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 ''' preprocessor = config.get_preprocessor(cfg, device=device) 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'], preprocessor=preprocessor, pnet_point_scale=cfg['trainer']['pnet_point_scale'], is_explicit_mesh=cfg['generation'].get('is_explicit_mesh', False), is_skip_surface_mask_generation_time=cfg['generation'].get( 'is_skip_surface_mask_generation_time', False), is_just_measuring_time=cfg['generation'].get('is_just_measuring_time', False), is_fit_to_gt_loc_scale=cfg['generation'].get('is_fit_to_gt_loc_scale', False), **cfg['generation'].get('mesh_kwargs', {}), ) return generator
def get_generator(model, cfg): ''' Returns the generator object. Args: model (tf.keras.Model): Occupancy Network model cfg (dict): imported yaml config ''' preprocessor = config.get_preprocessor(cfg) generator = generation.Generator3D( model, 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'], preprocessor=preprocessor, ) return generator
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 ''' preprocessor = config.get_preprocessor(cfg, device=device) input_type = cfg['data']['input_type'] generator_params = { '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'], 'preprocessor' :preprocessor, 'input_type' :input_type, } if input_type == 'depth_pred': generator_params['use_gt_depth_map'] = cfg['training']['use_gt_depth'] if 'pred_with_img' in cfg['model']: generator_params['with_img'] = cfg['model']['pred_with_img'] if 'depth_pointcloud_transfer' in cfg['model']: generator_params['depth_pointcloud_transfer'] = cfg['model']['depth_pointcloud_transfer'] if 'use_local_feature' in cfg['model']: generator_params['local'] = cfg['model']['use_local_feature'] generator = generation.Generator3D( model, **generator_params ) return generator
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 ''' preprocessor = config.get_preprocessor(cfg, device=device) 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'], preprocessor=preprocessor, ) return generator