# # 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()
recon_pc, mid_pc, _ = foldingnet(points, Cov, batch_graph)
pass
#classifier = PointNetCls(k = num_classes)
foldingnet = FoldingNet()
if opt.model != '':
classifier.load_state_dict(torch.load(opt.model))
#optimizer = optim.SGD(foldingnet.parameters(), lr=0.01, momentum=0.9)
optimizer = optim.Adam(foldingnet.parameters(), lr=0.0001, weight_decay=1e-6)
foldingnet.cuda()
num_batch = len(dataset) / opt.batchSize
chamferloss = ChamferLoss()
chamferloss.cuda()
start_time = time.time()
time_p, loss_p, loss_m = [], [], []
for epoch in range(opt.nepoch):
sum_loss = 0
sum_step = 0
sum_mid_loss = 0
for i, data in enumerate(dataloader, 0):
points, target = data
#print(points.size())
points, target = Variable(points), Variable(target[:, 0])
points = points.transpose(2, 1)