def __init__(self, envmaps, textures, models, pbrt_converter): self._envmaps = envmaps self._textures = textures self._current_textures = [] self._models = models self._converter = ObjConverter(pbrt_converter) self._randomize_textures() self._log = ttools.get_logger(self.__class__.__name__)
import os # import logging import random import numpy as np import torch from torchvision.datasets import MNIST import torchvision.transforms as xforms from torch.utils.data import DataLoader import ttools import ttools.interfaces import pydiffvg LOG = ttools.get_logger(__name__) pydiffvg.render_pytorch.print_timing = False torch.manual_seed(123) np.random.seed(123) torch.backends.cudnn.deterministic = True latent_dim = 100 img_size = 32 num_paths = 8 num_segments = 8 def weights_init_normal(m): classname = m.__class__.__name__