def get_generator(model, cfg, device, **kwargs):
''' Returns the generator object.
Args:
model (nn.Module): Occupancy Network model
cfg (dict): imported yaml config
device (device): pytorch device
'''
if cfg['data']['input_type'] == 'pointcloud_crop':
# calculate the volume boundary
query_vol_metric = cfg['data']['padding'] + 1
unit_size = cfg['data']['unit_size']
recep_field = 2**(
cfg['model']['encoder_kwargs']['unet3d_kwargs']['num_levels'] + 2)
if 'unet' in cfg['model']['encoder_kwargs']:
depth = cfg['model']['encoder_kwargs']['unet_kwargs']['depth']
elif 'unet3d' in cfg['model']['encoder_kwargs']:
depth = cfg['model']['encoder_kwargs']['unet3d_kwargs'][
'num_levels']
vol_info = decide_total_volume_range(query_vol_metric, recep_field,
unit_size, depth)
grid_reso = cfg['data']['query_vol_size'] + recep_field - 1
grid_reso = update_reso(grid_reso, depth)
query_vol_size = cfg['data']['query_vol_size'] * unit_size
input_vol_size = grid_reso * unit_size
# only for the sliding window case
vol_bound = None
if cfg['generation']['sliding_window']:
vol_bound = {
'query_crop_size': query_vol_size,
'input_crop_size': input_vol_size,
'fea_type': cfg['model']['encoder_kwargs']['plane_type'],
'reso': grid_reso
}
else:
vol_bound = None
vol_info = None
generator = generation.Generator3D(
model,
device=device,
threshold=cfg['test']['threshold'],
resolution0=cfg['generation']['resolution_0'],
upsampling_steps=cfg['generation']['upsampling_steps'],
sample=cfg['generation']['use_sampling'],
refinement_step=cfg['generation']['refinement_step'],
simplify_nfaces=cfg['generation']['simplify_nfaces'],
input_type=cfg['data']['input_type'],
padding=cfg['data']['padding'],
vol_info=vol_info,
vol_bound=vol_bound,
)
return generator
def get_vol_info(self, model_path):
''' Get crop information
Args:
model_path (str): path to the current data
'''
query_vol_size = self.cfg['data']['query_vol_size']
unit_size = self.cfg['data']['unit_size']
field_name = self.cfg['data']['pointcloud_file']
plane_type = self.cfg['model']['encoder_kwargs']['plane_type']
recep_field = 2**(
self.cfg['model']['encoder_kwargs']['unet3d_kwargs']['num_levels']
+ 2)
if self.cfg['data']['multi_files'] is None:
file_path = os.path.join(model_path, field_name)
else:
num = np.random.randint(self.cfg['data']['multi_files'])
file_path = os.path.join(model_path, field_name,
'%s_%02d.npz' % (field_name, num))
points_dict = np.load(file_path)
p = points_dict['points']
if self.split == 'train':
# randomly sample a point as the center of input/query volume
p_c = [
np.random.uniform(p[:, i].min(), p[:, i].max())
for i in range(3)
]
# p_c = [np.random.uniform(-0.55, 0.55) for i in range(3)]
p_c = np.array(p_c).astype(np.float32)
reso = query_vol_size + recep_field - 1
# make sure the defined reso can be properly processed by UNet
reso = update_reso(reso, self.depth)
input_vol_metric = reso * unit_size
query_vol_metric = query_vol_size * unit_size
# bound for the volumes
lb_input_vol, ub_input_vol = p_c - input_vol_metric / 2, p_c + input_vol_metric / 2
lb_query_vol, ub_query_vol = p_c - query_vol_metric / 2, p_c + query_vol_metric / 2
input_vol = [lb_input_vol, ub_input_vol]
query_vol = [lb_query_vol, ub_query_vol]
else:
reso = self.total_reso
input_vol = self.total_input_vol
query_vol = self.total_query_vol
vol_info = {
'plane_type': plane_type,
'reso': reso,
'input_vol': input_vol,
'query_vol': query_vol
}
return vol_info
def get_model(cfg, device=None, dataset=None, **kwargs):
''' Return the Occupancy Network model.
Args:
cfg (dict): imported yaml config
device (device): pytorch device
dataset (dataset): dataset
'''
decoder = cfg['model']['decoder']
encoder = cfg['model']['encoder']
dim = cfg['data']['dim']
c_dim = cfg['model']['c_dim']
decoder_kwargs = cfg['model']['decoder_kwargs']
encoder_kwargs = cfg['model']['encoder_kwargs']
padding = cfg['data']['padding']
# for pointcloud_crop
try:
encoder_kwargs['unit_size'] = cfg['data']['unit_size']
decoder_kwargs['unit_size'] = cfg['data']['unit_size']
except:
pass
# local positional encoding
if 'local_coord' in cfg['model'].keys():
encoder_kwargs['local_coord'] = cfg['model']['local_coord']
decoder_kwargs['local_coord'] = cfg['model']['local_coord']
if 'pos_encoding' in cfg['model']:
encoder_kwargs['pos_encoding'] = cfg['model']['pos_encoding']
decoder_kwargs['pos_encoding'] = cfg['model']['pos_encoding']
# update the feature volume/plane resolution
if cfg['data']['input_type'] == 'pointcloud_crop':
fea_type = cfg['model']['encoder_kwargs']['plane_type']
if (dataset.split == 'train') or (cfg['generation']['sliding_window']):
recep_field = 2**(
cfg['model']['encoder_kwargs']['unet3d_kwargs']['num_levels'] +
2)
reso = cfg['data']['query_vol_size'] + recep_field - 1
if 'grid' in fea_type:
encoder_kwargs['grid_resolution'] = update_reso(
reso, dataset.depth)
if bool(set(fea_type) & set(['xz', 'xy', 'yz'])):
encoder_kwargs['plane_resolution'] = update_reso(
reso, dataset.depth)
# if dataset.split == 'val': #TODO run validation in room level during training
else:
if 'grid' in fea_type:
encoder_kwargs['grid_resolution'] = dataset.total_reso
if bool(set(fea_type) & set(['xz', 'xy', 'yz'])):
encoder_kwargs['plane_resolution'] = dataset.total_reso
decoder = models.decoder_dict[decoder](dim=dim,
c_dim=c_dim,
padding=padding,
**decoder_kwargs)
if encoder == 'idx':
encoder = nn.Embedding(len(dataset), c_dim)
elif encoder is not None:
encoder = encoder_dict[encoder](dim=dim,
c_dim=c_dim,
padding=padding,
**encoder_kwargs)
else:
encoder = None
model = models.ConvolutionalOccupancyNetwork(decoder,
encoder,
device=device)
return model
