def evaluate(args):
test_data, test_label = _load(load_train = False)
test_dataset = S3DISDataLoader(test_data,test_label)
testdataloader = DataLoader(test_dataset, batch_size=args.batch_size,shuffle=True, num_workers=args.workers)
log.debug('Building Model', args.model_name)
num_classes = 13
if args.model_name == 'pointnet':
model = PointNetSeg(num_classes, feature_transform=True, input_dims = 9)
else:
model = PointNet2SemSeg(num_classes, feature_dims = 6)
torch.backends.cudnn.benchmark = True
model = torch.nn.DataParallel(model).cuda()
log.debug('Using gpu:',args.gpu)
if args.pretrain is None:
log.err('No pretrain model')
return
log.debug('Loading pretrain model...')
state_dict = torch.load(args.pretrain)
model.load_state_dict(state_dict)
test_metrics, cat_mean_iou = test_semseg(
model.eval(),
testdataloader,
label_id_to_name,
args.model_name,
num_classes,
)
mean_iou = np.mean(cat_mean_iou)
log.info(Test_accuracy=test_metrics['accuracy'], Test_meanIOU=mean_iou)
def vis(args):
test_data, test_label = _load(load_train = False)
test_dataset = S3DISDataLoader(test_data,test_label)
testdataloader = DataLoader(test_dataset, batch_size=args.batch_size,shuffle=False, num_workers=args.workers)
log.debug('Building Model', args.model_name)
num_classes = 13
if args.model_name == 'pointnet2':
model = PointNet2SemSeg(num_classes)
else:
model = PointNetSeg(num_classes,feature_transform=True,semseg = True)
torch.backends.cudnn.benchmark = True
model = torch.nn.DataParallel(model)
model.cuda()
log.debug('Using gpu:',args.gpu)
if args.pretrain is None:
log.err('No pretrain model')
return
log.debug('Loading pretrain model...')
checkpoint = torch.load(args.pretrain)
model.load_state_dict(checkpoint)
model.eval()
cmap = plt.cm.get_cmap("hsv", 13)
cmap = np.array([cmap(i) for i in range(13)])[:, :3]
for batch_id, (points, target) in enumerate(testdataloader):
log.info('Press space to exit','press Q for next frame')
batchsize, num_point, _ = points.size()
points, target = Variable(points.float()), Variable(target.long())
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda()
if args.model_name == 'pointnet2':
pred = model(points)
else:
pred, _ = model(points)
points = points[:, :3, :].transpose(-1, 1)
pred_choice = pred.data.max(-1)[1]
for idx in range(batchsize):
pt, gt, pred = points[idx], target[idx], pred_choice[idx]
gt_color = cmap[gt.cpu().numpy() - 1, :]
pred_color = cmap[pred.cpu().numpy() - 1, :]
point_cloud = open3d.geometry.PointCloud()
point_cloud.points = open3d.utility.Vector3dVector(pt.cpu().numpy())
point_cloud.colors = open3d.Vector3dVector(gt_color)
vis = open3d.visualization.VisualizerWithKeyCallback()
vis.create_window()
vis.get_render_option().background_color = np.asarray([0, 0, 0])
vis.add_geometry(point_cloud)
vis.register_key_callback(32, lambda vis: exit())
vis.run()
vis.destroy_window()
def evaluate(args):
test_data, test_label = load_data(
'experiment/data/modelnet40_ply_hdf5_2048/', train=False)
testDataset = ModelNetDataLoader(test_data, test_label)
testDataLoader = torch.utils.data.DataLoader(testDataset,
batch_size=args.batch_size,
shuffle=False)
log.debug('Building Model', args.model_name)
if args.model_name == 'pointnet':
num_class = 40
model = PointNetCls(num_class, args.feature_transform)
else:
model = PointNet2ClsMsg()
torch.backends.cudnn.benchmark = True
model = torch.nn.DataParallel(model).cuda()
log.debug('Using gpu:', args.gpu)
if args.pretrain is None:
log.err('No pretrain model')
return
log.debug('Loading pretrain model...')
state_dict = torch.load(args.pretrain)
model.load_state_dict(state_dict)
acc = test_clf(model.eval(), testDataLoader)
log.msg(Test_Accuracy='%.5f' % (acc))
def select_avaliable(fn_list):
selected = None
for fn in fn_list:
if os.path.exists(fn):
selected = fn
break
if selected is None:
log.err(log.yellow("Could not find dataset from"), fn_list)
else:
return selected
def vis(args):
test_data, test_label = load_data(root, train=False)
log.info(test_data=test_data.shape, test_label=test_label.shape)
log.debug('Building Model', args.model_name)
if args.model_name == 'pointnet':
num_class = 40
model = PointNetCls(num_class, args.feature_transform).cuda()
else:
model = PointNet2ClsMsg().cuda()
torch.backends.cudnn.benchmark = True
model = torch.nn.DataParallel(model)
model.cuda()
log.info('Using multi GPU:', args.gpu)
if args.pretrain is None:
log.err('No pretrain model')
return
log.debug('Loading pretrain model...')
checkpoint = torch.load(args.pretrain)
model.load_state_dict(checkpoint)
model.eval()
log.info('Press space to exit, press Q for next frame')
for idx in range(test_data.shape[0]):
point_np = test_data[idx:idx + 1]
gt = test_label[idx][0]
points = torch.from_numpy(point_np)
points = points.transpose(2, 1).cuda()
pred, trans_feat = model(points)
pred_choice = pred.data.max(1)[1]
log.info(gt=class_names[gt],
pred_choice=class_names[pred_choice.cpu().numpy().item()])
point_cloud = open3d.geometry.PointCloud()
point_cloud.points = open3d.utility.Vector3dVector(point_np[0])
vis = open3d.visualization.VisualizerWithKeyCallback()
vis.create_window()
vis.get_render_option().background_color = np.asarray([0, 0, 0])
vis.add_geometry(point_cloud)
vis.register_key_callback(32, lambda vis: exit())
vis.run()
vis.destroy_window()
def evaluate(args):
cache = _load(root)
norm = True if args.model_name == 'pointnet' else False
test_ds = PartNormalDataset(root, cache, npoints=2048, split='test')
testdataloader = DataLoader(test_ds,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers))
log.info("The number of test data is:", len(test_ds))
log.info('Building Model', args.model_name)
num_classes = 16
num_part = 50
if args.model_name == 'pointnet2':
model = PointNet2PartSegMsg_one_hot(num_part)
else:
model = PointNetDenseCls(cat_num=num_classes, part_num=num_part)
torch.backends.cudnn.benchmark = True
model = torch.nn.DataParallel(model).cuda()
log.debug('Using gpu:', args.gpu)
if args.pretrain is None:
log.err('No pretrain model')
return
log.debug('Loading pretrain model...')
state_dict = torch.load(args.pretrain)
model.load_state_dict(state_dict)
log.info('Testing pretrain model...')
test_metrics, test_hist_acc, cat_mean_iou = test_partseg(
model.eval(),
testdataloader,
label_id_to_name,
args.model_name,
num_part,
)
log.info('test_hist_acc', len(test_hist_acc))
log.info(cat_mean_iou)
log.info('Test Accuracy', '%.5f' % test_metrics['accuracy'])
log.info('Class avg mIOU:', '%.5f' % test_metrics['class_avg_iou'])
log.info('Inctance avg mIOU:', '%.5f' % test_metrics['inctance_avg_iou'])
def vis(args):
cache = _load(root)
norm = True if args.model_name == 'pointnet' else False
test_ds = PartNormalDataset(root, cache, npoints=2048, split='test')
testdataloader = DataLoader(test_ds,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers))
log.info("The number of test data is:", len(test_ds))
log.info('Building Model', args.model_name)
num_classes = 16
num_part = 50
if args.model_name == 'pointnet':
model = PointNetDenseCls(cat_num=num_classes, part_num=num_part)
else:
model = PointNet2PartSegMsg_one_hot(num_part)
torch.backends.cudnn.benchmark = True
model = torch.nn.DataParallel(model)
model.cuda()
log.debug('Using multi GPU:', args.gpu)
if args.pretrain is None:
log.err('No pretrain model')
return
log.info('Loading pretrain model...')
checkpoint = torch.load(args.pretrain)
model.load_state_dict(checkpoint)
log.info('Press space to exit, press Q for next frame')
for batch_id, (points, label, target,
norm_plt) in enumerate(testdataloader):
batchsize, num_point, _ = points.size()
points, label, target, norm_plt = points.float(), label.long(
), target.long(), norm_plt.float()
points = points.transpose(2, 1)
norm_plt = norm_plt.transpose(2, 1)
points, label, target, norm_plt = points.cuda(), label.squeeze().cuda(
), target.cuda(), norm_plt.cuda()
if args.model_name == 'pointnet':
labels_pred, seg_pred, _ = model(points, to_categorical(label, 16))
else:
seg_pred = model(points, norm_plt, to_categorical(label, 16))
pred_choice = seg_pred.max(-1)[1]
log.info(seg_pred=seg_pred.shape, pred_choice=pred_choice.shape)
log.info(seg_pred=seg_pred.shape, pred_choice=pred_choice.shape)
cmap_plt = plt.cm.get_cmap("hsv", num_part)
cmap_list = [cmap_plt(i)[:3] for i in range(num_part)]
np.random.shuffle(cmap_list)
cmap = np.array(cmap_list)
#log.info('points',points.shape,'label',label.shape,'target',target.shape,'norm_plt',norm_plt.shape)
for idx in range(batchsize):
pt, gt, pred = points[idx].transpose(
1, 0), target[idx], pred_choice[idx].transpose(-1, 0)
# log.info('pt',pt.size(),'gt',gt.size(),'pred',pred.shape)
gt_color = cmap[gt.cpu().numpy() - 1, :]
pred_color = cmap[pred.cpu().numpy() - 1, :]
point_cloud = open3d.geometry.PointCloud()
point_cloud.points = open3d.utility.Vector3dVector(
pt.cpu().numpy())
point_cloud.colors = open3d.utility.Vector3dVector(pred_color)
vis = open3d.visualization.VisualizerWithKeyCallback()
vis.create_window()
vis.get_render_option().background_color = np.asarray([0, 0, 0])
vis.add_geometry(point_cloud)
vis.register_key_callback(32, lambda vis: exit())
vis.run()
vis.destroy_window()