def evaluate(model_test):
model_test.eval()
total_correct=0
data_eval =pts_cls_dataset(datalist_path=args.data_eval,data_argument=False,num_points=args.num_pts,use_extra_feature=args.normal)
eval_loader = torch.utils.data.DataLoader(data_eval,
batch_size=4, shuffle=True, collate_fn=pts_collate)
print ("dataset size:",len(eval_loader.dataset))
for batch_idx, (pts, label) in enumerate(eval_loader):
if is_GPU:
pts = Variable(pts.cuda())
label = Variable(label.cuda())
else:
pts = Variable(pts)
label = Variable(label)
pred,trans = net(pts)
_, pred_index = torch.max(pred, dim=1)
num_correct = (pred_index.eq(label)).data.cpu().sum().item()
total_correct +=num_correct
print ('the average correct rate:{}'.format(total_correct*1.0/(len(eval_loader.dataset))))
model_test.train()
with open(logname,'a') as f:
f.write('\nthe evaluate average accuracy:{}'.format(total_correct*1.0/(len(eval_loader.dataset))))
def evaluate(model_test):
model_test.eval()
total_correct=0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
data_eval = pts_cls_dataset(datalist_path=args.data_eval,data_argument=False,num_points=args.num_pts,use_extra_feature=args.normal)
eval_loader = torch.utils.data.DataLoader(data_eval,
batch_size=args.batch_size, shuffle=True, collate_fn=pts_collate)
print ("dataset size:",len(eval_loader.dataset))
for batch_idx, (pts, label) in enumerate(eval_loader):
if is_GPU:
pts = Variable(pts.cuda())
label = Variable(label.cuda())
else:
pts = Variable(pts)
label = Variable(label)
pred,trans = net(pts)
loss = critenrion(pred, label)
K = trans.size(1)
reg_loss = torch.bmm(trans, trans.transpose(2, 1))
if is_GPU:
iden = Variable(torch.eye(K).cuda())
else:
iden = Variable(torch.eye(K))
reg_loss -= iden
reg_loss = reg_loss * reg_loss
loss = loss + reg_loss.sum()
loss_sum += loss.item()
_, pred_index = torch.max(pred, dim=1)
num_correct = (pred_index.eq(label)).data.cpu().sum().item()
total_correct +=num_correct
for idx,l in enumerate(label):
total_seen_class[l] += 1
total_correct_class[l] += (pred_index.eq(label))[idx]
print ('finish {}/{}'.format(batch_idx*args.batch_size,len(eval_loader.dataset)))
class_accuracies = np.array(total_correct_class) / np.array(total_seen_class, dtype=np.float)
for i, name in enumerate(SHAPE_NAMES):
print ('%10s:\t%0.3f' % (name, class_accuracies[i]))
print ('eval accuracy:{}'.format(total_correct*1.0/(len(eval_loader.dataset))))
print ('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
else:
resume = args.resume
logname = os.path.join(LOG_DIR,'log.txt')
optfile = os.path.join(LOG_DIR,'opt.txt')
with open(optfile, 'wt') as opt_f:
opt_f.write('------------ Options -------------\n')
for k, v in sorted(vars(args).items()):
opt_f.write('%s: %s\n' % (str(k), str(v)))
opt_f.write('-------------- End ----------------\n')
if is_GPU:
torch.cuda.set_device(args.gpu)
my_dataset=pts_cls_dataset(datalist_path=args.data,num_points=args.num_pts,use_extra_feature=args.normal)
data_loader = torch.utils.data.DataLoader(my_dataset,
batch_size=args.batch_size, shuffle=True, num_workers=4,collate_fn=pts_collate)
if args.normal:
pts_featdim = 6
else:
pts_featdim = 3
net = PointNet_cls(num_pts=args.num_pts,feat_dim=pts_featdim)
if is_GPU:
net = net.cuda()
optimizer = torch.optim.Adam(net.parameters(), lr=args.lr, betas=(0.9, 0.999))
critenrion = nn.NLLLoss()
def save_checkpoint(epoch,model,num_iter):
torch.save({
x = self.conv4(x)
x = self.conv5(x) ##[N,1024,P]
feature_vector = torch.squeeze(self.max_pool(x)) ##[N,1024]
scores = self.classifer(feature_vector) ##[N,40]
pred = F.log_softmax(scores, dim=-1)
return pred, trans2
if __name__ == '__main__':
from data_utils import pts_cls_dataset, pts_collate
num_pts = 2048
my_dataset = pts_cls_dataset(
datalist_path='/home/yuzhe/Downloads/3d_data/modelnet/test_files.txt',
num_points=num_pts,
use_extra_feature=True)
data_loader = torch.utils.data.DataLoader(my_dataset,
batch_size=2,
shuffle=True,
collate_fn=pts_collate)
net = PointNet_cls(num_pts=num_pts, feat_dim=6)
for batch_idx, (pts, label) in enumerate(data_loader):
if False:
pts = Variable(pts.cuda())
label = Variable(label.cuda())
else:
pts = Variable(pts)
label = Variable(label)
pred, _ = net(pts)