obj")):
shutil.copy(obj, os.path.join(args.out_dir, "obj"))
with open(os.path.join(args.out_dir, "args.yaml"), "w") as f:
yaml.dump(vars(args), f, default_flow_style=False)
# Initialize the renderer.
renderer = SoftRenderer(image_size=args.image_size,
camera_mode=args.camera_mode,
device=device)
renderer.set_eye_from_angles(args.camera_distance, args.elevation,
args.azimuth)
# Initialize sampler
prim_gen = PrimitiveGenerator(mass_scaling=args.mass_scaling,
mass_offset=args.mass_offset,
data_type=args.data_type,
data_dir=args.data_dir)
objects = sorted(glob.glob(os.path.join(args.data_dir, "*.obj")))
pos, ornt, linvel, angvel = None, None, None, None
try:
for i in trange(args.n):
volume = 0.0
