def main():
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
print("\n**************************")
for k, v in config['common'].items():
setattr(args, k, v)
print('\n[%s]:'%(k), v)
print("\n**************************\n")
try:
os.makedirs(args.save_path)
except OSError:
pass
train_transforms = transforms.Compose([
d_utils.PointcloudToTensor()
])
test_transforms = transforms.Compose([
d_utils.PointcloudToTensor()
])
train_dataset = ShapeNetPart(root = args.data_root, num_points = args.num_points, split = 'trainval', normalize = True, transforms = train_transforms)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers),
pin_memory=True
)
global test_dataset
test_dataset = ShapeNetPart(root = args.data_root, num_points = args.num_points, split = 'test', normalize = True, transforms = test_transforms)
test_dataloader = DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers),
pin_memory=True
)
model = RSCNN_MSN(num_classes = args.num_classes, input_channels = args.input_channels, relation_prior = args.relation_prior, use_xyz = True)
model.cuda()
optimizer = optim.Adam(
model.parameters(), lr=args.base_lr, weight_decay=args.weight_decay)
lr_lbmd = lambda e: max(args.lr_decay**(e // args.decay_step), args.lr_clip / args.base_lr)
bnm_lmbd = lambda e: max(args.bn_momentum * args.bn_decay**(e // args.decay_step), args.bnm_clip)
lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lbmd)
bnm_scheduler = pt_utils.BNMomentumScheduler(model, bnm_lmbd)
if args.checkpoint is not '':
model.load_state_dict(torch.load(args.checkpoint))
print('Load model successfully: %s' % (args.checkpoint))
criterion = nn.CrossEntropyLoss()
num_batch = len(train_dataset)/args.batch_size
# training
train(train_dataloader, test_dataloader, model, criterion, optimizer, lr_scheduler, bnm_scheduler, args, num_batch)
def main():
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
for k, v in config['common'].items():
setattr(args, k, v)
test_transforms = transforms.Compose([d_utils.PointcloudToTensor()])
test_dataset = ModelNet40Cls(num_points=args.num_points,
root=args.data_root,
transforms=test_transforms,
train=False)
test_dataloader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers),
pin_memory=True)
model = DensePoint(num_classes=args.num_classes,
input_channels=args.input_channels,
use_xyz=True)
model.cuda()
if args.checkpoint is not '':
model.load_state_dict(torch.load(args.checkpoint))
print('Load model successfully: %s' % (args.checkpoint))
# evaluate
PointcloudScale = d_utils.PointcloudScale() # initialize random scaling
model.eval()
global_acc = 0
for i in range(NUM_REPEAT):
preds = []
labels = []
s = time.time()
for j, data in enumerate(test_dataloader, 0):
points, target = data
points, target = points.cuda(), target.cuda()
points, target = Variable(points,
volatile=True), Variable(target,
volatile=True)
# points [batch_size, num_points, dimensions], e.g., [256, 2048, 3]
# furthest point sampling
# fps_idx = pointnet2_utils.furthest_point_sample(points, 1200) # (B, npoint)
# random sampling
fps_idx = np.random.randint(0,
points.shape[1] - 1,
size=[points.shape[0], 1200])
fps_idx = torch.from_numpy(fps_idx).type(torch.IntTensor).cuda()
pred = 0
for v in range(NUM_VOTE):
new_fps_idx = fps_idx[:,
np.random.choice(1200, args.
num_points, False)]
new_points = pointnet2_utils.gather_operation(
points.transpose(1, 2).contiguous(),
new_fps_idx).transpose(1, 2).contiguous()
if v > 0:
new_points.data = PointcloudScale(new_points.data)
pred += F.softmax(model(new_points), dim=1)
pred /= NUM_VOTE
target = target.view(-1)
_, pred_choice = torch.max(pred.data, -1)
preds.append(pred_choice)
labels.append(target.data)
e = time.time()
preds = torch.cat(preds, 0)
labels = torch.cat(labels, 0)
acc = (preds == labels).sum() / labels.numel()
if acc > global_acc:
global_acc = acc
print('Repeat %3d \t Acc: %0.6f' % (i + 1, acc))
print('time (secs) for 1 epoch: ', (e - s))
print('\nBest voting acc: %0.6f' % (global_acc))
def main():
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
for k, v in config['common'].items():
setattr(args, k, v)
test_transforms = transforms.Compose([
d_utils.PointcloudToTensor()
])
test_dataset = ShapeNetPart(root = args.data_root, num_points = args.num_points, split = 'test', normalize = True, transforms = test_transforms)
test_dataloader = DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers),
pin_memory=True
)
model = RSCNN_MSN(num_classes = args.num_classes, input_channels = args.input_channels, relation_prior = args.relation_prior, use_xyz = True)
model.cuda()
if args.checkpoint is not '':
model.load_state_dict(torch.load(args.checkpoint))
print('Load model successfully: %s' % (args.checkpoint))
# evaluate
PointcloudScale = d_utils.PointcloudScale(scale_low=0.87, scale_high=1.15) # initialize random scaling
model.eval()
global_Class_mIoU, global_Inst_mIoU = 0, 0
seg_classes = test_dataset.seg_classes
seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table}
for cat in seg_classes.keys():
for label in seg_classes[cat]:
seg_label_to_cat[label] = cat
for i in range(NUM_REPEAT):
shape_ious = {cat:[] for cat in seg_classes.keys()}
for _, data in enumerate(test_dataloader, 0):
points, target, cls = data
points, target = Variable(points, volatile=True), Variable(target, volatile=True)
points, target = points.cuda(), target.cuda()
batch_one_hot_cls = np.zeros((len(cls), 16)) # 16 object classes
for b in range(len(cls)):
batch_one_hot_cls[b, int(cls[b])] = 1
batch_one_hot_cls = torch.from_numpy(batch_one_hot_cls)
batch_one_hot_cls = Variable(batch_one_hot_cls.float().cuda())
pred = 0
new_points = Variable(torch.zeros(points.size()[0], points.size()[1], points.size()[2]).cuda(), volatile=True)
for v in range(NUM_VOTE):
if v > 0:
new_points.data = PointcloudScale(points.data)
pred += F.softmax(model(new_points, batch_one_hot_cls), dim = 2)
pred /= NUM_VOTE
pred = pred.data.cpu()
target = target.data.cpu()
pred_val = torch.zeros(len(cls), args.num_points).type(torch.LongTensor)
# pred to the groundtruth classes (selected by seg_classes[cat])
for b in range(len(cls)):
cat = seg_label_to_cat[target[b, 0]]
logits = pred[b, :, :] # (num_points, num_classes)
pred_val[b, :] = logits[:, seg_classes[cat]].max(1)[1] + seg_classes[cat][0]
for b in range(len(cls)):
segp = pred_val[b, :]
segl = target[b, :]
cat = seg_label_to_cat[segl[0]]
part_ious = [0.0 for _ in range(len(seg_classes[cat]))]
for l in seg_classes[cat]:
if torch.sum((segl == l) | (segp == l)) == 0:
# part is not present in this shape
part_ious[l - seg_classes[cat][0]] = 1.0
else:
part_ious[l - seg_classes[cat][0]] = torch.sum((segl == l) & (segp == l)) / float(torch.sum((segl == l) | (segp == l)))
shape_ious[cat].append(np.mean(part_ious))
instance_ious = []
for cat in shape_ious.keys():
for iou in shape_ious[cat]:
instance_ious.append(iou)
shape_ious[cat] = np.mean(shape_ious[cat])
mean_class_ious = np.mean(list(shape_ious.values()))
print('\n------ Repeat %3d ------' % (i + 1))
for cat in sorted(shape_ious.keys()):
print('%s: %0.6f'%(cat, shape_ious[cat]))
print('Class_mIoU: %0.6f' % (mean_class_ious))
print('Instance_mIoU: %0.6f' % (np.mean(instance_ious)))
if mean_class_ious > global_Class_mIoU:
global_Class_mIoU = mean_class_ious
global_Inst_mIoU = np.mean(instance_ious)
print('\nBest voting Class_mIoU = %0.6f, Instance_mIoU = %0.6f' % (global_Class_mIoU, global_Inst_mIoU))
def main():
# os.system('cp models/rscnn_ssn_cls.py cls/') # bkp of train procedure
# os.system('cp utils/pointnet2_utils.py cls/')
# os.system('cp utils/pointnet2_modules.py cls/')
# os.system('cp utils/csrc/ellipsoid_query_gpu.cu cls/')
# os.system('cp utils/csrc/ellipsoid_query.c cls/')
# os.system('cp cfgs/config_ssn_cls.yaml cls/')
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
print("\n**************************")
for k, v in config['common'].items():
setattr(args, k, v)
print('\n[%s]:' % (k), v)
print("\n**************************\n")
try:
os.makedirs(args.save_path)
except OSError:
pass
train_transforms = transforms.Compose([d_utils.PointcloudToTensor()])
test_transforms = transforms.Compose([d_utils.PointcloudToTensor()])
train_dataset = ModelNet40Cls(num_points=args.num_points,
root=args.data_root,
transforms=train_transforms)
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers),
pin_memory=True)
test_dataset = ModelNet40Cls(num_points=args.num_points,
root=args.data_root,
transforms=test_transforms,
train=False)
test_dataloader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers),
pin_memory=True)
model = RSCNN_SSN(num_classes=args.num_classes,
input_channels=args.input_channels,
relation_prior=args.relation_prior,
use_xyz=True)
model.cuda()
optimizer = optim.Adam(model.parameters(),
lr=args.base_lr,
weight_decay=args.weight_decay)
lr_lbmd = lambda e: max(args.lr_decay**(e // args.decay_step), args.lr_clip
/ args.base_lr)
bnm_lmbd = lambda e: max(
args.bn_momentum * args.bn_decay**
(e // args.decay_step), args.bnm_clip)
lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lbmd)
bnm_scheduler = pt_utils.BNMomentumScheduler(model, bnm_lmbd)
if args.checkpoint is not '':
model.load_state_dict(torch.load(args.checkpoint))
print('Load model successfully: %s' % (args.checkpoint))
criterion = nn.CrossEntropyLoss()
num_batch = len(train_dataset) / args.batch_size
# training
train(train_dataloader, test_dataloader, model, criterion, optimizer,
lr_scheduler, bnm_scheduler, args, num_batch)
help="Name for run in tensorboard_logger")
parser.add_argument('--visdom-port', type=int, default=8097)
return parser.parse_args()
lr_clip = 1e-5
bnm_clip = 1e-2
if __name__ == "__main__":
args = parse_args()
BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
transforms = transforms.Compose([
d_utils.PointcloudToTensor(),
d_utils.PointcloudScale(),
d_utils.PointcloudRotate(),
d_utils.PointcloudRotatePerturbation(),
d_utils.PointcloudTranslate(),
d_utils.PointcloudJitter(),
d_utils.PointcloudRandomInputDropout()
])
test_set = ModelNet40Cls(args.num_points,
BASE_DIR,
transforms=transforms,
train=False)
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
shuffle=True,
def main():
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
for k, v in config['common'].items():
setattr(args, k, v)
test_transforms = transforms.Compose([d_utils.PointcloudToTensor()])
test_dataset = ModelNet40Cls(num_points=args.num_points,
root=args.data_root,
transforms=test_transforms,
train=False)
test_dataloader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers),
pin_memory=False)
model = RSCNN_SSN(num_classes=args.num_classes,
input_channels=args.input_channels,
relation_prior=args.relation_prior,
use_xyz=True)
# for multi GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available() and torch.cuda.device_count() >= 2:
model = nn.DataParallel(model, device_ids=[0, 1])
model.to(device)
elif torch.cuda.is_available() and torch.cuda.device_count() == 1:
model.cuda()
if args.checkpoint is not '':
model.load_state_dict(torch.load(args.checkpoint))
print('Load model successfully: %s' % (args.checkpoint))
# evaluate
PointcloudScale = d_utils.PointcloudScale() # initialize random scaling
model.eval()
global_acc = 0
for i in range(NUM_REPEAT):
preds = []
labels = []
for j, data in enumerate(test_dataloader, 0):
points, target = data
points, target = points.cuda(), target.cuda()
points, target = Variable(points,
volatile=True), Variable(target,
volatile=True)
# fastest point sampling
fps_idx = pointnet2_utils.furthest_point_sample(
points, 1200) # (B, npoint)
pred = 0
for v in range(NUM_VOTE):
new_fps_idx = fps_idx[:,
np.random.choice(1200, args.
num_points, False)]
new_points = pointnet2_utils.gather_operation(
points.transpose(1, 2).contiguous(),
new_fps_idx).transpose(1, 2).contiguous()
if v > 0:
new_points.data = PointcloudScale(new_points.data)
pred += F.softmax(model(new_points), dim=1)
pred /= NUM_VOTE
target = target.view(-1)
_, pred_choice = torch.max(pred.data, -1)
preds.append(pred_choice)
labels.append(target.data)
preds = torch.cat(preds, 0)
labels = torch.cat(labels, 0)
acc = (preds == labels).sum() / labels.numel()
if acc > global_acc:
global_acc = acc
print('Repeat %3d \t Acc: %0.6f' % (i + 1, acc))
print('\nBest voting acc: %0.6f' % (global_acc))
def main():
global svm_best_acc40
svm_best_acc40 = 0
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
print("\n**************************")
for k, v in config['common'].items():
setattr(args, k, v)
print('\n[%s]:' % (k), v)
print("\n**************************\n")
os.makedirs('./ckpts/', exist_ok=True)
# dataset
train_transforms = transforms.Compose([d_utils.PointcloudToTensor()])
test_transforms = transforms.Compose([d_utils.PointcloudToTensor()])
ss_dataset = ModelNetCls(transforms=train_transforms,
self_supervision=True,
use_normal=True,
dataset_rate=1)
ss_dataloader = DataLoader(ss_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers),
pin_memory=True,
worker_init_fn=worker_init_fn)
if args.dataset == 'modelnet':
train_dataset = ModelNetCls(transforms=train_transforms,
self_supervision=False,
train=True)
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers),
pin_memory=True,
worker_init_fn=worker_init_fn)
test_dataset = ModelNetCls(transforms=test_transforms,
self_supervision=False,
train=False)
test_dataloader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers),
pin_memory=True)
elif args.dataset == 'scannet':
train_dataset = ScanNetCls(transforms=train_transforms,
self_supervision=False,
train=True)
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers),
pin_memory=True,
worker_init_fn=worker_init_fn)
test_dataset = ScanNetCls(transforms=test_transforms,
self_supervision=False,
train=False)
test_dataloader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers),
pin_memory=True)
elif args.dataset == 'scanobjectnn':
train_dataset = ScanObjectNNCls(transforms=train_transforms,
self_supervision=False,
train=True)
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers),
pin_memory=True,
worker_init_fn=worker_init_fn)
test_dataset = ScanObjectNNCls(transforms=test_transforms,
self_supervision=False,
train=False)
test_dataloader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers),
pin_memory=True)
else:
raise NotImplementedError
# models
n_rkhs = 512
if args.arch == 'pointnet2':
encoder = PointNet2(n_rkhs=n_rkhs,
input_channels=args.input_channels,
use_xyz=True,
point_wise_out=True,
multi=args.multiplier)
print('Using PointNet++ backbone')
elif args.arch == 'rscnn':
encoder = RSCNN_SSN(n_rkhs=n_rkhs,
input_channels=args.input_channels,
relation_prior=args.relation_prior,
use_xyz=True,
point_wise_out=True,
multi=args.multiplier)
print('Using RSCNN backbone')
else:
raise NotImplementedError
encoder = nn.DataParallel(encoder).cuda()
decoer = FoldingNet(in_channel=n_rkhs * 3)
decoer = nn.DataParallel(decoer).cuda()
# optimizer
optimizer = optim.Adam(list(encoder.parameters()) +
list(decoer.parameters()),
lr=args.base_lr,
weight_decay=args.weight_decay)
# resume
begin_epoch = -1
checkpoint_name = './ckpts/' + args.name + '.pth'
if os.path.isfile(checkpoint_name):
checkpoint = torch.load(checkpoint_name)
encoder.load_state_dict(checkpoint['encoder_state_dict'])
decoer.load_state_dict(checkpoint['decoder_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
svm_best_acc40 = checkpoint['svm_best_acc40']
begin_epoch = checkpoint['epoch'] - 1
print("-> loaded checkpoint %s (epoch: %d)" %
(checkpoint_name, begin_epoch))
lr_lbmd = lambda e: max(args.lr_decay**(e // args.decay_step), args.lr_clip
/ args.base_lr)
bnm_lmbd = lambda e: max(
args.bn_momentum * args.bn_decay**
(e // args.decay_step), args.bnm_clip)
lr_scheduler = lr_sched.LambdaLR(optimizer,
lr_lbmd,
last_epoch=begin_epoch)
bnm_scheduler = pt_utils.BNMomentumScheduler(encoder,
bnm_lmbd,
last_epoch=begin_epoch)
num_batch = len(ss_dataset) / args.batch_size
args.val_freq_epoch = 1.0
# training & evaluation
train(ss_dataloader, train_dataloader, test_dataloader, encoder, decoer,
optimizer, lr_scheduler, bnm_scheduler, args, num_batch, begin_epoch)
def main():
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
print("\n**************************")
for k, v in config['common'].items():
setattr(args, k, v)
print('\n[%s]:'%(k), v)
print("\n**************************\n")
try:
os.makedirs(args.save_path)
except OSError:
pass
train_transforms = transforms.Compose([
d_utils.PointcloudToTensor(),
d_utils.PointcloudScaleAndTranslate(),
d_utils.PointcloudRandomInputDropout()
])
test_transforms = transforms.Compose([
d_utils.PointcloudToTensor(),
#d_utils.PointcloudScaleAndTranslate()
])
train_dataset = ModelNet40Cls(num_points = args.num_points, root = args.data_root, transforms=train_transforms)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers)
)
test_dataset = ModelNet40Cls(num_points = args.num_points, root = args.data_root, transforms=test_transforms, train=False)
test_dataloader = DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers)
)
model = RSCNN_SSN(num_classes = args.num_classes, input_channels = args.input_channels, relation_prior = args.relation_prior, use_xyz = True)
# for multi GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available() and torch.cuda.device_count()>=2:
model = nn.DataParallel(model, device_ids=[0, 1])
model.to(device)
elif torch.cuda.is_available() and torch.cuda.device_count()==1:
model.cuda()
optimizer = optim.Adam(
model.parameters(), lr=args.base_lr, weight_decay=args.weight_decay)
lr_lbmd = lambda e: max(args.lr_decay**(e // args.decay_step), args.lr_clip / args.base_lr)
bnm_lmbd = lambda e: max(args.bn_momentum * args.bn_decay**(e // args.decay_step), args.bnm_clip)
lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lbmd)
bnm_scheduler = pt_utils.BNMomentumScheduler(model, bnm_lmbd)
if args.checkpoint is not '':
model.load_state_dict(torch.load(args.checkpoint))
print('Load model successfully: %s' % (args.checkpoint))
criterion = nn.CrossEntropyLoss()
num_batch = len(train_dataset)/args.batch_size
# training
train(train_dataloader, test_dataloader, model, criterion, optimizer, lr_scheduler, bnm_scheduler, args, num_batch)
def main():
global logger
args = parser.parse_args()
with open(args.config) as f:
config = yaml.safe_load(f)
for k, v in config['common'].items():
setattr(args, k, v)
output_dir = args.save_path
if output_dir:
import time
msg = 'init_train'
output_dir = os.path.join(
output_dir, "train_{}_{}".format(time.strftime("%m_%d_%H_%M_%S"),
msg))
os.makedirs(output_dir)
logger = get_logger("RS-CNN", output_dir, prefix="train")
logger.info("Running with config:\n{}".format(args))
train_transforms = transforms.Compose([d_utils.PointcloudToTensor()])
test_transforms = transforms.Compose([d_utils.PointcloudToTensor()])
train_dataset = ModelNet40Cls(num_points=args.num_points,
root=args.data_root,
transforms=train_transforms)
train_dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers),
pin_memory=True)
test_dataset = ModelNet40Cls(num_points=args.num_points,
root=args.data_root,
transforms=test_transforms,
train=False)
test_dataloader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers),
pin_memory=True)
model = RSCNN_SSN(num_classes=args.num_classes,
input_channels=args.input_channels,
relation_prior=args.relation_prior,
use_xyz=True)
model.cuda()
optimizer = optim.Adam(model.parameters(),
lr=args.base_lr,
weight_decay=args.weight_decay)
lr_lbmd = lambda e: max(args.lr_decay**(e // args.decay_step), args.lr_clip
/ args.base_lr)
bnm_lmbd = lambda e: max(
args.bn_momentum * args.bn_decay**
(e // args.decay_step), args.bnm_clip)
lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lbmd)
bnm_scheduler = pt_utils.BNMomentumScheduler(model, bnm_lmbd)
if args.checkpoint is not '':
model.load_state_dict(torch.load(args.checkpoint))
logger.info('Load model successfully: %s' % (args.checkpoint))
criterion = nn.CrossEntropyLoss()
num_batch = len(train_dataset) / args.batch_size
# training
train(train_dataloader, test_dataloader, model, criterion, optimizer,
lr_scheduler, bnm_scheduler, args, num_batch)
def main():
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
for k, v in config['common'].items():
setattr(args, k, v)
test_transforms = transforms.Compose([d_utils.PointcloudToTensor()])
test_dataset = Bosphorus_eval(num_points=args.num_points,
root=args.data_root,
transforms=test_transforms)
test_dataloader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers))
model = RSCNN_SSN(num_classes=args.num_classes,
input_channels=args.input_channels,
relation_prior=args.relation_prior,
use_xyz=True)
model.cuda()
if args.checkpoint is not '':
model.load_state_dict(torch.load(args.checkpoint))
print('Load model successfully: %s' % (args.checkpoint))
# model is used for feature extraction, so no need FC layers
model.FC_layer = nn.Linear(1024, 1024, bias=False).cuda()
for para in model.parameters():
para.requires_grad = False
nn.init.eye_(model.FC_layer.weight)
# evaluate
#PointcloudScale = d_utils.PointcloudScale() # initialize random scaling
model.eval()
global_acc = 0
with torch.no_grad():
Total_samples = 0
Correct = 0
gallery_points, gallery_labels = test_dataset.get_gallery()
gallery_points, gallery_labels = gallery_points.cuda(
), gallery_labels.cuda()
gallery_points = Variable(gallery_points)
gallery_pred = model(gallery_points)
print(gallery_pred.size())
gallery_pred = F.normalize(gallery_pred)
for j, data in enumerate(test_dataloader, 0):
probe_points, probe_labels = data
probe_points, probe_labels = probe_points.cuda(
), probe_labels.cuda()
probe_points = Variable(probe_points)
# get feature vetor for probe and gallery set from model
probe_pred = model(probe_points)
probe_pred = F.normalize(probe_pred)
# make tensor to size (probe_num, gallery_num, C)
probe_tmp = probe_pred.unsqueeze(1).expand(probe_pred.shape[0],
gallery_pred.shape[0],
probe_pred.shape[1])
gallery_tmp = gallery_pred.unsqueeze(0).expand(
probe_pred.shape[0], gallery_pred.shape[0],
gallery_pred.shape[1])
results = torch.sum(torch.mul(probe_tmp, gallery_tmp),
dim=2) # cosine distance
results = torch.argmax(results, dim=1)
Total_samples += probe_points.shape[0]
for i in np.arange(0, results.shape[0]):
if gallery_labels[results[i]] == probe_labels[i]:
Correct += 1
print('Total_samples:{}'.format(Total_samples))
acc = float(Correct / Total_samples)
if acc > global_acc:
global_acc = acc
print('Repeat %3d \t Acc: %0.6f' % (i + 1, acc))
print('\nBest voting acc: %0.6f' % (global_acc))
