def main(args):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
''' === Set up Loggers and Load Data === '''
MyLogger = TrainLogger(args, name=args.model.upper(), subfold='completion')
os.makedirs(os.path.join(MyLogger.experiment_dir, 'plots'), exist_ok=True)
writer = SummaryWriter(os.path.join(MyLogger.experiment_dir, 'runs'))
MyLogger.logger.info('Load dataset %s' % args.dataset)
if args.dataset == 'modelnet':
lmdb_train = './data/modelnet/train.lmdb'
lmdb_valid = './data/modelnet/test.lmdb'
elif args.dataset == 'shapenet':
lmdb_train = 'data/shapenet/train.lmdb'
lmdb_valid = 'data/shapenet/valid.lmdb'
else:
raise ValueError("Dataset is not available, it should be either ModelNet or ShapeNet")
assert (args.gt_pts == args.grid_size ** 2 * args.num_coarse)
df_train, num_train = lmdb_dataflow(
lmdb_train, args.batch_size, args.input_pts, args.gt_pts, is_training=True)
df_valid, num_valid = lmdb_dataflow(
lmdb_valid, args.batch_size, args.input_pts, args.gt_pts, is_training=False)
train_gen, valid_gen = df_train.get_data(), df_valid.get_data()
total_steps = num_train // args.batch_size * args.epoch
''' === Load Model and Backup Scripts === '''
MODEL = importlib.import_module(args.model)
shutil.copy(os.path.abspath(__file__), MyLogger.log_dir)
shutil.copy('./models/%s.py' % args.model, MyLogger.log_dir)
# multiple GPUs usage
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
completer = MODEL.get_model(args=args, grid_size=args.grid_size,
grid_scale=args.grid_scale, num_coarse=args.num_coarse).to(device)
criterion = MODEL.get_loss().to(device)
completer = torch.nn.DataParallel(completer)
# nn.DataParallel has its own issues (slow, memory expensive), bearable
# some optional advanced solutions: https://zhuanlan.zhihu.com/p/145427849
print('=' * 33)
print('Using %d GPU,' % torch.cuda.device_count(), 'Indices are: %s' % args.gpu)
print('=' * 33)
''' === Restore Model from Checkpoints, If there is any === '''
if args.restore:
checkpoint = torch.load(args.restore_path)
completer = copy_parameters(completer, checkpoint, verbose=True)
MyLogger.logger.info('Use pre-trained model from %s' % args.restore_path)
MyLogger.step, MyLogger.epoch = checkpoint['step'], checkpoint['epoch']
else:
MyLogger.logger.info('No pre-trained model, start training from scratch...')
''' IMPORTANT: for completion, no weight decay in Adam, no batch norm in decoder!'''
optimizer = torch.optim.Adam(
completer.parameters(),
lr=args.lr,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0)
# weight_decay=1e-4)
# For the sake of simplicity, we save the momentum decay in the batch norm
# scheduler = StepLR(optimizer, step_size=20, gamma=0.7) -> instead we define these manually
LEARNING_RATE_CLIP = 0.01 * args.lr
def vary2fix(inputs, npts, batch_size=args.batch_size, num_point=args.input_pts):
"""upsample/downsample varied input points into fixed length
:param inputs: input points cloud
:param npts: describe how many points of each input object
:param batch_size: training batch size
:param num_point: number of points of per occluded object
:return: fixed length of points of each object
"""
inputs_ls = np.split(inputs[0], npts.cumsum())
ret_inputs = np.zeros((1, batch_size * num_point, 3))
ret_npts = npts.copy()
for idx, obj in enumerate(inputs_ls[:-1]):
if len(obj) <= num_point:
select_idx = np.concatenate([
np.arange(len(obj)), np.random.choice(len(obj), num_point - len(obj))])
else:
select_idx = np.arange(len(obj))
np.random.shuffle(select_idx)
ret_inputs[0][idx * num_point:(idx + 1) * num_point] = obj[select_idx].copy()
ret_npts[idx] = num_point
return ret_inputs, ret_npts
def piecewise_constant(global_step, boundaries, values):
"""substitute for tf.train.piecewise_constant:
https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/piecewise_constant
global_step can be either training epoch or training step
"""
if len(boundaries) != len(values) - 1:
raise ValueError(
"The length of boundaries should be 1 less than the length of values")
if global_step <= boundaries[0]:
return values[0]
elif global_step > boundaries[-1]:
return values[-1]
else:
for low, high, v in zip(boundaries[:-1], boundaries[1:], values[1:-1]):
if (global_step > low) & (global_step <= high):
return v
total_time, train_start = 0, time.time()
for step in range(MyLogger.step + 1, total_steps + 1):
''' === Training === '''
start = time.time()
epoch = step * args.batch_size // num_train + 1
lr = max(args.lr * (args.lr_decay ** (epoch // args.step_size)), LEARNING_RATE_CLIP)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# follow the original alpha setting for ShapeNet Dataset in PCN paper:
alpha = piecewise_constant(step, [10000, 20000, 50000], [0.01, 0.1, 0.5, 1.0])
writer.add_scalar('Learning Rate', lr, step)
writer.add_scalar('Alpha', alpha, step)
ids, inputs, npts, gt = next(train_gen)
if args.dataset == 'shapenet':
inputs, _ = vary2fix(inputs, npts)
completer.train()
optimizer.zero_grad()
inputs = inputs.reshape(args.batch_size, args.input_pts, 3)
inputs, gt = torch.Tensor(inputs).transpose(2, 1).cuda(), torch.Tensor(gt).cuda()
pred_coarse, pred_fine = completer(inputs)
loss = criterion(pred_coarse, pred_fine, gt, alpha)
loss.backward()
optimizer.step()
total_time += time.time() - start
writer.add_scalar('Loss', loss, step)
if step % args.steps_print == 0:
MyLogger.logger.info('epoch %d step %d alpha %.2f loss %.8f time per step %.2f s' %
(epoch, step, alpha, loss, total_time / args.steps_print))
total_time = 0
''' === Validating === '''
if step % args.steps_eval == 0:
with torch.no_grad():
completer.eval()
MyLogger.logger.info('Testing...')
num_eval_steps, eval_loss, eval_time = num_valid // args.batch_size, 0, 0
for eval_step in range(num_eval_steps):
start = time.time()
_, inputs, npts, gt = next(valid_gen)
if args.dataset == 'shapenet':
inputs, _ = vary2fix(inputs, npts)
inputs = inputs.reshape(args.batch_size, args.input_pts, 3)
inputs, gt = torch.Tensor(inputs).transpose(2, 1).cuda(), torch.Tensor(gt).cuda()
pred_coarse, pred_fine = completer(inputs)
loss = criterion(pred_coarse, pred_fine, gt, alpha)
eval_loss += loss
eval_time += time.time() - start
MyLogger.logger.info('epoch %d step %d validation loss %.8f time per step %.2f s' %
(epoch, step, eval_loss / num_eval_steps, eval_time / num_eval_steps))
''' === Visualisation === '''
if step % args.steps_visu == 0:
all_pcds = [item.detach().cpu().numpy() for item in [
inputs.transpose(2, 1), pred_coarse, pred_fine, gt]]
for i in range(args.batch_size):
plot_path = os.path.join(MyLogger.experiment_dir, 'plots',
'epoch_%d_step_%d_%s.png' % (epoch, step, ids[i]))
pcds = [x[i] for x in all_pcds]
plot_pcd_three_views(plot_path, pcds,
['input', 'coarse output', 'fine output', 'ground truth'])
trained_epoch = epoch - 1
if (trained_epoch % args.epochs_save == 0) and (trained_epoch != 0) and \
not os.path.exists(os.path.join(MyLogger.checkpoints_dir,
'model_epoch_%d.pth' % trained_epoch)):
state = {
'step': step,
'epoch': epoch,
'model_state_dict': completer.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}
torch.save(state, os.path.join(MyLogger.checkpoints_dir,
"model_epoch_%d.pth" % trained_epoch))
MyLogger.logger.info('Model saved at %s/model_epoch_%d.pth\n'
% (MyLogger.checkpoints_dir, trained_epoch))
MyLogger.logger.info('Training Finished, Total Time: ' +
str(datetime.timedelta(seconds=time.time() - train_start)))
def main(args):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
# seed_torch(args.seed)
''' === Set up Loggers and Load Data === '''
MyLogger = TrainLogger(args,
name=args.model.upper(),
subfold='cls',
filename=args.mode + '_log')
writer = SummaryWriter(os.path.join(MyLogger.experiment_dir, 'runs'))
MyLogger.logger.info('Load dataset %s' % args.dataset)
NUM_CLASSES, TRAIN_FILES, TEST_FILES = Dataset_Loc(dataset=args.dataset,
fname=args.fname,
partial=args.partial,
bn=args.bn)
TRAIN_DATASET = General_CLSDataLoader_HDF5(file_list=TRAIN_FILES,
num_point=1024)
TEST_DATASET = General_CLSDataLoader_HDF5(file_list=TEST_FILES,
num_point=1024)
trainDataLoader = DataLoader(TRAIN_DATASET,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
drop_last=True)
testDataLoader = DataLoader(TEST_DATASET,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
''' === Load Model and Backup Scripts === '''
MODEL = importlib.import_module(args.model)
shutil.copy(os.path.abspath(__file__), MyLogger.log_dir)
shutil.copy('./models/%s.py' % args.model, MyLogger.log_dir)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
classifier = MODEL.get_model(args=args,
num_channel=3,
num_class=NUM_CLASSES).to(device)
criterion = MODEL.get_loss().to(device)
classifier = torch.nn.DataParallel(classifier)
# nn.DataParallel has its own issues (slow, memory expensive),
# here are some advanced solutions: https://zhuanlan.zhihu.com/p/145427849
print('=' * 27)
print('Using %d GPU,' % torch.cuda.device_count(),
'Indices: %s' % args.gpu)
print('=' * 27)
''' === Restore Model from Pre-Trained Checkpoints: OcCo/Jigsaw etc === '''
if args.restore:
checkpoint = torch.load(args.restore_path)
classifier = copy_parameters(classifier, checkpoint, verbose=True)
MyLogger.logger.info('Use pre-trained weights from %s' %
args.restore_path)
else:
MyLogger.logger.info(
'No pre-trained weights, start training from scratch...')
if not args.use_sgd:
optimizer = torch.optim.Adam(classifier.parameters(),
lr=args.lr,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=1e-4)
else:
optimizer = torch.optim.SGD(classifier.parameters(),
lr=args.lr * 100,
momentum=args.momentum,
weight_decay=1e-4)
if args.scheduler == 'cos':
scheduler = CosineAnnealingLR(optimizer,
T_max=args.epoch,
eta_min=1e-3)
else:
scheduler = StepLR(optimizer,
step_size=args.step_size,
gamma=args.lr_decay)
LEARNING_RATE_CLIP = 0.01 * args.lr
if args.mode == 'test':
with torch.no_grad():
classifier.eval()
MyLogger.epoch_init(training=False)
for points, target in tqdm(testDataLoader,
total=len(testDataLoader),
smoothing=0.9):
points, target = points.float().transpose(
2, 1).cuda(), target.long().cuda()
if args.model == 'pointnet_cls':
pred, trans_feat = classifier(points)
loss = criterion(pred, target, trans_feat)
else:
pred = classifier(points)
loss = criterion(pred, target)
MyLogger.step_update(
pred.data.max(1)[1].cpu().numpy(),
target.long().cpu().numpy(),
loss.cpu().detach().numpy())
MyLogger.epoch_summary(writer=writer, training=False)
sys.exit("Test Finished")
for epoch in range(MyLogger.epoch, args.epoch + 1):
''' === Training === '''
MyLogger.epoch_init()
for points, target in tqdm(trainDataLoader,
total=len(trainDataLoader),
smoothing=0.9):
writer.add_scalar('Learning Rate',
scheduler.get_lr()[-1], MyLogger.step)
# Augmentation, might bring performance gains
if args.data_aug:
points = random_point_dropout(points.data.numpy())
points[:, :, :3] = random_scale_point_cloud(points[:, :, :3])
points[:, :, :3] = random_shift_point_cloud(points[:, :, :3])
points = torch.Tensor(points)
points, target = points.transpose(
2, 1).float().cuda(), target.long().cuda()
# FP and BP
classifier.train()
optimizer.zero_grad()
if args.model == 'pointnet_cls':
pred, trans_feat = classifier(points)
loss = criterion(pred, target, trans_feat)
else:
pred = classifier(points)
loss = criterion(pred, target)
loss.backward()
optimizer.step()
MyLogger.step_update(
pred.data.max(1)[1].cpu().numpy(),
target.long().cpu().numpy(),
loss.cpu().detach().numpy())
MyLogger.epoch_summary(writer=writer, training=True)
''' === Validating === '''
with torch.no_grad():
classifier.eval()
MyLogger.epoch_init(training=False)
for points, target in tqdm(testDataLoader,
total=len(testDataLoader),
smoothing=0.9):
points, target = points.float().transpose(
2, 1).cuda(), target.long().cuda()
if args.model == 'pointnet_cls':
pred, trans_feat = classifier(points)
loss = criterion(pred, target, trans_feat)
else:
pred = classifier(points)
loss = criterion(pred, target)
MyLogger.step_update(
pred.data.max(1)[1].cpu().numpy(),
target.long().cpu().numpy(),
loss.cpu().detach().numpy())
MyLogger.epoch_summary(writer=writer, training=False)
if MyLogger.save_model:
state = {
'step': MyLogger.step,
'epoch': MyLogger.best_instance_epoch,
'instance_acc': MyLogger.best_instance_acc,
'best_class_acc': MyLogger.best_class_acc,
'best_class_epoch': MyLogger.best_class_epoch,
'model_state_dict': classifier.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}
torch.save(state, MyLogger.savepath)
scheduler.step()
if args.scheduler == 'step':
for param_group in optimizer.param_groups:
if optimizer.param_groups[0]['lr'] < LEARNING_RATE_CLIP:
param_group['lr'] = LEARNING_RATE_CLIP
MyLogger.train_summary()
def main(args):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
root = 'data/indoor3d_sem_seg_hdf5_data'
NUM_CLASSES = len(seg_label_to_cat)
TRAIN_DATASET = S3DISDataset_HDF5(root=root, split='train', test_area=args.test_area)
TEST_DATASET = S3DISDataset_HDF5(root=root, split='test', test_area=args.test_area)
trainDataLoader = DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, num_workers=4)
testDataLoader = DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=4)
MyLogger = TrainLogger(args, name=args.model.upper(), subfold='semseg',
cls2name=class2label, filename=args.mode + '_log')
MyLogger.logger.info("The number of training data is: %d" % len(TRAIN_DATASET))
MyLogger.logger.info("The number of testing data is: %d" % len(TEST_DATASET))
''' === Model Loading === '''
MODEL = importlib.import_module(args.model)
shutil.copy(os.path.abspath(__file__), MyLogger.log_dir)
shutil.copy('./models/%s.py' % args.model, MyLogger.log_dir)
writer = SummaryWriter(os.path.join(MyLogger.experiment_dir, 'runs'))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
classifier = MODEL.get_model(num_class=NUM_CLASSES, num_channel=9, args=args).to(device)
criterion = MODEL.get_loss().to(device)
classifier = torch.nn.DataParallel(classifier)
print('=' * 27)
print('Using %d GPU,' % torch.cuda.device_count(), 'Indices: %s' % args.gpu)
print('=' * 27)
if args.restore:
checkpoint = torch.load(args.restore_path)
classifier = copy_parameters(classifier, checkpoint, verbose=True)
MyLogger.logger.info('Use pre-trained weights from %s' % args.restore_path)
else:
MyLogger.logger.info('No pre-trained weights, start training from scratch...')
if args.xavier_init:
classifier = classifier.apply(weights_init)
MyLogger.logger.info("Using Xavier weight initialisation")
if args.optimizer == 'adam':
optimizer = torch.optim.Adam(
classifier.parameters(),
lr=args.lr,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=1e-4)
MyLogger.logger.info("Using Adam optimiser")
else:
optimizer = torch.optim.SGD(
classifier.parameters(),
lr=args.lr * 100,
momentum=args.momentum)
MyLogger.logger.info("Using SGD optimiser")
# using the similar lr decay setting from
# https://github.com/charlesq34/pointnet/blob/master/sem_seg/train.py
# scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=40, gamma=0.5)
if args.scheduler == 'cos':
scheduler = CosineAnnealingLR(optimizer, T_max=args.epoch, eta_min=1e-3)
else:
scheduler = StepLR(optimizer, step_size=args.step_size, gamma=args.lr_decay)
LEARNING_RATE_CLIP = 1e-5
MOMENTUM_ORIGINAL = 0.1
MOMENTUM_DECAY = 0.5
MOMENTUM_DECAY_STEP = args.step_size
''' === Testing then Exit === '''
if args.mode == 'test':
with torch.no_grad():
classifier.eval()
MyLogger.epoch_init(training=False)
for points, target in tqdm(testDataLoader, total=len(testDataLoader), smoothing=0.9):
points, target = points.transpose(2, 1).float().cuda(), target.view(-1, 1)[:, 0].long().cuda()
if args.model == 'pointnet_semseg':
seg_pred, trans_feat = classifier(points)
seg_pred = seg_pred.contiguous().view(-1, NUM_CLASSES)
loss = criterion(seg_pred, target, trans_feat)
else:
seg_pred = classifier(points)
seg_pred = seg_pred.contiguous().view(-1, NUM_CLASSES)
loss = criterion(seg_pred, target)
MyLogger.step_update(seg_pred.data.max(1)[1].cpu().numpy(),
target.long().cpu().numpy(),
loss.cpu().detach().numpy())
MyLogger.epoch_summary(writer=writer, training=False, mode='semseg')
sys.exit("Test Finished")
for epoch in range(MyLogger.epoch, args.epoch + 1):
''' === Training === '''
# scheduler.step()
MyLogger.epoch_init()
for points, target in tqdm(trainDataLoader, total=len(trainDataLoader), smoothing=0.9):
writer.add_scalar('learning rate', scheduler.get_lr()[-1], MyLogger.step)
points, target = points.float().transpose(2, 1).cuda(), target.view(-1, 1)[:, 0].long().cuda()
classifier.train()
optimizer.zero_grad()
# pdb.set_trace()
if args.model == 'pointnet_semseg':
seg_pred, trans_feat = classifier(points)
seg_pred = seg_pred.contiguous().view(-1, NUM_CLASSES)
loss = criterion(seg_pred, target, trans_feat)
else:
seg_pred = classifier(points)
seg_pred = seg_pred.contiguous().view(-1, NUM_CLASSES)
loss = criterion(seg_pred, target)
loss.backward()
optimizer.step()
MyLogger.step_update(seg_pred.data.max(1)[1].cpu().numpy(),
target.long().cpu().numpy(),
loss.cpu().detach().numpy())
MyLogger.epoch_summary(writer=writer, training=True, mode='semseg')
'''=== Evaluating ==='''
with torch.no_grad():
classifier.eval()
MyLogger.epoch_init(training=False)
for points, target in tqdm(testDataLoader, total=len(testDataLoader), smoothing=0.9):
points, target = points.transpose(2, 1).float().cuda(), target.view(-1, 1)[:, 0].long().cuda()
if args.model == 'pointnet_semseg':
seg_pred, trans_feat = classifier(points)
seg_pred = seg_pred.contiguous().view(-1, NUM_CLASSES)
loss = criterion(seg_pred, target, trans_feat)
else:
seg_pred = classifier(points)
seg_pred = seg_pred.contiguous().view(-1, NUM_CLASSES)
loss = criterion(seg_pred, target)
MyLogger.step_update(seg_pred.data.max(1)[1].cpu().numpy(),
target.long().cpu().numpy(),
loss.cpu().detach().numpy())
MyLogger.epoch_summary(writer=writer, training=False, mode='semseg')
if MyLogger.save_model:
state = {
'step': MyLogger.step,
'miou': MyLogger.best_miou,
'epoch': MyLogger.best_miou_epoch,
'model_state_dict': classifier.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}
torch.save(state, MyLogger.savepath)
scheduler.step()
if args.scheduler == 'step':
for param_group in optimizer.param_groups:
if optimizer.param_groups[0]['lr'] < LEARNING_RATE_CLIP:
param_group['lr'] = LEARNING_RATE_CLIP
if args.bn_decay:
momentum = MOMENTUM_ORIGINAL * (MOMENTUM_DECAY ** (epoch // MOMENTUM_DECAY_STEP))
if momentum < 0.01:
momentum = 0.01
print('BN momentum updated to: %f' % momentum)
classifier = classifier.apply(lambda x: bn_momentum_adjust(x, momentum))
MyLogger.train_summary(mode='semseg')
def main(args):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
def to_categorical(y, num_class):
""" 1-hot encodes a tensor """
new_y = torch.eye(num_class)[y.cpu().data.numpy(), ]
if y.is_cuda:
return new_y.cuda()
return new_y
''' === Set up Loggers and Load Data === '''
MyLogger = TrainLogger(args, name=args.model.upper(), subfold='partseg',
filename=args.mode + '_log', cls2name=seg_label_to_cat)
writer = SummaryWriter(os.path.join(MyLogger.experiment_dir, 'runs'))
root = 'data/shapenetcore_partanno_segmentation_benchmark_v0_normal/'
TRAIN_DATASET = PartNormalDataset(root=root, num_point=args.num_point, split='trainval', use_normal=args.normal)
TEST_DATASET = PartNormalDataset(root=root, num_point=args.num_point, split='test', use_normal=args.normal)
trainDataLoader = DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, num_workers=4)
testDataLoader = DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=4)
num_classes, num_part = 16, 50
''' === Load Model and Backup Scripts === '''
channel_num = 6 if args.normal else 3
MODEL = importlib.import_module(args.model)
shutil.copy(os.path.abspath(__file__), MyLogger.log_dir)
shutil.copy('./models/%s.py' % args.model, MyLogger.log_dir)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
classifier = MODEL.get_model(part_num=num_part, num_channel=channel_num, args=args).cuda().to(device)
criterion = MODEL.get_loss().to(device)
classifier = torch.nn.DataParallel(classifier)
if args.restore:
checkpoint = torch.load(args.restore_path)
classifier = copy_parameters(classifier, checkpoint, verbose=True)
MyLogger.logger.info('Use pre-trained weights from %s' % args.restore_path)
else:
MyLogger.logger.info('No pre-trained weights, start training from scratch...')
if args.xavier_init:
classifier = classifier.apply(weights_init)
MyLogger.logger.info("Using Xavier weight initialisation")
if args.mode == 'test':
MyLogger.logger.info('\n\n')
MyLogger.logger.info('=' * 33)
MyLogger.logger.info('load parrameters from %s' % args.restore_path)
with torch.no_grad():
test_metrics = {}
total_correct, total_seen = 0, 0
total_seen_class = [0 for _ in range(num_part)]
total_correct_class = [0 for _ in range(num_part)]
shape_ious = {cat: [] for cat in seg_classes.keys()} # {shape: []}
for points, label, target in tqdm(testDataLoader, total=len(testDataLoader), smoothing=0.9):
classifier.eval()
cur_batch_size, num_point, _ = points.size()
vote_pool = torch.zeros(cur_batch_size, num_point, num_part).cuda() # (batch, num point, num part)
points, label, target = points.transpose(2, 1).float().cuda(), label.long().cuda(), target.numpy()
''' === generate predictions from raw output (multiple via voting) === '''
for _ in range(args.num_votes):
if args.model == 'pointnet_partseg':
seg_pred, _ = classifier(points, to_categorical(label, num_classes))
else:
seg_pred = classifier(points, to_categorical(label, num_classes))
vote_pool += seg_pred # added on probability
seg_pred = vote_pool / args.num_votes
cur_pred_val_logits = seg_pred.cpu().data.numpy()
cur_pred_val = np.zeros((cur_batch_size, num_point)).astype(np.int32)
for i in range(cur_batch_size):
cat = seg_label_to_cat[target[i, 0]] # str, shape name
logits = cur_pred_val_logits[i, :, :] # array, (num point, num part)
cur_pred_val[i, :] = np.argmax(logits[:, seg_classes[cat]], 1) + seg_classes[cat][0]
# only consider parts from that shape
''' === calculate accuracy === '''
total_correct += np.sum(cur_pred_val == target)
total_seen += (cur_batch_size * num_point)
for l in range(num_part):
total_seen_class[l] += np.sum(target == l)
total_correct_class[l] += (np.sum((cur_pred_val == l) & (target == l)))
''' === calculate iou === '''
for i in range(cur_batch_size):
segl = target[i, :] # array, (num point, )
segp = cur_pred_val[i, :] # array, (num point, )
cat = seg_label_to_cat[segl[0]] # str, shape name
part_ious = [0. for _ in range(len(seg_classes[cat]))] # parts belong to that shape
for l in seg_classes[cat]:
if (np.sum(segl == l) == 0) and (np.sum(segp == l) == 0): # no prediction or gt
part_ious[l - seg_classes[cat][0]] = 1.0
else:
iou = np.sum((segl == l) & (segp == l)) / float(np.sum((segl == l) | (segp == l)))
part_ious[l - seg_classes[cat][0]] = iou
shape_ious[cat].append(np.mean(part_ious))
all_shape_ious = []
for cat in shape_ious.keys():
for iou in shape_ious[cat]:
all_shape_ious.append(iou)
shape_ious[cat] = np.mean(shape_ious[cat])
mean_shape_ious = np.mean(list(shape_ious.values()))
test_metrics['class_avg_iou'] = mean_shape_ious
test_metrics['instance_avg_iou'] = np.mean(all_shape_ious)
test_metrics['accuracy'] = total_correct / float(total_seen)
test_metrics['class_avg_accuracy'] = np.mean(
np.array(total_correct_class) / np.array(total_seen_class, dtype=np.float))
for cat in sorted(shape_ious.keys()):
MyLogger.logger.info('test mIoU of %-14s %f' % (cat, shape_ious[cat]))
MyLogger.logger.info('Accuracy is: %.5f' % test_metrics['accuracy'])
MyLogger.logger.info('Class avg accuracy is: %.5f' % test_metrics['class_avg_accuracy'])
MyLogger.logger.info('Class avg mIoU is: %.5f' % test_metrics['class_avg_iou'])
MyLogger.logger.info('Instance avg mIoU is: %.5f' % test_metrics['instance_avg_iou'])
sys.exit("Test Finished")
if not args.use_sgd:
optimizer = torch.optim.Adam(
classifier.parameters(),
lr=args.lr,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=1e-4)
else:
optimizer = torch.optim.SGD(classifier.parameters(),
lr=args.lr * 100,
momentum=args.momentum,
weight_decay=1e-4)
if args.scheduler is 'step':
scheduler = StepLR(optimizer, step_size=args.step_size, gamma=args.lr_decay)
else:
scheduler = CosineAnnealingLR(optimizer, T_max=args.epoch, eta_min=1e-3)
LEARNING_RATE_CLIP = 1e-5
MOMENTUM_ORIGINAL = 0.1
MOMENTUM_DECAY = 0.5
MOMENTUM_DECAY_STEP = args.step_size
for epoch in range(MyLogger.epoch, args.epoch + 1):
MyLogger.epoch_init()
for points, label, target in tqdm(trainDataLoader, total=len(trainDataLoader), smoothing=0.9):
if args.data_aug:
points = points.data.numpy()
points[:, :, :3] = random_scale_point_cloud(points[:, :, 0:3])
points[:, :, :3] = random_shift_point_cloud(points[:, :, 0:3])
points = torch.Tensor(points)
points, label, target = points.transpose(2, 1).float().cuda(), label.long().cuda(), \
target.view(-1, 1)[:, 0].long().cuda()
classifier.train()
optimizer.zero_grad()
if args.model == 'pointnet_partseg':
seg_pred, trans_feat = classifier(points, to_categorical(label, num_classes))
seg_pred = seg_pred.contiguous().view(-1, num_part)
loss = criterion(seg_pred, target, trans_feat)
else:
seg_pred = classifier(points, to_categorical(label, num_classes))
seg_pred = seg_pred.contiguous().view(-1, num_part)
loss = criterion(seg_pred, target)
loss.backward()
optimizer.step()
MyLogger.step_update(seg_pred.data.max(1)[1].cpu().numpy(),
target.long().cpu().numpy(),
loss.cpu().detach().numpy())
MyLogger.epoch_summary(writer=writer, training=True, mode='partseg')
'''=== Evaluating ==='''
with torch.no_grad():
classifier.eval()
MyLogger.epoch_init(training=False)
for points, label, target in tqdm(testDataLoader, total=len(testDataLoader), smoothing=0.9):
cur_batch_size, NUM_POINT, _ = points.size()
points, label, target = points.transpose(2, 1).float().cuda(), label.long().cuda(), \
target.view(-1, 1)[:, 0].long().cuda()
if args.model == 'pointnet_partseg':
seg_pred, trans_feat = classifier(points, to_categorical(label, num_classes))
seg_pred = seg_pred.contiguous().view(-1, num_part)
loss = criterion(seg_pred, target, trans_feat)
else:
seg_pred = classifier(points, to_categorical(label, num_classes))
seg_pred = seg_pred.contiguous().view(-1, num_part)
loss = criterion(seg_pred, target)
MyLogger.step_update(seg_pred.data.max(1)[1].cpu().numpy(),
target.long().cpu().numpy(),
loss.cpu().detach().numpy())
MyLogger.epoch_summary(writer=writer, training=False, mode='partseg')
if MyLogger.save_model:
state = {
'step': MyLogger.step,
'miou': MyLogger.best_miou,
'epoch': MyLogger.best_miou_epoch,
'model_state_dict': classifier.state_dict(),
'optimizer_state_dict': optimizer.state_dict()}
torch.save(state, MyLogger.savepath)
if epoch % 5 == 0:
state = {
'model_state_dict': classifier.state_dict(),
'optimizer_state_dict': optimizer.state_dict()}
torch.save(state, MyLogger.savepath.replace('best_model', 'model_ep%d' % epoch))
scheduler.step()
if args.scheduler == 'step':
for param_group in optimizer.param_groups:
if optimizer.param_groups[0]['lr'] < LEARNING_RATE_CLIP:
param_group['lr'] = LEARNING_RATE_CLIP
if args.bn_decay:
momentum = MOMENTUM_ORIGINAL * (MOMENTUM_DECAY ** (epoch // MOMENTUM_DECAY_STEP))
if momentum < 0.01:
momentum = 0.01
print('BN momentum updated to: %f' % momentum)
classifier = classifier.apply(lambda x: bn_momentum_adjust(x, momentum))
bn=args.bn)
TRAIN_DATASET = General_CLSDataLoader_HDF5(file_list=TRAIN_FILES)
# TEST_DATASET = General_CLSDataLoader_HDF5(file_list=TEST_FILES)
trainDataLoader = DataLoader(TRAIN_DATASET,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
# testDataLoader = DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=True, num_workers=4)
MODEL = importlib.import_module(args.model)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder = MODEL.encoder(args=args, num_channel=3).to(device)
encoder = torch.nn.DataParallel(encoder)
checkpoint = torch.load(args.restore_path)
encoder = copy_parameters(encoder, checkpoint, verbose=True)
X_train, y_train, X_test, y_test = [], [], [], []
with torch.no_grad():
encoder.eval()
for points, target in tqdm(trainDataLoader,
total=len(trainDataLoader),
smoothing=0.9):
points, target = points.float().transpose(
2, 1).cuda(), target.long().cuda()
feats = encoder(points)
X_train.append(feats.cpu().numpy())
y_train.append(target.cpu().numpy())
# for points, target in tqdm(testDataLoader, total=len(testDataLoader), smoothing=0.9):
