def __init__(self):
config = Config()
self.ts_queue = queue.Queue()
self.max_threads = Response().get_max_threads()
self.video_format = config.getValue("video_format")
self.ffmpeg_path = config.getValue("ffmpeg_path")
self.key_host = Response().get_key_url()
EmbeddingLoss,
primitive_loss,
evaluate_miou,
)
from src.segment_utils import to_one_hot, SIOU_matched_segments
from src.utils import visualize_point_cloud_from_labels, visualize_point_cloud
from src.dataset import generator_iter
from src.mean_shift import MeanShift
from src.segment_utils import SIOU_matched_segments
from src.residual_utils import Evaluation
import time
from src.primitives import SaveParameters
# Use only one gpu.
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
config = Config(sys.argv[1])
if_normals = config.normals
userspace = ""
Loss = EmbeddingLoss(margin=1.0)
if config.mode == 0:
# Just using points for training
model = PrimitivesEmbeddingDGCNGn(
embedding=True,
emb_size=128,
primitives=True,
num_primitives=10,
loss_function=Loss.triplet_loss,
mode=config.mode,
num_channels=3,
from read_config import Config
from src.VisUtils import tessalate_points
from src.dataset import DataSetControlPointsPoisson
from src.dataset import generator_iter
from src.fitting_utils import sample_points_from_control_points_
from src.fitting_utils import up_sample_points_torch_in_range
from src.loss import control_points_permute_reg_loss
from src.loss import laplacian_loss
from src.loss import (
uniform_knot_bspline,
spline_reconstruction_loss,
)
from src.model import DGCNNControlPoints
from src.primitive_forward import optimize_open_spline
config = Config(sys.argv[1])
control_decoder = DGCNNControlPoints(20, num_points=10, mode=config.mode)
control_decoder = torch.nn.DataParallel(control_decoder)
control_decoder.cuda()
split_dict = {"train": config.num_train, "val": config.num_val, "test": config.num_test}
dataset = DataSetControlPointsPoisson(
config.dataset_path,
config.batch_size,
splits=split_dict,
size_v=config.grid_size,
size_u=config.grid_size)
nu, nv = uniform_knot_bspline(20, 20, 3, 3, 30)
from src.VisUtils import tessalate_points
from src.dataset import DataSetControlPointsPoisson
from src.dataset import generator_iter
from src.fitting_utils import sample_points_from_control_points_
from src.fitting_utils import up_sample_points_torch_in_range
from src.loss import control_points_permute_reg_loss
from src.loss import laplacian_loss
from src.loss import (
uniform_knot_bspline,
spline_reconstruction_loss,
)
from src.model import DGCNNControlPoints
from src.primitive_forward import optimize_close_spline
from src.utils import chamfer_distance_single_shape
config = Config(sys.argv[1])
userspace = ".."
print(config.mode)
control_decoder = DGCNNControlPoints(20, num_points=10, mode=config.mode)
control_decoder = torch.nn.DataParallel(control_decoder)
control_decoder.cuda()
config.batch_size = 1
split_dict = {
"train": config.num_train,
"val": config.num_val,
"test": config.num_test
}
dataset = DataSetControlPointsPoisson(path=config.dataset_path,
batch_size=config.batch_size,