def main(argv=None):
print('Hello! This is XXXXXX Program')
trn_ds = PartDataset(root=opt.directory, npoints=opt.num_points, classification=False, class_choice=['pipe'])
val_ds = PartDataset(root=opt.directory, npoints=opt.num_points, classification=False, class_choice=['pipe'], train=False)
num_classes = trn_ds.num_seg_classes
trn_dl = DataLoader(trn_ds, batch_size=opt.batch_size, shuffle=True, num_workers=0, pin_memory=True)
val_dl = DataLoader(val_ds, batch_size=32, shuffle=False, num_workers=0, pin_memory=True)
data = ModelData(opt.directory, trn_dl, val_dl)
classifier = PointNetDenseCls(num_points=opt.num_points, k=num_classes)
learn = ConvLearner.from_model_data(classifier.cuda(), data=data)
learn.crit = nn.CrossEntropyLoss()
learn.metrics = [accuracy]
learn.clip = 1e-1
learn.fit(1.5, 1, wds=1e-4, cycle_len=20, use_clr_beta=(12, 15, 0.95, 0.85))
preds, targs = learn.TTA()
print('classes', num_classes)
try:
os.makedirs(opt.outf)
except OSError:
pass
blue = lambda x:'\033[94m' + x + '\033[0m'
classifier = PointNetDenseCls(num_points=num_points, k=num_classes)
if opt.model != '':
classifier.load_state_dict(torch.load(opt.model))
optimizer = optim.SGD(classifier.parameters(), lr=0.01, momentum=0.9)
classifier.cuda()
num_batch = len(dataset)/opt.batchSize
max_acc = -1
for epoch in range(opt.nepoch):
for i, data in enumerate(dataloader, 0):
points, target = data
points, target = Variable(points), Variable(target)
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda()
optimizer.zero_grad()
pred, _ = classifier(points)
pred = pred.view(-1, num_classes)
target = target.view(-1,1)[:,0] - 1
#print(pred.size(), target.size())
def main(argv=None):
print('Hello! This is XXXXXX Program')
num_points = 2048
dataset = PartDataset(root='DATA/ARLab/objects',
npoints=num_points,
classification=False,
class_choice=['pipe'])
# dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize, shuffle=False, num_workers=int(opt.workers))
dataloader = DataLoader(dataset,
batch_size=opt.batchSize,
shuffle=False,
num_workers=int(opt.workers))
test_dataset = PartDataset(root='DATA/ARLab/objects',
npoints=num_points,
classification=False,
class_choice=['pipe'])
testdataloader = torch.utils.data.DataLoader(test_dataset,
batch_size=opt.batchSize,
shuffle=True,
num_workers=int(opt.workers))
num_classes = dataset.num_seg_classes
blue = lambda x: '\033[94m' + x + '\033[0m'
classifier = PointNetDenseCls(num_points=num_points, k=num_classes)
if opt.model != '':
classifier.load_state_dict(torch.load(opt.model))
optimizer = optim.SGD(classifier.parameters(), lr=0.01, momentum=0.9)
classifier.cuda()
num_batch = len(dataset) / opt.batchSize
for epoch in range(opt.nepoch):
for i, data in enumerate(dataloader, 0):
points, target = data
points, target = Variable(points), Variable(target)
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda()
optimizer.zero_grad()
print(points.shape)
pred, _ = classifier(points)
pred = pred.view(-1, num_classes)
target = target.view(-1, 1)[:, 0] - 1
# print(pred.size(), target.size())
loss = F.nll_loss(pred, target)
loss.backward()
optimizer.step()
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum()
print('[%d: %d/%d] train loss: %f accuracy: %f' %
(epoch, i, num_batch, loss.item(),
correct.item() / float(list(target.shape)[0])))
if i % 10 == 0:
j, data = next(enumerate(testdataloader, 0))
points, target = data
points, target = Variable(points), Variable(target)
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda()
pred, _ = classifier(points)
pred = pred.view(-1, num_classes)
target = target.view(-1, 1)[:, 0] - 1
loss = F.nll_loss(pred, target)
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum()
print('[%d: %d/%d] %s loss: %f accuracy: %f' %
(epoch, i, num_batch, blue('test'), loss.item(),
correct.item() / float(list(target.shape)[0])))
torch.save(classifier.state_dict(),
'%s/seg_model_%d.pth' % (opt.outf, epoch))
print('classes', num_classes)
try:
os.makedirs(opt.outf)
except OSError:
pass
blue = lambda x:'\033[94m' + x + '\033[0m'
classifier = PointNetDenseCls(k = num_classes)
if opt.model != '':
classifier.load_state_dict(torch.load(opt.model))
optimizer = optim.SGD(classifier.parameters(), lr=0.01, momentum=0.9)
classifier.cuda()
num_batch = len(dataset)/opt.batchSize
for epoch in range(opt.nepoch):
for i, data in enumerate(dataloader, 0):
points, target = data
points, target = Variable(points), Variable(target)
points = points.transpose(2,1)
points, target = points.cuda(), target.cuda()
optimizer.zero_grad()
classifier = classifier.train()
pred, _ = classifier(points)
pred = pred.view(-1, num_classes)
target = target.view(-1,1)[:,0] - 1
#print(pred.size(), target.size())