def test_Surface_Meshes(): surface_meshes = shapenet.ShapeNet_Surface_Meshes(root=SHAPENET_ROOT, cache_dir=CACHE_DIR, categories=['can'], train=True, split=.1, resolution=100, smoothing_iterations=3, mode='Tri') assert len(surface_meshes) == 10 assert surface_meshes.cache_dir.exists() assert len(list(surface_meshes.cache_dir.rglob('*.p'))) == 10 for smesh in surface_meshes: assert smesh['data']['vertices'].shape[0] > 0 assert smesh['data']['faces'].shape[1] == 3 shutil.rmtree('tests/datasets/cache/surface_meshes') surface_meshes = shapenet.ShapeNet_Surface_Meshes(root=SHAPENET_ROOT, cache_dir=CACHE_DIR, categories=['can'], train=True, split=.1, resolution=100, smoothing_iterations=3, mode='Quad') assert len(surface_meshes) == 10 assert surface_meshes.cache_dir.exists() assert len(list(surface_meshes.cache_dir.rglob('*.p'))) == 10 for smesh in surface_meshes: assert smesh['data']['vertices'].shape[0] > 0 assert smesh['data']['faces'].shape[1] == 4 shutil.rmtree('tests/datasets/cache/voxels') shutil.rmtree('tests/datasets/cache/surface_meshes')
cache_dir=args.cache_dir, categories=args.categories, train=True, split=.7, num_points=3000) images_set = shapenet.ShapeNet_Images(root=args.shapenet_images_root, categories=args.categories, train=True, split=.7, views=23, transform=preprocess) if args.latent_loss: mesh_set = shapenet.ShapeNet_Surface_Meshes(root=args.shapenet_root, cache_dir=args.cache_dir, categories=args.categories, resolution=100, train=True, split=.7, mode='Tri') train_set = shapenet.ShapeNet_Combination( [points_set, images_set, mesh_set]) dataloader_train = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, collate_fn=collate_fn, num_workers=8) else: train_set = shapenet.ShapeNet_Combination([points_set, images_set]) dataloader_train = DataLoader(train_set, batch_size=args.batch_size, shuffle=True,