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__
