# # a2 = fig2.add_subplot(111,projection='3d')
# # a2.scatter(ps2[:,0],ps2[:,1],ps2[:,2])
# plt.show()
foldingnet = FoldingNet_graph()
#foldingnet.load_state_dict(torch.load('cls_fold_512code_2500points/foldingnet_model_170.pth'))
foldingnet.load_state_dict(
torch.load(
'cls_fold_512code_2500points_170_restart/foldingnet_model_150.pth')
)
foldingnet.cuda()
chamferloss = ChamferLoss()
chamferloss = chamferloss.cuda()
#print(foldingnet)
foldingnet.eval()
i, data = li[1]
points, target = data
batch_graph, Cov = build_graph(points, opt)
Cov = Cov.transpose(2, 1)
Cov = Cov.cuda()
points = points.transpose(2, 1)
points = points.cuda()