def execute(self):
max_workers = self.get_value("max_workers")
chunk_size = self.get_value("chunk_size")
use_mpi = self.get_value("use_mpi")
force_single_core = self.get_value("force_single_core")
problem_dir = file.get_relative_path(self.get_value("input_dir"),
self._parent_dir)
problem_list = file.get_file_list(problem_dir,
constants.PROBLEM_FILE_REGEX)
domain_list = file.get_file_list(problem_dir,
constants.DOMAIN_FILE_REGEX)
assert len(domain_list) == 1
domain_file = domain_list[0]
if force_single_core:
results = executor.singlecore_execute(self.solve,
(domain_file, problem_list))
else:
results = executor.multicore_execute(self.solve,
(domain_file, problem_list),
self.generate_args,
max_workers, chunk_size,
use_mpi)
return results
def get_training_data(self, problem_dir, max_workers, chunk_size, use_mpi,
force_single_core, abstract_domain=None):
solver_name = self.get_value("solver_name")
problem_list = file.get_file_list(problem_dir,
constants.PROBLEM_FILE_REGEX)
domain_list = file.get_file_list(problem_dir,
constants.DOMAIN_FILE_REGEX)
assert len(domain_list) == 1
domain_filepath = domain_list[0]
if force_single_core:
training_data = executor.singlecore_execute(
self._get_training_data,
(domain_filepath, problem_list, solver_name, abstract_domain))
else:
training_data = executor.multicore_execute(
self._get_training_data,
(domain_filepath, problem_list, solver_name, abstract_domain),
self._gen_get_training_data_args,
max_workers, chunk_size,
use_mpi)
return training_data
def extract_features_batch(model, config, source_path, target_path, voxel_size,
device):
folders = get_folder_list(source_path)
assert len(
folders) > 0, f"Could not find 3DMatch folders under {source_path}"
logging.info(folders)
list_file = os.path.join(target_path, "list.txt")
f = open(list_file, "w")
timer, tmeter = Timer(), AverageMeter()
num_feat = 0
model.eval()
for fo in folders:
if 'evaluation' in fo:
continue
files = get_file_list(fo, ".ply")
fo_base = os.path.basename(fo)
f.write("%s %d\n" % (fo_base, len(files)))
for i, fi in enumerate(files):
# Extract features from a file
pcd = o3d.io.read_point_cloud(fi)
save_fn = "%s_%03d" % (fo_base, i)
if i % 100 == 0:
logging.info(f"{i} / {len(files)}: {save_fn}")
timer.tic()
xyz_down, feature = extract_features(model,
xyz=np.array(pcd.points),
rgb=None,
normal=None,
voxel_size=voxel_size,
device=device,
skip_check=True)
t = timer.toc()
if i > 0:
tmeter.update(t)
num_feat += len(xyz_down)
np.savez_compressed(os.path.join(target_path, save_fn),
points=np.array(pcd.points),
xyz=xyz_down,
feature=feature.detach().cpu().numpy())
if i % 20 == 0 and i > 0:
# 最后一项算的是每个点的特征提取时间
logging.info(
f'Average time: {tmeter.avg}, FPS: {num_feat / tmeter.sum}, time / feat: {tmeter.sum / num_feat}, '
)
f.close()
def get_documents(self, input_dir):
# Get the solution file list.
solutions = file.get_file_list(input_dir,
constants.SOLUTION_FILE_REGEX)
# Get the list of solution properties.
if self.get_value("force_single_core"):
properties = executor.singlecore_execute(self._extract_properties,
(solutions))
else:
properties = executor.multicore_execute(
self._extract_properties, (solutions, ), self._generate_args,
self.get_value("max_workers"), self.get_value("chunk_size"),
self.get_value("use_mpi"))
# Create the database.
db = TinyDB(storage=MemoryStorage)
db.insert_multiple(properties)
# Try applying any specified filter.
try:
query_str = self.get_value("filter")
query = Query()
(query)
documents = db.search(eval(query_str))
except KeyError:
# If no filter, then all documents are a part of the data.
documents = db.all()
return documents
