def main():
parser = argparse.ArgumentParser(description="Atlas Testing")
parser.add_argument("--model",
required=True,
metavar="FILE",
help="path to checkpoint")
parser.add_argument("--scenes",
default="data/scannet_test.txt",
help="which scene(s) to run on")
args = parser.parse_args()
# get all the info_file.json's from the command line
# .txt files contain a list of info_file.json's
info_files = parse_splits_list(args.scenes)
# info_files=[info_files[0]]
metrics = {}
for i, info_file in enumerate(info_files):
# run model on each scene
scene, temp = process(info_file, args.model, i, len(info_files))
metrics[scene] = temp
rslt_file = os.path.join(args.model, 'metrics.json')
json.dump(metrics, open(rslt_file, 'w'))
# display results
visualize(rslt_file)
def main():
parser = argparse.ArgumentParser(description='Inference with COLMAP')
parser.add_argument("--scenes", default="data/scannet_test.txt",
help="path to raw dataset")
parser.add_argument("--pathout", required=True, metavar="DIR",
help="path to store processed (derived) dataset")
parser.add_argument('--stride', default=2, type=int,
help='number of frames to skip (imroves runtime)')
parser.add_argument('--scale', default=4, type=int,
help='factor to downsample images by (imroves runtime and quality)')
parser.add_argument('--i', default=0, type=int,
help='index of part for parallel processing')
parser.add_argument('--n', default=1, type=int,
help='number of parts to devide data into for parallel processing')
args = parser.parse_args()
i=args.i
n=args.n
assert 0<=i and i<n
scenes = parse_splits_list(args.scenes)
scenes = scenes[i::n]
for scene in scenes:
process(scene, args.pathout, args.stride, args.scale)
def main():
parser = argparse.ArgumentParser(description="Atlas Testing")
parser.add_argument("--model",
required=True,
metavar="FILE",
help="path to checkpoint")
parser.add_argument("--scenes",
default="data/scannet_test.txt",
help="which scene(s) to run on")
parser.add_argument("--num_frames",
default=-1,
type=int,
help="number of frames to use (-1 for all)")
parser.add_argument("--voxel_dim",
nargs=3,
default=[-1, -1, -1],
type=int,
help="override voxel dim")
parser.add_argument("--result_folder_name", default="final", type=str)
parser.add_argument("--log_step_3d_visualize", default=-1, type=int)
parser.add_argument("--log_step_mesh_save", default=-1, type=int)
parser.add_argument("--scale", default=1, type=float)
args = parser.parse_args()
# get all the info_file.json's from the command line
# .txt files contain a list of info_file.json's
info_files = parse_splits_list(args.scenes)
model = VoxelNet.load_from_checkpoint(args.model)
model = model.cuda().eval()
torch.set_grad_enabled(False)
# overwrite default values of voxel_dim_test
if args.voxel_dim[0] != -1:
model.voxel_dim_test = args.voxel_dim
# TODO: implement voxel_dim_test
model.voxel_dim_val = model.voxel_dim_test
model_name = args.result_folder_name
pass
#model_name = os.path.splitext(os.path.split(args.model)[1])[0]
#model_name = 'EuRoC_VC_01_EASY_scale_0.5'
#model_name = 'EuRoC_MH_01_scale_new_0.02'
#model_name = 'sample_scale_new_test'
#model_name = 'kitti_scale_new_0.01'
#model_name = 'ICL_NUIM_scale_1'
save_path = os.path.join(model.cfg.LOG_DIR, model.cfg.TRAINER.NAME,
model.cfg.TRAINER.VERSION, 'test_' + model_name)
if args.num_frames > -1:
save_path = '%s_%d' % (save_path, args.num_frames)
os.makedirs(save_path, exist_ok=True)
model.scale = args.scale
print(info_files)
for i, info_file in enumerate(info_files):
# run model on each scene
process(info_file, model, args.num_frames, save_path, i,
len(info_files), args)
def val_dataloader(self):
transform = self.get_transform(False)
info_files = parse_splits_list(self.cfg.DATASETS_VAL)
dataset = ScenesDataset(info_files, self.num_frames_val, transform,
self.frame_types, self.frame_selection,
self.voxel_types, self.voxel_sizes)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=1,
num_workers=1,
collate_fn=collate_fn,
shuffle=False,
drop_last=False)
return dataloader
def train_dataloader(self):
transform = self.get_transform(True)
info_files = parse_splits_list(self.cfg.DATASETS_TRAIN)
dataset = ScenesDataset(info_files, self.num_frames_train, transform,
self.frame_types, self.frame_selection,
self.voxel_types, self.voxel_sizes)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=self.batch_size_train,
num_workers=2,
collate_fn=collate_fn,
shuffle=True,
drop_last=True)
return dataloader
def main():
parser = argparse.ArgumentParser(description="Atlas Testing")
parser.add_argument("--model",
required=True,
metavar="FILE",
help="path to checkpoint")
parser.add_argument("--scenes",
default="data/scannet_test.txt",
help="which scene(s) to run on")
parser.add_argument("--num_frames",
default=-1,
type=int,
help="number of frames to use (-1 for all)")
parser.add_argument("--voxel_dim",
nargs=3,
default=[-1, -1, -1],
type=int,
help="override voxel dim")
args = parser.parse_args()
# get all the info_file.json's from the command line
# .txt files contain a list of info_file.json's
info_files = parse_splits_list(args.scenes)
model = VoxelNet.load_from_checkpoint(args.model)
model = model.cuda().eval()
torch.set_grad_enabled(False)
# overwrite default values of voxel_dim_test
if args.voxel_dim[0] != -1:
model.voxel_dim_test = args.voxel_dim
# TODO: implement voxel_dim_test
model.voxel_dim_val = model.voxel_dim_test
model_name = os.path.splitext(os.path.split(args.model)[1])[0]
save_path = os.path.join(model.cfg.LOG_DIR, model.cfg.TRAINER.NAME,
model.cfg.TRAINER.VERSION, 'test_' + model_name)
if args.num_frames > -1:
save_path = '%s_%d' % (save_path, args.num_frames)
os.makedirs(save_path, exist_ok=True)
for i, info_file in enumerate(info_files):
# run model on each scene
process(info_file, model, args.num_frames, save_path, i,
len(info_files))
def main():
parser = argparse.ArgumentParser(description='Evaluate COLMAP')
parser.add_argument("--scenes",
default="data/scannet_test.txt",
help="path to raw dataset")
parser.add_argument("--pathout",
required=True,
metavar="DIR",
help="path to store processed (derived) dataset")
args = parser.parse_args()
scenes = parse_splits_list(args.scenes)
metrics = {}
for scene in scenes:
metrics[scene] = eval_scene(scene, args.pathout)
rslt_file = os.path.join(args.pathout, 'metrics.json')
json.dump(metrics, open(rslt_file, 'w'))
def main():
parser = argparse.ArgumentParser(
description='Fuse ground truth tsdf on Scannet')
parser.add_argument("--path",
required=True,
metavar="DIR",
help="path to raw dataset")
parser.add_argument("--path_meta",
required=True,
metavar="DIR",
help="path to store processed (derived) dataset")
parser.add_argument("--dataset",
required=True,
type=str,
help="which dataset to prepare")
parser.add_argument('--i',
default=0,
type=int,
help='index of part for parallel processing')
parser.add_argument(
'--n',
default=1,
type=int,
help='number of parts to devide data into for parallel processing')
parser.add_argument(
'--test',
action='store_true',
help=
'only prepare the test set (for rapid testing if you dont plan to train)'
)
parser.add_argument(
'--max_depth',
default=3.,
type=float,
help='mask out large depth values since they are noisy')
parser.add_argument("--model",
required=True,
metavar="FILE",
help="path to checkpoint")
parser.add_argument("--scenes",
default="data/scannet_test.txt",
help="which scene(s) to run on")
parser.add_argument("--num_frames",
default=-1,
type=int,
help="number of frames to use (-1 for all)")
parser.add_argument("--voxel_dim",
nargs=3,
default=[-1, -1, -1],
type=int,
help="override voxel dim")
args = parser.parse_args()
i = args.i
n = args.n
assert 0 <= i and i < n
if args.dataset == 'sample':
scenes = ['sample1']
scenes = scenes[i::n] # distribute among workers
for scene in scenes:
prepare_sample_scene(
scene,
os.path.join(args.path, 'sample'),
os.path.join(args.path_meta, 'sample'),
)
elif args.dataset == 'scannet':
prepare_scannet(os.path.join(args.path, 'scannet'),
os.path.join(args.path_meta, 'scannet'), i, n,
args.test, args.max_depth)
else:
raise NotImplementedError('unknown dataset %s' % args.dataset)
name = "Thread #%s" % (2)
thread1 = MyThread1(name)
thread1.start()
# get all the info_file.json's from the command line
# .txt files contain a list of info_file.json's
info_files = parse_splits_list(args.scenes)
model = VoxelNet.load_from_checkpoint(args.model)
model = model.cuda().eval()
torch.set_grad_enabled(False)
# overwrite default values of voxel_dim_test
if args.voxel_dim[0] != -1:
model.voxel_dim_test = args.voxel_dim
# TODO: implement voxel_dim_test
model.voxel_dim_val = model.voxel_dim_test
model_name = os.path.splitext(os.path.split(args.model)[1])[0]
save_path = os.path.join(model.cfg.LOG_DIR, model.cfg.TRAINER.NAME,
model.cfg.TRAINER.VERSION, 'test_' + model_name)
if args.num_frames > -1:
save_path = '%s_%d' % (save_path, args.num_frames)
os.makedirs(save_path, exist_ok=True)
i = 0
# run model on each scene
for info_file in info_files:
window_conf = gl.Config(double_buffer=True, depth_size=24)
process(info_file, model, args.num_frames, save_path, i,
len(info_files), window_conf)