def __init__(self,
resolution="512x512",
frames=501,
speed=5,
raymarching_steps=None,
path_gen=None,
beam=10,
at=(0, 0, 0),
up=(0, 1, 0),
output_dir=None,
output_type=None,
fps=24,
test_camera_poses=None,
test_camera_intrinsics=None,
test_camera_views=None):
self.frames = frames
self.speed = speed
self.raymarching_steps = raymarching_steps
self.path_gen = path_gen
if isinstance(resolution, str):
self.resolution = [int(r) for r in resolution.split('x')]
else:
self.resolution = [resolution, resolution]
self.beam = beam
self.output_dir = output_dir
self.output_type = output_type
self.at = at
self.up = up
self.fps = fps
if self.path_gen is None:
self.path_gen = trajectory.circle()
if self.output_type is None:
self.output_type = ["rgb"]
if test_camera_intrinsics is not None:
self.test_int = data_utils.load_intrinsics(test_camera_intrinsics)
else:
self.test_int = None
self.test_frameids = None
if test_camera_poses is not None:
if os.path.isdir(test_camera_poses):
self.test_poses = [
np.loadtxt(f)[None, :, :]
for f in sorted(glob.glob(test_camera_poses + "/*.txt"))
]
self.test_poses = np.concatenate(self.test_poses, 0)
else:
self.test_poses = data_utils.load_matrix(test_camera_poses)
if self.test_poses.shape[1] == 17:
self.test_frameids = self.test_poses[:,
-1].astype(np.int32)
self.test_poses = self.test_poses[:, :-1]
self.test_poses = self.test_poses.reshape(-1, 4, 4)
if test_camera_views is not None:
render_views = parse_views(test_camera_views)
self.test_poses = np.stack(
[self.test_poses[r] for r in render_views])
else:
self.test_poses = None
def __init__(self, args):
super().__init__(args)
self._trainer, self._dummy_batch = None, None
# check dataset
self.train_data = self.val_data = self.test_data = args.data
self.object_ids = None if args.object_id_path is None else \
{line.strip(): i for i, line in enumerate(open(args.object_id_path))}
self.output_valid = getattr(args, "output_valid", None)
if os.path.isdir(args.data):
if os.path.exists(args.data + '/train.txt'):
self.train_data = args.data + '/train.txt'
if os.path.exists(args.data + '/val.txt'):
self.val_data = args.data + '/val.txt'
if os.path.exists(args.data + '/test.txt'):
self.test_data = args.data + '/test.txt'
if self.object_ids is None and os.path.exists(args.data +
'/object_ids.txt'):
self.object_ids = {
line.strip(): i
for i, line in enumerate(
open(args.data + '/object_ids.txt'))
}
if self.object_ids is not None:
self.ids_object = {self.object_ids[o]: o for o in self.object_ids}
else:
self.ids_object = {0: 'model'}
if len(self.args.tensorboard_logdir) > 0 and getattr(
args, "distributed_rank", -1) == 0:
from tensorboardX import SummaryWriter
self.writer = SummaryWriter(self.args.tensorboard_logdir +
'/images')
else:
self.writer = None
self._num_updates = {'pv': -1, 'sv': -1, 'rs': -1, 're': -1}
self.pruning_every_steps = getattr(self.args, "pruning_every_steps",
None)
self.pruning_th = getattr(self.args, "pruning_th", 0.5)
self.rendering_every_steps = getattr(self.args,
"rendering_every_steps", None)
self.steps_to_half_voxels = getattr(self.args, "half_voxel_size_at",
None)
self.steps_to_reduce_step = getattr(self.args, "reduce_step_size_at",
None)
self.steps_to_prune_voxels = getattr(self.args, "prune_voxel_at", None)
if self.steps_to_half_voxels is not None:
self.steps_to_half_voxels = [
int(s) for s in self.steps_to_half_voxels.split(',')
]
if self.steps_to_reduce_step is not None:
self.steps_to_reduce_step = [
int(s) for s in self.steps_to_reduce_step.split(',')
]
if self.steps_to_prune_voxels is not None:
self.steps_to_prune_voxels = [
int(s) for s in self.steps_to_prune_voxels.split(',')
]
if self.rendering_every_steps is not None:
gen_args = {
'path': args.save_dir,
'render_beam': 1,
'render_resolution': '512x512',
'render_num_frames': 120,
'render_angular_speed': 3,
'render_output_types': ["rgb"],
'render_raymarching_steps': 10,
'render_at_vector': "(0,0,0)",
'render_up_vector': "(0,0,-1)",
'render_path_args': "{'radius': 1.5, 'h': 0.5}",
'render_path_style': 'circle',
"render_output": None
}
gen_args.update(
json.loads(getattr(args, 'rendering_args', '{}') or '{}'))
self.renderer = self.build_generator(Namespace(**gen_args))
else:
self.renderer = None
self.train_views = parse_views(args.train_views)
self.valid_views = parse_views(args.valid_views)
self.test_views = parse_views(args.test_views)