def load_trial(trial_path):
"""
@param trial_path: full path or relative path from shape_completion_training/trials
@type trial_path: str
@return:
"""
trial_path = pathlib.Path(trial_path)
if not trial_path.is_absolute():
r = rospkg.RosPack()
trial_path = pathlib.Path(
r.get_path('shape_completion_training')) / "trials" / trial_path
if not trial_path.is_dir():
raise ValueError(
"Cannot load, the path {} is not an existing directory".format(
trial_path))
group_name = get_group_name(trial_path)
params = default_params.get_default_params(group_name)
params_filename = trial_path / 'params.json'
with params_filename.open("r") as params_file:
params.update(json.load(params_file))
return trial_path, params
import argparse
# params = {
# 'batch_size': 1500,
# 'network': 'RealNVP',
# 'dim': 24,
# 'num_masked': 12,
# 'learning_rate': 1e-5,
# 'translation_pixel_range_x': 10,
# 'translation_pixel_range_y': 10,
# 'translation_pixel_range_z': 10,
# }
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Process args for training")
parser.add_argument('--tmp', action='store_true')
parser.add_argument('--group', default=None)
args = parser.parse_args()
params = default_params.get_default_params(group_name=args.group)
params['load_bb_only'] = True
data_supervisor = shape_completion_training.utils.dataset_loader.get_dataset_supervisor(
params['dataset'])
if args.tmp:
mr = ModelRunner(training=True, params=params, group_name=None)
else:
mr = ModelRunner(training=True, params=params, group_name=args.group)
mr.train_and_test(data_supervisor)
from shape_completion_training.utils.tf_utils import log_normal_pdf, stack_known, sample_gaussian
from shape_completion_training.voxelgrid.metrics import chamfer_distance
from shape_completion_visualization.voxelgrid_publisher import VoxelgridPublisher, PointcloudPublisher
"""
Publish object pointclouds for use in gpu_voxels planning
"""
ARGS = None
VG_PUB = None
PT_PUB = None
model_runner = None
dataset_params = None
default_dataset_params = default_params.get_default_params()
default_translations = {
'translation_pixel_range_x': 0,
'translation_pixel_range_y': 0,
'translation_pixel_range_z': 0,
}
Transformer = None
def wip_enforce_contact(elem):
inference = model_runner.model(elem)
VG_PUB.publish_inference(inference)
pssnet = model_runner.model
latent = tf.Variable(pssnet.sample_latent(elem))