def eval_one_epoch(sess, ops, test_writer):
is_training = False
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
# current_data, current_label = data_utils.get_current_data(TEST_DATA, TEST_LABELS, sample_num)
# current_data, current_label = data_utils.get_current_data_h5(TEST_DATA, TEST_LABELS, sample_num)
if (".h5" in TEST_FILE):
current_data, current_label = data_utils.get_current_data_h5(TEST_DATA, TEST_LABELS, sample_num)
else:
current_data, current_label = data_utils.get_current_data(TEST_DATA, TEST_LABELS, sample_num)
current_label = np.squeeze(current_label)
num_batches = current_data.shape[0]//batch_size
for batch_idx in range(num_batches):
start_idx = batch_idx * batch_size
end_idx = (batch_idx+1) * batch_size
xforms_np, rotations_np = pf.get_xforms(batch_size,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
# Augment batched point clouds by rotation and jittering
feed_dict = {ops['pointclouds_pl']: current_data[start_idx:end_idx, :, :],
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training,
ops['xforms']: xforms_np,
ops['rotations']: rotations_np,
ops['jitter_range']: np.array([jitter_val])}
summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
ops['loss'], ops['pred']], feed_dict=feed_dict)
pred_val = np.sum(pred_val, axis=1)
pred_val = np.argmax(pred_val, 1)
correct = np.sum(pred_val == current_label[start_idx:end_idx])
total_correct += correct
total_seen += batch_size
loss_sum += (loss_val*batch_size)
for i in range(start_idx, end_idx):
l = current_label[i]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i-start_idx] == l)
log_string('eval mean loss: %f' % (loss_sum / float(total_seen)))
log_string('eval accuracy: %f'% (total_correct / float(total_seen)))
log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
def train_one_epoch(sess, ops, train_writer):
is_training = True
#get current data, shuffle and set to numpy array with desired num_points
# current_data, current_label = data_utils.get_current_data(TRAIN_DATA, TRAIN_LABELS, sample_num)
# current_data, current_label = data_utils.get_current_data_h5(TRAIN_DATA, TRAIN_LABELS, sample_num)
if (".h5" in TRAIN_FILE):
current_data, current_label = data_utils.get_current_data_h5(TRAIN_DATA, TRAIN_LABELS, sample_num)
else:
current_data, current_label = data_utils.get_current_data(TRAIN_DATA, TRAIN_LABELS, sample_num)
current_label = np.squeeze(current_label)
num_batches = current_data.shape[0]//batch_size
total_correct = 0
total_seen = 0
loss_sum = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * batch_size
end_idx = (batch_idx+1) * batch_size
xforms_np, rotations_np = pf.get_xforms(batch_size,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
# Augment batched point clouds by rotation and jittering
feed_dict = {ops['pointclouds_pl']: current_data[start_idx:end_idx, :, :],
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training,
ops['xforms']: xforms_np,
ops['rotations']: rotations_np,
ops['jitter_range']: np.array([jitter])}
summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
train_writer.add_summary(summary, step)
pred_val = np.sum(pred_val, axis=1)
pred_val = np.argmax(pred_val, 1)
# print(pred_val)
# print(current_label[start_idx:end_idx])
correct = np.sum(pred_val == current_label[start_idx:end_idx])
total_correct += correct
total_seen += batch_size
loss_sum += loss_val
log_string('mean loss: %f' % (loss_sum / float(num_batches)))
log_string('accuracy: %f' % (total_correct / float(total_seen)))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--category',
'-c',
help='category name',
required=True)
parser.add_argument('--level',
'-l',
type=int,
help='level id',
required=True)
parser.add_argument('--load_ckpt',
'-k',
help='Path to a check point file for load')
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting',
'-x',
help='Setting to use',
required=True)
args = parser.parse_args()
args.save_folder = 'exps_results/'
args.data_folder = '../../data/sem_seg_h5/'
root_folder = os.path.join(
args.save_folder,
'%s_%s_%s_%d' % (args.model, args.setting, args.category, args.level))
if os.path.exists(root_folder):
print('ERROR: folder %s exist! Please check and delete!' % root_folder)
exit(1)
os.makedirs(root_folder)
flog = open(os.path.join(root_folder, 'log.txt'), 'w')
def printout(d):
flog.write(str(d) + '\n')
print(d)
printout('PID: %s' % os.getpid())
printout(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
step_val = setting.step_val
rotation_range = setting.rotation_range
rotation_range_val = setting.rotation_range_val
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
args.filelist = os.path.join(args.data_folder,
'%s-%d' % (args.category, args.level),
'train_files.txt')
args.filelist_val = os.path.join(args.data_folder,
'%s-%d' % (args.category, args.level),
'val_files.txt')
# Load current category + level statistics
with open(
'../../stats/after_merging_label_ids/%s-level-%d.txt' %
(args.category, args.level), 'r') as fin:
setting.num_class = len(fin.readlines()) + 1 # with "other"
printout('NUM CLASS: %d' % setting.num_class)
label_weights_list = [1.0] * setting.num_class
# Prepare inputs
printout('{}-Preparing datasets...'.format(datetime.now()))
data_train, data_num_train, label_train = data_utils.load_seg(
args.filelist)
data_val, data_num_val, label_val = data_utils.load_seg(args.filelist_val)
# shuffle
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
num_train = data_train.shape[0]
point_num = data_train.shape[1]
num_val = data_val.shape[0]
printout('{}-{:d}/{:d} training/validation samples.'.format(
datetime.now(), num_train, num_val))
batch_num = (num_train * num_epochs + batch_size - 1) // batch_size
train_batch = num_train // batch_size
printout('{}-{:d} training batches.'.format(datetime.now(), batch_num))
batch_num_val = math.ceil(num_val / batch_size)
printout('{}-{:d} testing batches per test.'.format(
datetime.now(), batch_num_val))
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32,
shape=(None, 3, 3),
name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
pts_fts = tf.placeholder(tf.float32,
shape=(None, point_num, setting.data_dim),
name='pts_fts')
labels_seg = tf.placeholder(tf.int64,
shape=(None, point_num),
name='labels_seg')
labels_weights = tf.placeholder(tf.float32,
shape=(None, point_num),
name='labels_weights')
######################################################################
pts_fts_sampled = tf.gather_nd(pts_fts,
indices=indices,
name='pts_fts_sampled')
features_augmented = None
if setting.data_dim > 3:
points_sampled, features_sampled = tf.split(
pts_fts_sampled, [3, setting.data_dim - 3],
axis=-1,
name='split_points_features')
if setting.use_extra_features:
if setting.with_normal_feature:
if setting.data_dim < 6:
printout('Only 3D normals are supported!')
exit()
elif setting.data_dim == 6:
features_augmented = pf.augment(features_sampled,
rotations)
else:
normals, rest = tf.split(features_sampled,
[3, setting.data_dim - 6])
normals_augmented = pf.augment(normals, rotations)
features_augmented = tf.concat([normals_augmented, rest],
axis=-1)
else:
features_augmented = features_sampled
else:
points_sampled = pts_fts_sampled
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
labels_sampled = tf.gather_nd(labels_seg,
indices=indices,
name='labels_sampled')
labels_weights_sampled = tf.gather_nd(labels_weights,
indices=indices,
name='labels_weight_sampled')
net = model.Net(points_augmented, features_augmented, is_training, setting)
logits = net.logits
probs = tf.nn.softmax(logits, name='probs')
predictions = tf.argmax(probs, axis=-1, name='predictions')
loss_op = tf.losses.sparse_softmax_cross_entropy(
labels=labels_sampled, logits=logits, weights=labels_weights_sampled)
with tf.name_scope('metrics'):
loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op)
t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy(
labels_sampled, predictions, weights=labels_weights_sampled)
t_1_per_class_acc_op, t_1_per_class_acc_update_op = \
tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class,
weights=labels_weights_sampled)
reset_metrics_op = tf.variables_initializer([
var for var in tf.local_variables()
if var.name.split('/')[0] == 'metrics'
])
_ = tf.summary.scalar('loss/train',
tensor=loss_mean_op,
collections=['train'])
_ = tf.summary.scalar('t_1_acc/train',
tensor=t_1_acc_op,
collections=['train'])
_ = tf.summary.scalar('t_1_per_class_acc/train',
tensor=t_1_per_class_acc_op,
collections=['train'])
_ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val',
tensor=t_1_acc_op,
collections=['val'])
_ = tf.summary.scalar('t_1_per_class_acc/val',
tensor=t_1_per_class_acc_op,
collections=['val'])
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base,
global_step,
setting.decay_steps,
setting.decay_rate,
staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate',
tensor=lr_clip_op,
collections=['train'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op,
epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op,
momentum=setting.momentum,
use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss_op + reg_loss,
global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum(
[np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
printout('{}-Parameter number: {:d}.'.format(datetime.now(),
parameter_num))
# Create a session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.Session(config=config)
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
printout('{}-Checkpoint loaded from {}!'.format(
datetime.now(), args.load_ckpt))
for batch_idx_train in range(batch_num):
if (batch_idx_train % (10 * train_batch) == 0 and (batch_idx_train != 0 or args.load_ckpt is not None)) \
or batch_idx_train == batch_num - 1:
######################################################################
# Validation
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
printout('{}-Checkpoint saved to {}!'.format(
datetime.now(), filename_ckpt))
sess.run(reset_metrics_op)
for batch_val_idx in range(batch_num_val):
start_idx = batch_size * batch_val_idx
end_idx = min(start_idx + batch_size, num_val)
batch_size_val = end_idx - start_idx
points_batch = data_val[start_idx:end_idx, ...]
points_num_batch = data_num_val[start_idx:end_idx, ...]
labels_batch = label_val[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[labels_batch]
xforms_np, rotations_np = pf.get_xforms(
batch_size_val,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
sess.run(
[
loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op
],
feed_dict={
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_val, sample_num,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter_val]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
False,
})
loss_val, t_1_acc_val, t_1_per_class_acc_val, summaries_val = sess.run(
[
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
summaries_val_op
])
summary_writer.add_summary(summaries_val, batch_idx_train)
printout(
'{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}'
.format(datetime.now(), loss_val, t_1_acc_val,
t_1_per_class_acc_val))
flog.flush()
######################################################################
######################################################################
# Training
start_idx = (batch_size * batch_idx_train) % num_train
end_idx = min(start_idx + batch_size, num_train)
batch_size_train = end_idx - start_idx
points_batch = data_train[start_idx:end_idx, ...]
points_num_batch = data_num_train[start_idx:end_idx, ...]
labels_batch = label_train[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[labels_batch]
if start_idx + batch_size_train == num_train:
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
offset = int(random.gauss(0, sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(batch_size_train,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
sess.run(reset_metrics_op)
sess.run(
[
train_op, loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op
],
feed_dict={
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_train, sample_num_train,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
True,
})
if batch_idx_train % 10 == 0:
loss, t_1_acc, t_1_per_class_acc, summaries = sess.run(
[loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, summaries_op])
summary_writer.add_summary(summaries, batch_idx_train)
printout(
'{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}'
.format(datetime.now(), batch_idx_train, loss, t_1_acc,
t_1_per_class_acc))
flog.flush()
######################################################################
printout('{}-Done!'.format(datetime.now()))
flog.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--filelist', '-t', help='Path to training set ground truth (.txt)', required=True)
parser.add_argument('--filelist_val', '-v', help='Path to validation set ground truth (.txt)', required=True)
parser.add_argument('--load_ckpt', '-l', help='Path to a check point file for load')
parser.add_argument('--save_folder', '-s', help='Path to folder for saving check points and summary', required=True)
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting', '-x', help='Setting to use', required=True)
args = parser.parse_args()
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(args.save_folder, '%s_%s_%d_%s' % (args.model, args.setting, os.getpid(), time_string))
if not os.path.exists(root_folder):
os.makedirs(root_folder)
sys.stdout = open(os.path.join(root_folder, 'log.txt'), 'w')
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
print(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
step_val = 1000
num_parts = setting.num_parts
label_weights_list = setting.label_weights
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
#data_train, data_num_train, label_train = data_utils.load_seg(args.filelist)
#data_val, data_num_val, label_val = data_utils.load_seg(args.filelist_val)
data_train, img_train, data_num_train, label_train, label_hwl_train, label_xyz_train,label_ry_train, label_ry_reg_train = data_utils.load_seg_kitti(args.filelist)
data_val, img_val, data_num_val, label_val, label_hwl_val, label_xyz_val,label_ry_val, label_ry_reg_val = data_utils.load_seg_kitti(args.filelist_val)
# shuffle
#data_train, data_num_train, label_train = \
# data_utils.grouped_shuffle([data_train, data_num_train, label_train])
data_train, img_train, data_num_train, label_train, label_hwl_train, label_xyz_train,\
label_ry_train, label_ry_reg_train = data_utils.grouped_shuffle([data_train, img_train,\
data_num_train, label_train, label_hwl_train,label_xyz_train, label_ry_train,label_ry_reg_train])
num_train = data_train.shape[0]
point_num = data_train.shape[1]
num_val = data_val.shape[0]
print('{}-{:d}/{:d} training/validation samples.'.format(datetime.now(), num_train, num_val))
batch_num = (num_train * num_epochs + batch_size - 1) // batch_size
print('{}-{:d} training batches.'.format(datetime.now(), batch_num))
batch_num_val = math.ceil(num_val / batch_size)
print('{}-{:d} testing batches per test.'.format(datetime.now(), batch_num_val))
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
pts_fts = tf.placeholder(tf.float32, shape=(None, point_num, setting.data_dim), name='pts_fts')
labels_seg = tf.placeholder(tf.int32, shape=(None, point_num), name='labels_seg')
labels_weights = tf.placeholder(tf.float32, shape=(None, point_num), name='labels_weights')
labels_hwl = tf.placeholder(tf.float32, shape=(None, 3), name='labels_hwl')
labels_xyz = tf.placeholder(tf.float32, shape=(None, 3), name='labels_xyz')
labels_ry = tf.placeholder(tf.int32, shape=(None, 1), name='labels_ry')
labels_ry_reg = tf.placeholder(tf.float32, shape=(None, 1), name='labels_ry_reg')
######################################################################
# Set Inputs(points,features_sampled)
features_sampled = None
if setting.data_dim > 3:
points, _, features = tf.split(pts_fts, [setting.data_format["pts_xyz"], setting.data_format["img_xy"], setting.data_format["extra_features"]], axis=-1, name='split_points_xy_features')
if setting.use_extra_features:
features_sampled = tf.gather_nd(features, indices=indices, name='features_sampled')
else:
points = pts_fts
points_sampled = tf.gather_nd(points, indices=indices, name='points_sampled')
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
labels_sampled = tf.gather_nd(labels_seg, indices=indices, name='labels_sampled')
labels_weights_sampled = tf.gather_nd(labels_weights, indices=indices, name='labels_weight_sampled')
# Build net
#net = model.Net(points_augmented, features_sampled, None, None, num_parts, is_training, setting)
net = model.Net(points_augmented, features_sampled, None, None, num_parts, is_training, setting)
#logits, probs= net.logits, net.probs
logits, probs, logits_hwl, logits_xyz, logits_ry, probs_ry=net.logits, net.probs, net.logits_hwl, net.logits_xyz, net.logits_ry, net.probs_ry
# Define Loss Func
loss_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_sampled, logits=logits, weights=labels_weights_sampled)
_ = tf.summary.scalar('loss/train_seg', tensor=loss_op, collections=['train'])
# HWL loss
# 这里以MSE loss 为例
loss_hwl_op = tf.losses.mean_squared_error(labels_hwl, logits_hwl) # API提供的MSE loss
# 计算RMSE来作为一种评估指标
rmse_hwl_op = tf.sqrt(tf.reduce_mean(tf.reduce_sum(tf.square(labels_hwl - logits_hwl), -1)))
# XYZ loss
# 这里以MSE loss 为例
loss_xyz_op = tf.losses.mean_squared_error(labels_xyz, logits_xyz)
# 计算RMSE来作为一种评估指标
rmse_xyz_op = tf.sqrt(tf.reduce_mean(tf.reduce_sum(tf.square(labels_xyz - logits_xyz), -1)))
# RY loss
# 分类以softmax_cross_entropy为例
loss_ry_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_ry, logits=logits_ry)
# 计算分类准确率作为评估指标
t_1_acc_ry_op = pf.top_1_accuracy(probs_ry, labels_ry)
# 计算平均角度偏差作为评估指标
mdis_ry_angle_op = tf.reduce_mean(tf.sqrt(tf.square(pf.ry_getangle(labels_ry,labels_ry_reg)-
pf.ry_getangle(tf.nn.top_k(probs_ry, 1)[1], tf.zeros_like(labels_ry_reg)))))
# 将结果可视化在Tensorboard上
_ = tf.summary.scalar('loss/train_hwl', tensor=loss_hwl_op, collections=['train'])
_ = tf.summary.scalar('loss/train_xyz', tensor=loss_xyz_op, collections=['train'])
_ = tf.summary.scalar('loss/train_ry', tensor=loss_ry_op, collections=['train'])
_ = tf.summary.scalar('t_1_acc/train_rmse_hwl', tensor=rmse_hwl_op, collections=['train'])
_ = tf.summary.scalar('t_1_acc/train_rmse_xyz', tensor=rmse_xyz_op, collections=['train'])
_ = tf.summary.scalar('t_1_acc/train_ry', tensor=t_1_acc_ry_op, collections=['train'])
_ = tf.summary.scalar('t_1_acc/mdis_ry_angle', tensor=mdis_ry_angle_op, collections=['train'])
#for vis t1 acc
t_1_acc_op = pf.top_1_accuracy(probs, labels_sampled)
_ = tf.summary.scalar('t_1_acc/train_seg', tensor=t_1_acc_op, collections=['train'])
#for vis instance acc
t_1_acc_instance_op = pf.top_1_accuracy(probs, labels_sampled, labels_weights_sampled, 0.6)
_ = tf.summary.scalar('t_1_acc/train_seg_instance', tensor=t_1_acc_instance_op, collections=['train'])
#for vis other acc
t_1_acc_others_op = pf.top_1_accuracy(probs, labels_sampled, labels_weights_sampled, 0.6,"less")
_ = tf.summary.scalar('t_1_acc/train_seg_others', tensor=t_1_acc_others_op, collections=['train'])
loss_val_avg = tf.placeholder(tf.float32)
_ = tf.summary.scalar('loss/val_seg', tensor=loss_val_avg, collections=['val'])
t_1_acc_val_avg = tf.placeholder(tf.float32)
_ = tf.summary.scalar('t_1_acc/val_seg', tensor=t_1_acc_val_avg, collections=['val'])
t_1_acc_val_instance_avg = tf.placeholder(tf.float32)
_ = tf.summary.scalar('t_1_acc/val_seg_instance', tensor=t_1_acc_val_instance_avg, collections=['val'])
t_1_acc_val_others_avg = tf.placeholder(tf.float32)
_ = tf.summary.scalar('t_1_acc/val_seg_others', tensor=t_1_acc_val_others_avg, collections=['val'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
# 对于val，需要计算整个Val过程误差的平均值作为Val的评估指标
loss_val_avg_hwl = tf.placeholder(tf.float32)
loss_val_avg_xyz = tf.placeholder(tf.float32)
loss_val_avg_ry = tf.placeholder(tf.float32)
_ = tf.summary.scalar('loss/val_hwl', tensor=loss_val_avg_hwl, collections=['val'])
_ = tf.summary.scalar('loss/val_xyz', tensor=loss_val_avg_xyz, collections=['val'])
_ = tf.summary.scalar('loss/val_ry', tensor=loss_val_avg_ry, collections=['val'])
rmse_val_hwl_avg = tf.placeholder(tf.float32)
rmse_val_xyz_avg = tf.placeholder(tf.float32)
t_1_acc_val_ry_avg = tf.placeholder(tf.float32)
mdis_val_ry_angle_avg = tf.placeholder(tf.float32)
_ = tf.summary.scalar('t_1_acc/val_rmse_hwl', tensor=rmse_val_hwl_avg, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val_rmse_xyz', tensor=rmse_val_xyz_avg, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val_ry', tensor=t_1_acc_val_ry_avg, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val_mdis_ry_angle', tensor=mdis_val_ry_angle_avg, collections=['val'])
# lr decay
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base, global_step, setting.decay_steps,setting.decay_rate, staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate', tensor=lr_clip_op, collections=['train'])
# Optimizer
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op, epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op, momentum=0.9, use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
#train_op = optimizer.minimize(loss_op + reg_loss, global_step=global_step)
train_op = optimizer.minimize(loss_op + reg_loss + loss_hwl_op + loss_xyz_op + loss_ry_op, global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
# backup this file, model and setting
if not os.path.exists(os.path.join(root_folder, args.model)):
os.makedirs(os.path.join(root_folder, args.model))
shutil.copy(__file__, os.path.join(root_folder, os.path.basename(__file__)))
shutil.copy(os.path.join(os.path.dirname(__file__), args.model + '.py'),
os.path.join(root_folder, args.model + '.py'))
shutil.copy(os.path.join(os.path.dirname(__file__), args.model.split("_")[0] + "_kitti" + '.py'),
os.path.join(root_folder, args.model.split("_")[0] + "_kitti" + '.py'))
shutil.copy(os.path.join(setting_path, args.setting + '.py'),
os.path.join(root_folder, args.model, args.setting + '.py'))
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum([np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
# Session Run
with tf.Session() as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(datetime.now(), args.load_ckpt))
for batch_idx in range(batch_num):
if (batch_idx != 0 and batch_idx % step_val == 0) or batch_idx == batch_num - 1:
######################################################################
# Validation
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
print('{}-Checkpoint saved to {}!'.format(datetime.now(), filename_ckpt))
losses_val = []
t_1_accs = []
t_1_accs_instance = []
t_1_accs_others = []
# 声明用于计算平均值的list
losses_val_hwl = []
losses_val_xyz = []
losses_val_ry = []
rmses_hwl = []
rmses_xyz = []
t_1_accs_ry = []
mdises_ry_angle = []
for batch_val_idx in range(math.ceil(num_val / batch_size)):
start_idx = batch_size * batch_val_idx
end_idx = min(start_idx + batch_size, num_val)
batch_size_val = end_idx - start_idx
points_batch = data_val[start_idx:end_idx, ...]
points_num_batch = data_num_val[start_idx:end_idx, ...]
labels_batch = label_val[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[label_val[start_idx:end_idx, ...]]
# 取出每个batch的label
labels_hwl_batch = label_hwl_val[start_idx:end_idx, ...]
labels_xyz_batch = label_xyz_val[start_idx:end_idx, ...]
labels_ry_batch = label_ry_val[start_idx:end_idx, ...]
labels_ry_reg_batch = label_ry_reg_val[start_idx:end_idx, ...]
xforms_np, rotations_np = pf.get_xforms(batch_size_val, scaling_range=scaling_range_val)
sess_op_list = [loss_op, t_1_acc_op, t_1_acc_instance_op, t_1_acc_others_op]
sess_op_list = sess_op_list + [loss_hwl_op, loss_xyz_op, loss_ry_op, rmse_hwl_op, rmse_xyz_op,t_1_acc_ry_op, mdis_ry_angle_op]
sess_feed_dict = {pts_fts: points_batch,
indices: pf.get_indices(batch_size_val, sample_num, points_num_batch),
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter_val]),
labels_seg: labels_batch,
labels_weights: weights_batch,
is_training: False}
sess_feed_dict[labels_hwl] = labels_hwl_batch
sess_feed_dict[labels_xyz] = labels_xyz_batch
sess_feed_dict[labels_ry] = labels_ry_batch
sess_feed_dict[labels_ry_reg] = labels_ry_reg_batch
#loss_val, t_1_acc_val, t_1_acc_val_instance, t_1_acc_val_others = sess.run(sess_op_list,feed_dict=sess_feed_dict)
loss_val, t_1_acc_val, t_1_acc_val_instance, t_1_acc_val_others, loss_val_hwl, loss_val_xyz,loss_val_ry, rmse_hwl_val, rmse_xyz_val, t_1_acc_ry_val, mdis_ry_angle_val =sess.run(sess_op_list, feed_dict=sess_feed_dict)
#print('{}-[Val ]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f}'.format(datetime.now(), batch_val_idx, loss_val, t_1_acc_val))
print('{}-[Val ]-Iter: {:06d} Loss: {:.4f} Loss_hwl: {:.4f} Loss_xyz: {:.4f} Loss_ry: {:.4f}Rmse_hwl: {:.4f} Rmse_xyz: {:.4f} T - 1Ry_Acc: {:.4f} Mdis_ry_angle: {:.4f} T - 1Acc: {:.4f}'.format(datetime.now(), batch_val_idx, loss_val, loss_val_hwl, loss_val_xyz, loss_val_ry,rmse_hwl_val, rmse_xyz_val, t_1_acc_ry_val, mdis_ry_angle_val, t_1_acc_val))
sys.stdout.flush()
losses_val.append(loss_val * batch_size_val)
t_1_accs.append(t_1_acc_val * batch_size_val)
t_1_accs_instance.append(t_1_acc_val_instance * batch_size_val)
t_1_accs_others.append(t_1_acc_val_others * batch_size_val)
# 记录每个iter的评估指标
losses_val_hwl.append(loss_val_hwl * batch_size_val)
losses_val_xyz.append(loss_val_xyz * batch_size_val)
losses_val_ry.append(loss_val_ry * batch_size_val)
rmses_hwl.append(rmse_hwl_val * batch_size_val)
rmses_xyz.append(rmse_xyz_val * batch_size_val)
t_1_accs_ry.append(t_1_acc_ry_val * batch_size_val)
mdises_ry_angle.append(mdis_ry_angle_val * batch_size_val)
loss_avg = sum(losses_val)/num_val
t_1_acc_avg = sum(t_1_accs) / num_val
t_1_acc_instance_avg = sum(t_1_accs_instance) / num_val
t_1_acc_others_avg = sum(t_1_accs_others) / num_val
loss_avg_hwl = sum(losses_val_hwl) / num_val
loss_avg_xyz = sum(losses_val_xyz) / num_val
loss_avg_ry = sum(losses_val_ry) / num_val
rmse_hwl_avg = sum(rmses_hwl) / num_val
rmse_xyz_avg = sum(rmses_xyz) / num_val
t_1_acc_ry_avg = sum(t_1_accs_ry) / num_val
mdis_ry_angle_avg = sum(mdises_ry_angle) / num_val
summaries_feed_dict = { loss_val_avg: loss_avg,
t_1_acc_val_avg: t_1_acc_avg,
t_1_acc_val_instance_avg: t_1_acc_instance_avg,
t_1_acc_val_others_avg: t_1_acc_others_avg}
summaries_feed_dict[loss_val_avg_hwl] = loss_avg_hwl
summaries_feed_dict[loss_val_avg_xyz] = loss_avg_xyz
summaries_feed_dict[loss_val_avg_ry] = loss_avg_ry
summaries_feed_dict[rmse_val_hwl_avg] = rmse_hwl_avg
summaries_feed_dict[rmse_val_xyz_avg] = rmse_xyz_avg
summaries_feed_dict[t_1_acc_val_ry_avg] = t_1_acc_ry_avg
summaries_feed_dict[mdis_val_ry_angle_avg] = mdis_ry_angle_avg
summaries_val = sess.run(summaries_val_op,feed_dict=summaries_feed_dict)
summary_writer.add_summary(summaries_val, batch_idx)
#print('{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f}'
# .format(datetime.now(), loss_avg, t_1_acc_avg))
print('{}-[Val ]-Average: Loss: {:.4f} Loss_hwl: {:.4f} Loss_xyz: {:.4f} Loss_ry: {:.4f}Rmse_hwl: {:.4f} Rmse_xyz: {:.4f} T - 1Ry_Acc: {:.4f} Mdis_ry_angle: {:.4f} T - 1Acc: {:.4f}'.format(datetime.now(), loss_avg, loss_avg_hwl, loss_avg_xyz, loss_avg_ry, rmse_hwl_avg,rmse_xyz_avg, t_1_acc_ry_avg, mdis_ry_angle_avg,t_1_acc_avg))
sys.stdout.flush()
######################################################################
######################################################################
# Training
start_idx = (batch_size * batch_idx) % num_train
end_idx = min(start_idx + batch_size, num_train)
batch_size_train = end_idx - start_idx
points_batch = data_train[start_idx:end_idx, ...]
points_num_batch = data_num_train[start_idx:end_idx, ...]
labels_batch = label_train[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[labels_batch]
labels_hwl_batch = label_hwl_train[start_idx:end_idx, ...]
labels_xyz_batch = label_xyz_train[start_idx:end_idx, ...]
labels_ry_batch = label_ry_train[start_idx:end_idx, ...]
labels_ry_reg_batch = label_ry_reg_train[start_idx:end_idx, ...]
if start_idx + batch_size_train == num_train:
#data_train, data_num_train, label_train = \
# data_utils.grouped_shuffle([data_train, data_num_train, label_train])
data_train, img_train, data_num_train, label_train, label_hwl_train, label_xyz_train, \
label_ry_train, label_ry_reg_train = data_utils.grouped_shuffle([data_train, img_train,data_num_train, label_train,label_hwl_train, label_xyz_train,label_ry_train, label_ry_reg_train])
offset = int(random.gauss(0, sample_num // 8))
offset = max(offset, -sample_num // 4)
offset = min(offset, sample_num // 4)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(batch_size_train, scaling_range=scaling_range)
sess_op_list = [train_op, loss_op, t_1_acc_op, t_1_acc_instance_op, t_1_acc_others_op, summaries_op]
sess_op_list = sess_op_list + [loss_hwl_op, loss_xyz_op, loss_ry_op, rmse_hwl_op, rmse_xyz_op,t_1_acc_ry_op, mdis_ry_angle_op]
sess_feed_dict = {pts_fts: points_batch,
indices: pf.get_indices(batch_size_train, sample_num_train, points_num_batch),
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter]),
labels_seg: labels_batch,
labels_weights: weights_batch,
is_training: True}
# 为placeholder feed label
sess_feed_dict[labels_hwl] = labels_hwl_batch
sess_feed_dict[labels_xyz] = labels_xyz_batch
sess_feed_dict[labels_ry] = labels_ry_batch
sess_feed_dict[labels_ry_reg] = labels_ry_reg_batch
#_, loss, t_1_acc, t_1_acc_instance, t_1_acc_others, summaries = sess.run(sess_op_list,feed_dict=sess_feed_dict)
_, loss, t_1_acc, t_1_acc_instance, t_1_acc_others, summaries, loss_hwl, loss_xyz, loss_ry,rmse_hwl, rmse_xyz, t_1_acc_ry, mdis_ry_angle=sess.run(sess_op_list, feed_dict=sess_feed_dict)
#print('{}-[Train]-Iter: {:06d} Loss_seg: {:.4f} T-1 Acc: {:.4f}'
# .format(datetime.now(), batch_idx, loss, t_1_acc))
print('{}-[Train]-Iter: {:06d} Loss_seg: {:.4f} Loss_hwl: {:.4f} Loss_xyz: {:.4f} Loss_ry:{:.4f} Rmse_hwl: {:.4f} Rmse_xyz: {:.4f} T - 1Ry_Acc: {:.4f} Mdis_ry_angle: {:.4f} T - 1Acc:{:.4f}'.format(datetime.now(), batch_idx, loss, loss_hwl, loss_xyz, loss_ry, rmse_hwl, rmse_xyz,t_1_acc_ry, mdis_ry_angle, t_1_acc))
summary_writer.add_summary(summaries, batch_idx)
sys.stdout.flush()
######################################################################
print('{}-Done!'.format(datetime.now()))
def eval_one_epoch(sess, ops, num_votes=1, topk=1):
error_cnt = 0
is_training = False
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
fout = open(os.path.join(DUMP_DIR, 'pred_label.txt'), 'w')
# data_utils.shuffle_points(TEST_DATA)
# current_data, current_label = data_utils.get_current_data(TEST_DATA, TEST_LABELS, NUM_POINT)
# current_data, current_label = data_utils.get_current_data_h5(TEST_DATA, TEST_LABELS, NUM_POINT)
if (".h5" in TEST_FILE):
current_data, current_label = data_utils.get_current_data_h5(
TEST_DATA, TEST_LABELS, NUM_POINT)
else:
current_data, current_label = data_utils.get_current_data(
TEST_DATA, TEST_LABELS, NUM_POINT)
current_label = np.squeeze(current_label)
num_batches = current_data.shape[0] // BATCH_SIZE
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
cur_batch_size = end_idx - start_idx
# Aggregating BEG
batch_loss_sum = 0 # sum of losses for the batch
batch_pred_sum = np.zeros(
(cur_batch_size, NUM_CLASSES)) # score for classes
batch_pred_classes = np.zeros(
(cur_batch_size, NUM_CLASSES)) # 0/1 for classes
for vote_idx in range(num_votes):
rotated_data = provider.rotate_point_cloud_by_angle(
current_data[start_idx:end_idx, :, :],
vote_idx / float(num_votes) * np.pi * 2)
xforms_np, rotations_np = pf.get_xforms(
BATCH_SIZE,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
# Augment batched point clouds by rotation and jittering
feed_dict = {
ops['pointclouds_pl']: rotated_data,
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training,
ops['xforms']: xforms_np,
ops['rotations']: rotations_np,
ops['jitter_range']: np.array([jitter_val])
}
loss_val, pred_val = sess.run([ops['loss'], ops['pred']],
feed_dict=feed_dict)
pred_val = np.sum(pred_val, axis=1)
# pred_val = np.argmax(pred_val, 1)
batch_pred_sum += pred_val
batch_pred_val = np.argmax(pred_val, 1)
for el_idx in range(cur_batch_size):
batch_pred_classes[el_idx, batch_pred_val[el_idx]] += 1
batch_loss_sum += (loss_val * cur_batch_size / float(num_votes))
# pred_val_topk = np.argsort(batch_pred_sum, axis=-1)[:,-1*np.array(range(topk))-1]
# pred_val = np.argmax(batch_pred_classes, 1)
pred_val = np.argmax(batch_pred_sum, 1)
# Aggregating END
correct = np.sum(pred_val == current_label[start_idx:end_idx])
# correct = np.sum(pred_val_topk[:,0:topk] == label_val)
total_correct += correct
total_seen += cur_batch_size
loss_sum += batch_loss_sum
for i in range(start_idx, end_idx):
l = current_label[i]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i - start_idx] == l)
fout.write('%s, %s\n' %
(SHAPE_NAMES[pred_val[i - start_idx]], SHAPE_NAMES[l]))
if pred_val[i -
start_idx] != l and FLAGS.visu: # ERROR CASE, DUMP!
img_filename = '%d_label_%s_pred_%s.jpg' % (
error_cnt, SHAPE_NAMES[l],
SHAPE_NAMES[pred_val[i - start_idx]])
img_filename = os.path.join(DUMP_DIR, img_filename)
output_img = pc_util.point_cloud_three_views(
np.squeeze(current_data[i, :, :]))
scipy.misc.imsave(img_filename, output_img)
#save ply
ply_filename = '%d_label_%s_pred_%s.ply' % (
error_cnt, SHAPE_NAMES[l],
SHAPE_NAMES[pred_val[i - start_idx]])
ply_filename = os.path.join(DUMP_DIR, ply_filename)
data_utils.save_ply(np.squeeze(current_data[i, :, :]),
ply_filename)
error_cnt += 1
log_string('total seen: %d' % (total_seen))
log_string('eval mean loss: %f' % (loss_sum / float(total_seen)))
log_string('eval accuracy: %f' % (total_correct / float(total_seen)))
log_string('eval avg class acc: %f' % (np.mean(
np.array(total_correct_class) /
np.array(total_seen_class, dtype=np.float))))
class_accuracies = np.array(total_correct_class) / np.array(
total_seen_class, dtype=np.float)
for i, name in enumerate(SHAPE_NAMES):
log_string('%10s:\t%0.3f' % (name, class_accuracies[i]))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--path', '-t', help='Path to data', required=True)
parser.add_argument('--path_val', '-v', help='Path to validation data')
parser.add_argument('--load_ckpt',
'-l',
help='Path to a check point file for load')
parser.add_argument(
'--save_folder',
'-s',
help='Path to folder for saving check points and summary',
required=True)
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting',
'-x',
help='Setting to use',
required=True)
parser.add_argument(
'--epochs',
help='Number of training epochs (default defined in setting)',
type=int)
parser.add_argument('--batch_size',
help='Batch size (default defined in setting)',
type=int)
parser.add_argument(
'--log',
help=
'Log to FILE in save folder; use - for stdout (default is log.txt)',
metavar='FILE',
default='log.txt')
parser.add_argument('--no_timestamp_folder',
help='Dont save to timestamp folder',
action='store_true')
parser.add_argument('--no_code_backup',
help='Dont backup code',
action='store_true')
args = parser.parse_args()
if not args.no_timestamp_folder:
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(
args.save_folder, '%s_%s_%s_%d' %
(args.model, args.setting, time_string, os.getpid()))
else:
root_folder = args.save_folder
if not os.path.exists(root_folder):
os.makedirs(root_folder)
if args.log != '-':
sys.stdout = open(os.path.join(root_folder, args.log), 'w')
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = args.epochs or setting.num_epochs
batch_size = args.batch_size or setting.batch_size
sample_num = setting.sample_num
step_val = setting.step_val
rotation_range = setting.rotation_range
rotation_range_val = setting.rotation_range_val
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
pool_setting_val = None if not hasattr(
setting, 'pool_setting_val') else setting.pool_setting_val
pool_setting_train = None if not hasattr(
setting, 'pool_setting_train') else setting.pool_setting_train
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
data_train, label_train, data_val, label_val = setting.load_fn(
args.path, args.path_val)
if setting.balance_fn is not None:
num_train_before_balance = data_train.shape[0]
repeat_num = setting.balance_fn(label_train)
data_train = np.repeat(data_train, repeat_num, axis=0)
label_train = np.repeat(label_train, repeat_num, axis=0)
data_train, label_train = data_utils.grouped_shuffle(
[data_train, label_train])
num_epochs = math.floor(
num_epochs * (num_train_before_balance / data_train.shape[0]))
if setting.save_ply_fn is not None:
folder = os.path.join(root_folder, 'pts')
print('{}-Saving samples as .ply files to {}...'.format(
datetime.now(), folder))
sample_num_for_ply = min(512, data_train.shape[0])
if setting.map_fn is None:
data_sample = data_train[:sample_num_for_ply]
else:
data_sample_list = []
for idx in range(sample_num_for_ply):
data_sample_list.append(setting.map_fn(data_train[idx], 0)[0])
data_sample = np.stack(data_sample_list)
setting.save_ply_fn(data_sample, folder)
num_train = data_train.shape[0]
point_num = data_train.shape[1]
num_val = data_val.shape[0]
print('{}-{:d}/{:d} training/validation samples.'.format(
datetime.now(), num_train, num_val))
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32,
shape=(None, 3, 3),
name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
data_train_placeholder = tf.placeholder(data_train.dtype,
data_train.shape,
name='data_train')
label_train_placeholder = tf.placeholder(tf.int64,
label_train.shape,
name='label_train')
data_val_placeholder = tf.placeholder(data_val.dtype,
data_val.shape,
name='data_val')
label_val_placeholder = tf.placeholder(tf.int64,
label_val.shape,
name='label_val')
handle = tf.placeholder(tf.string, shape=[], name='handle')
######################################################################
dataset_train = tf.data.Dataset.from_tensor_slices(
(data_train_placeholder, label_train_placeholder))
dataset_train = dataset_train.shuffle(buffer_size=batch_size * 4)
if setting.map_fn is not None:
dataset_train = dataset_train.map(
lambda data, label: tuple(
tf.py_func(setting.map_fn, [data, label],
[tf.float32, label.dtype])),
num_parallel_calls=setting.num_parallel_calls)
# quick draw 会用到map
if setting.keep_remainder:
dataset_train = dataset_train.batch(batch_size)
batch_num_per_epoch = math.ceil(num_train / batch_size)
else:
dataset_train = dataset_train.apply(
tf.contrib.data.batch_and_drop_remainder(batch_size))
batch_num_per_epoch = math.floor(num_train / batch_size)
dataset_train = dataset_train.repeat(num_epochs)
iterator_train = dataset_train.make_initializable_iterator()
batch_num = batch_num_per_epoch * num_epochs
print('{}-{:d} training batches.'.format(datetime.now(), batch_num))
dataset_val = tf.data.Dataset.from_tensor_slices(
(data_val_placeholder, label_val_placeholder))
if setting.map_fn is not None:
dataset_val = dataset_val.map(
lambda data, label: tuple(
tf.py_func(setting.map_fn, [data, label],
[tf.float32, label.dtype])),
num_parallel_calls=setting.num_parallel_calls)
if setting.keep_remainder:
dataset_val = dataset_val.batch(batch_size)
batch_num_val = math.ceil(num_val /
batch_size) # batch_num_val = count batch
else:
dataset_val = dataset_val.apply(
tf.contrib.data.batch_and_drop_remainder(batch_size))
batch_num_val = math.floor(num_val / batch_size)
iterator_val = dataset_val.make_initializable_iterator()
print('{}-{:d} testing batches per test.'.format(datetime.now(),
batch_num_val))
iterator = tf.data.Iterator.from_string_handle(
handle, dataset_train.output_types) # feedable iterator,
(pts_fts, labels) = iterator.get_next()
pts_fts_sampled = tf.gather_nd(
pts_fts, indices=indices,
name='pts_fts_sampled') # gather_nd is differentiable
features_augmented = None
if setting.data_dim > 3:
points_sampled, features_sampled = tf.split(
pts_fts_sampled, [3, setting.data_dim - 3],
axis=-1,
name='split_points_features')
if setting.use_extra_features:
if setting.with_normal_feature:
if setting.data_dim < 6:
print('Only 3D normals are supported!')
exit()
elif setting.data_dim == 6:
features_augmented = pf.augment(features_sampled,
rotations)
else:
normals, rest = tf.split(features_sampled,
[3, setting.data_dim - 6])
normals_augmented = pf.augment(normals, rotations)
features_augmented = tf.concat([normals_augmented, rest],
axis=-1)
else:
features_augmented = features_sampled
else:
points_sampled = pts_fts_sampled
points_augmented = pf.augment(
points_sampled, xforms,
jitter_range) # input含feature吗；要不要用这些Feature, rotate/jitter/scale加强
net = model.Net(points=points_augmented,
features=features_augmented,
is_training=is_training,
setting=setting)
logits = net.logits
probs = tf.nn.softmax(logits, name='probs')
predictions = tf.argmax(probs, axis=-1, name='predictions')
labels_2d = tf.expand_dims(labels, axis=-1, name='labels_2d')
labels_tile = tf.tile(labels_2d, (1, tf.shape(logits)[1]),
name='labels_tile')
loss_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_tile,
logits=logits)
with tf.name_scope('metrics'):
loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op)
t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy(
labels_tile, predictions)
t_1_per_class_acc_op, t_1_per_class_acc_update_op = tf.metrics.mean_per_class_accuracy(
labels_tile, predictions, setting.num_class)
reset_metrics_op = tf.variables_initializer([
var for var in tf.local_variables()
if var.name.split('/')[0] == 'metrics'
])
_ = tf.summary.scalar('loss/train',
tensor=loss_mean_op,
collections=['train'])
_ = tf.summary.scalar('t_1_acc/train',
tensor=t_1_acc_op,
collections=['train'])
_ = tf.summary.scalar('t_1_per_class_acc/train',
tensor=t_1_per_class_acc_op,
collections=['train'])
_ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val',
tensor=t_1_acc_op,
collections=['val'])
_ = tf.summary.scalar('t_1_per_class_acc/val',
tensor=t_1_per_class_acc_op,
collections=['val'])
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base,
global_step,
setting.decay_steps,
setting.decay_rate,
staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate',
tensor=lr_clip_op,
collections=['train'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op,
epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op,
momentum=setting.momentum,
use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss_op + reg_loss,
global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
# backup all code
if not args.no_code_backup:
code_folder = os.path.abspath(os.path.dirname(__file__))
shutil.copytree(
code_folder,
os.path.join(root_folder, os.path.basename(code_folder)))
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum(
[np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
with tf.Session() as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(
datetime.now(), args.load_ckpt))
else:
latest_ckpt = tf.train.latest_checkpoint(folder_ckpt)
if latest_ckpt:
print('{}-Found checkpoint {}'.format(datetime.now(),
latest_ckpt))
saver.restore(sess, latest_ckpt)
print('{}-Checkpoint loaded from {} (Iter {})'.format(
datetime.now(), latest_ckpt, sess.run(global_step)))
handle_train = sess.run(iterator_train.string_handle())
handle_val = sess.run(iterator_val.string_handle())
sess.run(iterator_train.initializer,
feed_dict={
data_train_placeholder: data_train,
label_train_placeholder: label_train,
})
for batch_idx_train in range(batch_num):
######################################################################
# Validation
if (batch_idx_train % step_val == 0 and (batch_idx_train != 0 or args.load_ckpt is not None)) \
or batch_idx_train == batch_num - 1:
sess.run(iterator_val.initializer,
feed_dict={
data_val_placeholder: data_val,
label_val_placeholder: label_val,
})
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
print('{}-Checkpoint saved to {}!'.format(
datetime.now(), filename_ckpt))
sess.run(reset_metrics_op)
for batch_idx_val in range(
batch_num_val): # batch_idx_val = record which batch
if not setting.keep_remainder \
or num_val % batch_size == 0 \
or batch_idx_val != batch_num_val - 1:
batch_size_val = batch_size
else:
batch_size_val = num_val % batch_size # batch_size_val = ex in this batch
xforms_np, rotations_np = pf.get_xforms(
batch_size_val, # xforms rely on batch_size_val
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
sess.run(
[
loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op
],
feed_dict={
handle:
handle_val,
indices:
pf.get_indices(
batch_size_val,
sample_num,
point_num,
), # randomly pick sample_num points
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter_val]),
is_training:
False,
})
loss_val, t_1_acc_val, t_1_per_class_acc_val, summaries_val, step = sess.run(
[
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
summaries_val_op, global_step
])
summary_writer.add_summary(summaries_val, step)
print(
'{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}'
.format(datetime.now(), loss_val, t_1_acc_val,
t_1_per_class_acc_val))
sys.stdout.flush()
######################################################################
######################################################################
# Training
if not setting.keep_remainder \
or num_train % batch_size == 0 \
or (batch_idx_train % batch_num_per_epoch) != (batch_num_per_epoch - 1):
batch_size_train = batch_size
else:
batch_size_train = num_train % batch_size
offset = int(
random.gauss(0, sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset # 'To train a model that takes N points as input, N (N,(N/8)2) points are used for training', 作者尝试发现这样取样训练效果好
xforms_np, rotations_np = pf.get_xforms(
batch_size_train,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
sess.run(reset_metrics_op)
sess.run(
[
train_op, loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op
],
feed_dict={
handle:
handle_train,
indices:
pf.get_indices(batch_size_train, sample_num_train,
point_num, pool_setting_train),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
is_training:
True,
})
if batch_idx_train % 10 == 0:
loss, t_1_acc, t_1_per_class_acc, summaries, step = sess.run([
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
summaries_op, global_step
])
summary_writer.add_summary(summaries, step)
print(
'{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}'
.format(datetime.now(), step, loss, t_1_acc,
t_1_per_class_acc))
sys.stdout.flush()
######################################################################
print('{}-Done!'.format(datetime.now()))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--filelist',
'-t',
help='Path to training set ground truth (.txt)',
required=True)
parser.add_argument('--filelist_val',
'-v',
help='Path to validation set ground truth (.txt)',
required=True)
parser.add_argument('--load_ckpt',
'-l',
help='Path to a check point file for load')
parser.add_argument(
'--save_folder',
'-s',
help='Path to folder for saving check points and summary',
required=True)
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting',
'-x',
help='Setting to use',
required=True)
args = parser.parse_args()
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(
args.save_folder,
'%s_%s_%d_%s' % (args.model, args.setting, os.getpid(), time_string))
if not os.path.exists(root_folder):
os.makedirs(root_folder)
# 输出重定向
# sys.stdout = open(os.path.join(root_folder, 'log.txt'), 'w')
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
print(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
step_val = 1000
num_parts = setting.num_parts
label_weights_list = setting.label_weights
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
data_train, data_num_train, label_train = data_utils.load_seg(
args.filelist)
data_val, data_num_val, label_val = data_utils.load_seg(args.filelist_val)
# shuffle
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
num_train = data_train.shape[0] # 2048
point_num = data_train.shape[1] # 6501
num_val = data_val.shape[0]
print('{}-{:d}/{:d} training/validation samples.'.format(
datetime.now(), num_train, num_val))
batch_num = (num_train * num_epochs + batch_size - 1) // batch_size
print('{}-{:d} training batches.'.format(datetime.now(), batch_num))
batch_num_val = math.ceil(num_val / batch_size)
print('{}-{:d} testing batches per test.'.format(datetime.now(),
batch_num_val))
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3),
name="xforms") # 缩放形变
rotations = tf.placeholder(tf.float32,
shape=(None, 3, 3),
name="rotations") # 旋转形变
jitter_range = tf.placeholder(tf.float32, shape=(1),
name="jitter_range") # 数据扰动
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
pts_fts = tf.placeholder(tf.float32,
shape=(None, point_num, setting.data_dim),
name='pts_fts')
labels_seg = tf.placeholder(tf.int32,
shape=(None, point_num),
name='labels_seg')
labels_weights = tf.placeholder(tf.float32,
shape=(None, point_num),
name='labels_weights')
loss_val_avg = tf.placeholder(tf.float32)
t_1_acc_val_avg = tf.placeholder(tf.float32)
t_1_acc_val_instance_avg = tf.placeholder(tf.float32)
t_1_acc_val_others_avg = tf.placeholder(tf.float32)
######################################################################
# Set Inputs(points,features_sampled)
features_sampled = None
if setting.data_dim > 3:
# [3 pts_xyz , 2 img_xy , 4 extra_features]
points, _, features = tf.split(pts_fts, [
setting.data_format["pts_xyz"], setting.data_format["img_xy"],
setting.data_format["extra_features"]
],
axis=-1,
name='split_points_xy_features')
# r, g, b, i
if setting.use_extra_features:
# 选取extra特征
features_sampled = tf.gather_nd(features,
indices=indices,
name='features_sampled')
else:
points = pts_fts
points_sampled = tf.gather_nd(points,
indices=indices,
name='points_sampled')
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
# N * 6501
labels_sampled = tf.gather_nd(labels_seg,
indices=indices,
name='labels_sampled')
labels_weights_sampled = tf.gather_nd(labels_weights,
indices=indices,
name='labels_weight_sampled')
# Build net
net = model.Net(points_augmented, features_sampled, None, None, num_parts,
is_training, setting)
logits, probs = net.logits, net.probs
loss_op = tf.losses.sparse_softmax_cross_entropy(
labels=labels_sampled, logits=logits, weights=labels_weights_sampled)
t_1_acc_op = pf.top_1_accuracy(probs, labels_sampled)
t_1_acc_instance_op = pf.top_1_accuracy(probs, labels_sampled,
labels_weights_sampled, 0.6)
t_1_acc_others_op = pf.top_1_accuracy(probs, labels_sampled,
labels_weights_sampled, 0.6, "less")
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
# lr decay
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base,
global_step,
setting.decay_steps,
setting.decay_rate,
staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
# Optimizer
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op,
epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op,
momentum=0.9,
use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss_op + reg_loss,
global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=1)
# backup this file, model and setting
if not os.path.exists(os.path.join(root_folder, args.model)):
os.makedirs(os.path.join(root_folder, args.model))
shutil.copy(__file__, os.path.join(root_folder,
os.path.basename(__file__)))
shutil.copy(os.path.join(os.path.dirname(__file__), args.model + '.py'),
os.path.join(root_folder, args.model + '.py'))
shutil.copy(
os.path.join(os.path.dirname(__file__),
args.model.split("_")[0] + "_kitti" + '.py'),
os.path.join(root_folder,
args.model.split("_")[0] + "_kitti" + '.py'))
shutil.copy(os.path.join(setting_path, args.setting + '.py'),
os.path.join(root_folder, args.model, args.setting + '.py'))
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum(
[np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
_ = tf.summary.scalar('loss/train_seg',
tensor=loss_op,
collections=['train'])
_ = tf.summary.scalar('t_1_acc/train_seg',
tensor=t_1_acc_op,
collections=['train'])
_ = tf.summary.scalar('t_1_acc/train_seg_instance',
tensor=t_1_acc_instance_op,
collections=['train'])
_ = tf.summary.scalar('t_1_acc/train_seg_others',
tensor=t_1_acc_others_op,
collections=['train'])
_ = tf.summary.scalar('loss/val_seg',
tensor=loss_val_avg,
collections=['val'])
_ = tf.summary.scalar('t_1_acc/val_seg_instance',
tensor=t_1_acc_val_instance_avg,
collections=['val'])
_ = tf.summary.scalar('t_1_acc/val_seg',
tensor=t_1_acc_val_avg,
collections=['val'])
_ = tf.summary.scalar('t_1_acc/val_seg_others',
tensor=t_1_acc_val_others_avg,
collections=['val'])
_ = tf.summary.scalar('learning_rate',
tensor=lr_clip_op,
collections=['train'])
# Session Run
with tf.Session() as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(
datetime.now(), args.load_ckpt))
for batch_idx in range(batch_num):
if (batch_idx != 0 and batch_idx % step_val
== 0) or batch_idx == batch_num - 1:
######################################################################
# Validation
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
print('{}-Checkpoint saved to {}!'.format(
datetime.now(), filename_ckpt))
losses_val = []
t_1_accs = []
t_1_accs_instance = []
t_1_accs_others = []
for batch_val_idx in range(math.ceil(num_val / batch_size)):
start_idx = batch_size * batch_val_idx
end_idx = min(start_idx + batch_size, num_val)
batch_size_val = end_idx - start_idx
points_batch = data_val[start_idx:end_idx, ...]
points_num_batch = data_num_val[start_idx:end_idx, ...]
labels_batch = label_val[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[label_val[
start_idx:end_idx, ...]]
xforms_np, rotations_np = pf.get_xforms(
batch_size_val, scaling_range=scaling_range_val)
sess_op_list = [
loss_op, t_1_acc_op, t_1_acc_instance_op,
t_1_acc_others_op
]
sess_feed_dict = {
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_val, sample_num,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter_val]), # data jitter
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
False
}
loss_val, t_1_acc_val, t_1_acc_val_instance, t_1_acc_val_others = sess.run(
sess_op_list, feed_dict=sess_feed_dict)
print(
'{}-[Val ]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f}'
.format(datetime.now(), batch_val_idx, loss_val,
t_1_acc_val))
sys.stdout.flush()
losses_val.append(loss_val * batch_size_val)
t_1_accs.append(t_1_acc_val * batch_size_val)
t_1_accs_instance.append(t_1_acc_val_instance *
batch_size_val)
t_1_accs_others.append(t_1_acc_val_others * batch_size_val)
loss_avg = sum(losses_val) / num_val
t_1_acc_avg = sum(t_1_accs) / num_val
t_1_acc_instance_avg = sum(t_1_accs_instance) / num_val
t_1_acc_others_avg = sum(t_1_accs_others) / num_val
summaries_feed_dict = {
loss_val_avg: loss_avg,
t_1_acc_val_avg: t_1_acc_avg,
t_1_acc_val_instance_avg: t_1_acc_instance_avg,
t_1_acc_val_others_avg: t_1_acc_others_avg
}
summaries_val = sess.run(summaries_val_op,
feed_dict=summaries_feed_dict)
summary_writer.add_summary(summaries_val, batch_idx)
print('{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f}'.
format(datetime.now(), loss_avg, t_1_acc_avg))
sys.stdout.flush()
######################################################################
######################################################################
# Training
start_idx = (batch_size * batch_idx) % num_train
end_idx = min(start_idx + batch_size, num_train)
batch_size_train = end_idx - start_idx
points_batch = data_train[start_idx:end_idx, ...] # 2048 6501 9
points_num_batch = data_num_train[start_idx:end_idx, ...] #2048
labels_batch = label_train[start_idx:end_idx, ...] #2048 6501
weights_batch = np.array(label_weights_list)[labels_batch] # 值为零
if start_idx + batch_size_train == num_train:
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
offset = int(random.gauss(0, sample_num // 8)) # 均值 方差
offset = max(offset, -sample_num // 4)
offset = min(offset, sample_num // 4)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(
batch_size_train, scaling_range=scaling_range) #数据增强
sess_op_list = [
train_op, loss_op, t_1_acc_op, t_1_acc_instance_op,
t_1_acc_others_op, summaries_op
]
sess_feed_dict = {
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_train, sample_num_train,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
True
}
_, loss, t_1_acc, t_1_acc_instance, t_1_acc_others, summaries = sess.run(
sess_op_list, feed_dict=sess_feed_dict)
print('{}-[Train]-Iter: {:06d} Loss_seg: {:.4f} T-1 Acc: {:.4f}'.
format(datetime.now(), batch_idx, loss, t_1_acc))
summary_writer.add_summary(summaries, batch_idx)
sys.stdout.flush()
######################################################################
print('{}-Done!'.format(datetime.now()))
def evaluate(model_num, num_votes, verbose=False):
# Model path
model_path = MODELS_PATH + '/model_' + str(model_num) + '/model_' + str(
model_num)
# Start session
with tf.Session() as sess:
#sess.run(init_op)
# Load the model
saver = tf.train.import_meta_graph(model_path + '.meta')
saver.restore(sess, model_path)
if verbose:
print('{}-Checkpoint loaded from {}!'.format(
datetime.now(), model_path))
print("LOADED!")
exit()
# Handle
handle_val = sess.run(iterator_val.string_handle())
# Reset metrics
#sess.run(reset_metrics_op)
# Data to remember
voted_logits = []
voted_labels = None
# Num votes range
for _ in range(num_votes):
# Restart dataset iterator
sess.run(iterator_val.initializer,
feed_dict={
data_val_placeholder: data_val,
label_val_placeholder: label_val,
})
# Data to remember
batch_logits = []
batch_labels = []
# For each batch in dataset
for batch_idx_val in range(int(batch_num_val)):
# Set the batchsize
if not setting.keep_remainder or num_val % batch_size == 0 or batch_idx_val != batch_num_val - 1:
batch_size_val = batch_size
else:
batch_size_val = num_val % batch_size
# Get the xforms and rotations
xforms_np, rotations_np = pf.get_xforms(
batch_size_val,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
# Get logits and labels
pred_val, labels_val = sess.run(
[logits, labels_tile],
feed_dict={
handle:
handle_val,
indices:
pf.get_indices(batch_size_val, sample_num, point_num),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter_val]),
is_training:
False,
})
print("PREDICTED", pred_val)
print("VALUE", labels_val)
exit()
# Remember data
batch_logits.append(np.squeeze(pred_val, axis=1))
batch_labels.append(np.squeeze(labels_val))
# Concatenate
batch_logits = np.concatenate(batch_logits, axis=0)
batch_labels = np.concatenate(batch_labels, axis=0)
voted_logits.append(batch_logits)
voted_labels = batch_labels
# Final concat
voted_logits = np.sum(np.stack(voted_logits).transpose(1, 2, 0),
axis=-1)
voted_predic = np.argmax(voted_logits, axis=-1)
voted_accura = float(
np.sum(voted_predic == voted_labels)) / len(voted_labels)
return voted_logits, voted_labels, voted_accura
def main():
# sys.stdout = open("./log_test_mynet.txt", 'w')
modelname = "pointcnn_cls"
# 载入pointcnn
model = importlib.import_module(modelname)
# 载入超参数
setting_path = os.path.join(os.path.dirname(__file__), modelname)
sys.path.append(setting_path)
setting = importlib.import_module("mynet")
sample_num = setting.sample_num
rotation_range_val = setting.rotation_range_val
scaling_range_val = setting.scaling_range_val
jitter_val = setting.jitter_val
# 输入文件
filepath = "../data/mynet/test/1/2018-05-12-12-52-11_Velodyne-HDL-32-Data(1955to2295)_1955.pcd"
batch_size_val = 1 # 不知道干嘛用的参数
data_frame = np.zeros((batch_size_val, sample_num, 3), dtype=np.float32)
#frame_id = (filepath.split('_')[-1]).split('.')[0]
#######################################################################
# Loading PCD
with open(filepath, 'r') as f:
xyz = np.array([ [float(value) for value in line.split(' ')[0:3]]
for line in f.readlines()[11:len(f.readlines())-1]])
#######################################################################
xyz = xyz.astype(np.float32)
np.random.shuffle(xyz)
pt_num = xyz.shape[0]
indices = np.random.choice(pt_num, sample_num, replace=(pt_num <sample_num))
data_frame[0, ...] = xyz[indices]
#data_frame[0, ...] = xyz
######################################################################
# Placeholders
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
is_training = tf.placeholder(tf.bool, name='is_training')
data_frame_placeholder = tf.placeholder(data_frame.dtype, data_frame.shape, name="data_frame")
######################################################################
#######################################################################
# 网络的输入
points_augmented = pf.augment(data_frame_placeholder, xforms, jitter_range)
# 构建网络,暂时不使用其他特征
net = model.Net(points=points_augmented, features=None, is_training=is_training, setting=setting)
logits = net.logits
probs = tf.nn.softmax(logits, name='probs')
# 网络的输出
predict = tf.argmax(probs, axis=-1, name='predictions')
#######################################################################
#######################################################################
load_ckpt = "/home/elvin/models/mynet/pointcnn_cls_mynet_2019-07-05-20-00-17_31693/ckpts/iter-528"
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, load_ckpt) # Load the model
xforms_np, rotations_np = pf.get_xforms(batch_size_val, rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
res = sess.run(predict, feed_dict={data_frame_placeholder: data_frame,
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter_val]),
is_training: False,
})
print("res=", res[0][0])
######################################################################
#sys.stdout.flush()
return res[0][0]
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--load_ckpt', '-l', help='Path to a check point file for load')
parser.add_argument('--save_folder', '-s',default='models', help='Path to folder for saving check points and summary')
parser.add_argument('--model', '-m', default='pointcnn_cls',help='Model to use')
parser.add_argument('--setting', '-x',default='ScanObjectNN_x3_l4', help='Setting to use')
parser.add_argument('--epochs', help='Number of training epochs (default defined in setting)', type=int)
parser.add_argument('--batch_size', help='Batch size (default defined in setting)', type=int)
parser.add_argument('--log', help='Log to FILE in save folder; use - for stdout (default is log.txt)', metavar='FILE', default='log.txt')#default='log.txt'输出存入文档//default='-'显示输出
parser.add_argument('--no_timestamp_folder', help='Dont save to timestamp folder', action='store_true')
parser.add_argument('--no_code_backup', help='Dont backup code', action='store_true',default='True')
parser.add_argument('--err_data', type=float, default=5696)
parser.add_argument('--weight', type=float, default=10)
args = parser.parse_args()
if not args.no_timestamp_folder:
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(args.save_folder, '%s_%s_%s_%d' % (args.model, args.setting, time_string, os.getpid()))
else:
root_folder = args.save_folder
if not os.path.exists(root_folder):
os.makedirs(root_folder)
if args.log != '-':
sys.stdout = open(os.path.join(root_folder, args.log), 'w')
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = args.epochs or setting.num_epochs
batch_size = args.batch_size or setting.batch_size
sample_num = setting.sample_num#1024
step_val = setting.step_val
rotation_range = setting.rotation_range
rotation_range_val = setting.rotation_range_val
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
pool_setting_val = None if not hasattr(setting, 'pool_setting_val') else setting.pool_setting_val
pool_setting_train = None if not hasattr(setting, 'pool_setting_train') else setting.pool_setting_train
ERR = args.err_data
WEIGHT = args.weight
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
if setting.data_set == 'ModelNet':
prepare_data.make_ModelNet_data_A_B(ERR,batch_size)
if setting.data_set == 'ScanObjectNN':
prepare_data.make_ScanObjectNN_data_batch_A_B(ERR,batch_size)
data_train, label_train, data_val, label_val = setting.load_fn(setting.path, setting.path_val)
#data_train:[2*11416,2048,3],label_train:[11416,],data_val:[2882,2048,3],label_val:[2882,]
if setting.balance_fn is not None:#None
num_train_before_balance = data_train.shape[0]
repeat_num = setting.balance_fn(label_train)
data_train = np.repeat(data_train, repeat_num, axis=0)
label_train = np.repeat(label_train, repeat_num, axis=0)
data_train, label_train = data_utils.grouped_shuffle([data_train, label_train])
num_epochs = math.floor(num_epochs * (num_train_before_balance / data_train.shape[0]))
if setting.save_ply_fn is not None:#None
folder = os.path.join(root_folder, 'pts')
print('{}-Saving samples as .ply files to {}...'.format(datetime.now(), folder))
sample_num_for_ply = min(512, data_train.shape[0])
if setting.map_fn is None:
data_sample = data_train[:sample_num_for_ply]
else:
data_sample_list = []
for idx in range(sample_num_for_ply):
data_sample_list.append(setting.map_fn(data_train[idx], 0)[0])
data_sample = np.stack(data_sample_list)
setting.save_ply_fn(data_sample, folder)
num_train = data_train.shape[0]#11416
point_num = data_train.shape[1]#2048
num_val = data_val.shape[0]#2882
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
data_train_placeholder = tf.placeholder(data_train.dtype, data_train.shape, name='data_train')
label_train_placeholder = tf.placeholder(tf.int64, label_train.shape, name='label_train')
data_val_placeholder = tf.placeholder(data_val.dtype, data_val.shape, name='data_val')
label_val_placeholder = tf.placeholder(tf.int64, label_val.shape, name='label_val')
handle = tf.placeholder(tf.string, shape=[], name='handle')
######################################################################
dataset_train = tf.data.Dataset.from_tensor_slices((data_train_placeholder, label_train_placeholder))
#dataset_train = dataset_train.shuffle(buffer_size=batch_size * 4)
if setting.map_fn is not None:#None
dataset_train = dataset_train.map(lambda data, label:
tuple(tf.py_func(setting.map_fn, [data, label], [tf.float32, label.dtype])),
num_parallel_calls=setting.num_parallel_calls)
if setting.keep_remainder:#Ture
dataset_train = dataset_train.batch(batch_size)
batch_num_per_epoch = math.ceil(num_train / batch_size)
else:
dataset_train = dataset_train.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
batch_num_per_epoch = math.floor(num_train / batch_size)
dataset_train = dataset_train.repeat(num_epochs)
iterator_train = dataset_train.make_initializable_iterator()
batch_num = batch_num_per_epoch * num_epochs
print('{}-{:d} training batches.'.format(datetime.now(), batch_num))
dataset_val = tf.data.Dataset.from_tensor_slices((data_val_placeholder, label_val_placeholder))
if setting.map_fn is not None:#None
dataset_val = dataset_val.map(lambda data, label: tuple(tf.py_func(
setting.map_fn, [data, label], [tf.float32, label.dtype])), num_parallel_calls=setting.num_parallel_calls)
if setting.keep_remainder:#Ture
dataset_val = dataset_val.batch(batch_size)
batch_num_val = math.ceil(num_val / batch_size)
else:
dataset_val = dataset_val.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
batch_num_val = math.floor(num_val / batch_size)
iterator_val = dataset_val.make_initializable_iterator()
print('{}-{:d} testing batches per test.'.format(datetime.now(), batch_num_val))
iterator = tf.data.Iterator.from_string_handle(handle, dataset_train.output_types)
(pts_fts, labels) = iterator.get_next()
pts_fts_sampled = tf.gather_nd(pts_fts, indices=indices, name='pts_fts_sampled')
features_augmented = None
points_sampled = pts_fts_sampled
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
net = model.Net(points=points_augmented, features=features_augmented, is_training=is_training, setting=setting)
logits = net.logits
feature_A = net.feature_list_A
feature_B = net.feature_list_B
probs = tf.nn.softmax(logits, name='probs')
predictions = tf.argmax(probs, axis=-1, name='predictions')
labels_2d = tf.expand_dims(labels, axis=-1, name='labels_2d')
labels_tile = tf.tile(labels_2d, (1, tf.shape(logits)[1]), name='labels_tile')
# --------------------------------------------------------------------
# compute the Loss of DINet
different = tf.square(tf.subtract(feature_A,feature_B))
different = tf.reduce_sum(different,1)
label_A = tf.to_float(labels[0:tf.to_int32(batch_size/2)])
label_B = tf.to_float(labels[tf.to_int32(batch_size/2):tf.to_int32(batch_size)])
f_same = tf.multiply(tf.add(tf.sign(-tf.abs(tf.subtract(label_A,label_B))),1),different)
f_diff = tf.divide(tf.sign(tf.abs(tf.subtract(label_A,label_B))),different)
f_same = tf.reduce_sum(f_same,0)
f_diff = tf.reduce_sum(f_diff,0)
f_loss = tf.add(f_diff,f_same)
# --------------------------------------------------------------------
loss_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_tile, logits=logits) + ((f_loss/(61440*batch_size/2)) * WEIGHT)#10
with tf.name_scope('metrics'):
loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op)
t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy(labels_tile, predictions)
t_1_per_class_acc_op, t_1_per_class_acc_update_op = tf.metrics.mean_per_class_accuracy(labels_tile,
predictions,
setting.num_class)
reset_metrics_op = tf.variables_initializer([var for var in tf.local_variables()
if var.name.split('/')[0] == 'metrics'])
_ = tf.summary.scalar('loss/train', tensor=loss_mean_op, collections=['train'])
_ = tf.summary.scalar('t_1_acc/train', tensor=t_1_acc_op, collections=['train'])
_ = tf.summary.scalar('t_1_per_class_acc/train', tensor=t_1_per_class_acc_op, collections=['train'])
_ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val', tensor=t_1_acc_op, collections=['val'])
_ = tf.summary.scalar('t_1_per_class_acc/val', tensor=t_1_per_class_acc_op, collections=['val'])
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base, global_step, setting.decay_steps,
setting.decay_rate, staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate', tensor=lr_clip_op, collections=['train'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op, epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op, momentum=setting.momentum, use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss_op + reg_loss, global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
# backup all code
if not args.no_code_backup:
code_folder = os.path.abspath(os.path.dirname(__file__))
shutil.copytree(code_folder, os.path.join(root_folder, os.path.basename(code_folder)))
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum([np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
with tf.Session() as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(datetime.now(), args.load_ckpt))
else:
latest_ckpt = tf.train.latest_checkpoint(folder_ckpt)
if latest_ckpt:
print('{}-Found checkpoint {}'.format(datetime.now(), latest_ckpt))
saver.restore(sess, latest_ckpt)
print('{}-Checkpoint loaded from {} (Iter {})'.format(
datetime.now(), latest_ckpt, sess.run(global_step)))
handle_train = sess.run(iterator_train.string_handle())
handle_val = sess.run(iterator_val.string_handle())
sess.run(iterator_train.initializer, feed_dict={
data_train_placeholder: data_train,
label_train_placeholder: label_train,
})
for batch_idx_train in range(batch_num):
######################################################################
# Validation
if (batch_idx_train % step_val == 0 and (batch_idx_train != 0 or args.load_ckpt is not None)) \
or batch_idx_train == batch_num - 1:
sess.run(iterator_val.initializer, feed_dict={
data_val_placeholder: data_val,
label_val_placeholder: label_val,
})
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
print('{}-Checkpoint saved to {}!'.format(datetime.now(), filename_ckpt))
sess.run(reset_metrics_op)
total_seen_class = [0 for _ in range(setting.num_class)]
total_correct_class = [0 for _ in range(setting.num_class)]
for batch_idx_val in range(batch_num_val):
if not setting.keep_remainder \
or num_val % batch_size == 0 \
or batch_idx_val != batch_num_val - 1:
batch_size_val = batch_size
else:
batch_size_val = num_val % batch_size
xforms_np, rotations_np = pf.get_xforms(batch_size_val,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
lll, ppp, _, _ = sess.run([labels, predictions, t_1_acc_update_op, t_1_per_class_acc_update_op],
feed_dict={
handle: handle_val,
indices: pf.get_indices(batch_size_val, sample_num, point_num,
),
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter_val]),
is_training: False,
})
ppp = np.reshape(ppp,[-1,])
for i in range(len(lll)):
l = lll[i]
total_seen_class[l] +=1
total_correct_class[l] +=(ppp[i] == l)
t_1_acc_val, t_1_per_class_acc_val, summaries_val, step = sess.run(
[t_1_acc_op, t_1_per_class_acc_op, summaries_val_op, global_step])
summary_writer.add_summary(summaries_val, step)
print('{}-[Val ]-Average: T-1 Acc: {:.4f} T-1 mAcc: {:.4f}'
.format(datetime.now(), t_1_acc_val, t_1_per_class_acc_val))
print('every class acc:',np.array(total_correct_class)/np.array(total_seen_class,dtype = np.float))
sys.stdout.flush()
######################################################################
######################################################################
# Training
if not setting.keep_remainder \
or num_train % batch_size == 0 \
or (batch_idx_train % batch_num_per_epoch) != (batch_num_per_epoch - 1):
batch_size_train = batch_size
else:
batch_size_train = num_train % batch_size
offset = int(random.gauss(0, sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(batch_size_train,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
sess.run(reset_metrics_op)
sess.run([train_op, loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op],
feed_dict={
handle: handle_train,
indices: pf.get_indices(batch_size_train, sample_num_train, point_num, pool_setting_train),
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter]),
is_training: True,
})
if batch_idx_train % 10 == 0:
loss, t_1_acc, t_1_per_class_acc, summaries, step = sess.run([loss_mean_op,
t_1_acc_op,
t_1_per_class_acc_op,
summaries_op,
global_step])
summary_writer.add_summary(summaries, step)
print('{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}'
.format(datetime.now(), step, loss, t_1_acc, t_1_per_class_acc))
sys.stdout.flush()
######################################################################
print('{}-Done!'.format(datetime.now()))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dir_bin',
'-i',
help='Path to binary files dir (*.npy)',
required=True)
parser.add_argument('--filelist',
'-t',
help='Path to training set ground truth (.txt)',
required=True)
parser.add_argument('--filelist_val',
'-v',
help='Path to validation set ground truth (.txt)',
required=False)
parser.add_argument('--load_ckpt',
'-l',
help='Path to a check point file for load')
parser.add_argument(
'--save_folder',
'-s',
help='Path to folder for saving check points and summary',
required=True)
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting',
'-x',
help='Setting to use',
required=True)
args = parser.parse_args()
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(
args.save_folder,
'%s_%s_%s_%d' % (args.model, args.setting, time_string, os.getpid()))
if not os.path.exists(root_folder):
os.makedirs(root_folder)
# sys.stdout = open(os.path.join(root_folder, 'log.txt'), 'w')
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
step_val = setting.step_val
label_weights_list = setting.label_weights
rotation_range = setting.rotation_range
rotation_range_val = setting.rotation_range_val
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
dir_bin = args.dir_bin
path_filelist_train = args.filelist
path_filelist_val = args.filelist_val
list_fru_train, max_point_num_train = data_utils.load_bin_all(
dir_bin, path_filelist_train)
if path_filelist_val is None:
print("train with no val data")
list_fru_val = list_fru_train[0:100]
max_point_num_val = 0
else:
list_fru_val, max_point_num_val = data_utils.load_bin_all(
dir_bin, path_filelist_val)
max_point_num = max(max_point_num_train, max_point_num_val)
# shuffle
random.shuffle(list_fru_train)
num_train = len(list_fru_train)
num_val = len(list_fru_val)
print('{}-{:d}/{:d} training/validation samples.'.format(
datetime.now(), num_train, num_val))
batch_num = (num_train * num_epochs + batch_size - 1) // batch_size
print('{}-{:d} training batches.'.format(datetime.now(), batch_num))
batch_num_val = int(math.ceil(num_val / batch_size))
print('{}-{:d} testing batches per test.'.format(datetime.now(),
batch_num_val))
######################################################################
# Placeholders
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32,
shape=(None, 3, 3),
name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
max_sample_num = sample_num + sample_num * setting.sample_num_clip
pts_fts_sampled = tf.placeholder(tf.float32,
shape=(None, max_sample_num,
setting.data_dim),
name='pts_fts')
labels_sampled = tf.placeholder(tf.int64,
shape=(None, max_sample_num),
name='labels_seg')
labels_weights_sampled = tf.placeholder(tf.float32,
shape=(None, max_sample_num),
name='labels_weights')
######################################################################
features_augmented = None
if setting.data_dim > 3:
points_sampled, features_sampled = tf.split(
pts_fts_sampled, [3, setting.data_dim - 3],
axis=-1,
name='split_points_features')
if setting.use_extra_features:
if setting.with_normal_feature:
if setting.data_dim < 6:
print('Only 3D normals are supported!')
exit()
elif setting.data_dim == 6:
features_augmented = pf.augment(features_sampled,
rotations)
else:
normals, rest = tf.split(features_sampled,
[3, setting.data_dim - 6])
normals_augmented = pf.augment(normals, rotations)
features_augmented = tf.concat([normals_augmented, rest],
axis=-1)
else:
features_augmented = features_sampled
else:
points_sampled = pts_fts_sampled
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
# points_augmented = points_sampled
net = model.Net(points_augmented, features_augmented, is_training, setting)
logits = net.logits
probs = tf.nn.softmax(logits, name='probs')
predictions = tf.argmax(probs, axis=-1, name='predictions')
loss_op = tf.losses.sparse_softmax_cross_entropy(
labels=labels_sampled, logits=logits, weights=labels_weights_sampled)
with tf.name_scope('metrics'):
loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op)
t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy(
labels_sampled, predictions, weights=labels_weights_sampled)
t_1_per_class_acc_op, t_1_per_class_acc_update_op = \
tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class,
weights=labels_weights_sampled)
t_1_mean_iou_op, t_1_mean_iou_update_op = \
tf.metrics.mean_iou(labels_sampled, predictions, setting.num_class, labels_weights_sampled)
reset_metrics_op = tf.variables_initializer([
var for var in tf.local_variables()
if var.name.split('/')[0] == 'metrics'
])
_ = tf.summary.scalar('loss/train',
tensor=loss_mean_op,
collections=['train'])
_ = tf.summary.scalar('t_1_acc/train',
tensor=t_1_acc_op,
collections=['train'])
_ = tf.summary.scalar('t_1_per_class_acc/train',
tensor=t_1_per_class_acc_op,
collections=['train'])
_ = tf.summary.scalar('t_1_mean_iou/train',
tensor=t_1_mean_iou_op,
collections=['train'])
_ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val',
tensor=t_1_acc_op,
collections=['val'])
_ = tf.summary.scalar('t_1_per_class_acc/val',
tensor=t_1_per_class_acc_op,
collections=['val'])
_ = tf.summary.scalar('t_1_mean_iou/val',
tensor=t_1_mean_iou_op,
collections=['val'])
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base,
global_step,
setting.decay_steps,
setting.decay_rate,
staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate',
tensor=lr_clip_op,
collections=['train'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op,
epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op,
momentum=setting.momentum,
use_nesterov=True)
else:
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op,
epsilon=setting.epsilon) # adam
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss_op + reg_loss,
global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
# backup all code
code_folder = os.path.abspath(os.path.dirname(__file__))
shutil.copytree(code_folder,
os.path.join(root_folder, os.path.basename(code_folder)))
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum(
[np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
with tf.Session() as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(
datetime.now(), args.load_ckpt))
for batch_idx_train in range(batch_num):
if (batch_idx_train % step_val == 0 and (batch_idx_train != 0 or args.load_ckpt is not None)) \
or batch_idx_train == batch_num - 1:
######################################################################
# Validation
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
print('{}-Checkpoint saved to {}!'.format(
datetime.now(), filename_ckpt))
sess.run(reset_metrics_op)
for batch_val_idx in range(batch_num_val):
start_idx = batch_size * batch_val_idx
end_idx = min(start_idx + batch_size, num_val)
batch_size_val = end_idx - start_idx
fru_batch = list_fru_train[start_idx:end_idx]
points_batch_sampled, labels_batch_sampled, weights_batch_sampled = \
df_utils.group_sampling_fru(fru_batch, sample_num, label_weights_list)
xforms_np, rotations_np = pf.get_xforms(
batch_size_val,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
sess.run(
[
loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op, t_1_mean_iou_update_op
],
feed_dict={
pts_fts_sampled: points_batch_sampled,
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter_val]),
labels_sampled: labels_batch_sampled,
labels_weights_sampled: weights_batch_sampled,
is_training: False,
})
loss_val, t_1_acc_val, t_1_per_class_acc_val, t_1_mean_iou_val, summaries_val = sess.run(
[
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
t_1_mean_iou_op, summaries_val_op
])
summary_writer.add_summary(summaries_val, batch_idx_train)
print(
'{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f} T-1 mIOU: {:.4f}'
.format(datetime.now(), loss_val, t_1_acc_val,
t_1_per_class_acc_val, t_1_mean_iou_val))
######################################################################
######################################################################
# Training
start_idx = (batch_size * batch_idx_train) % num_train
end_idx = min(start_idx + batch_size, num_train)
batch_size_train = end_idx - start_idx
fru_batch = list_fru_train[start_idx:end_idx]
if start_idx + batch_size_train == num_train:
random.shuffle(list_fru_train)
offset = int(
random.gauss(0, sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
points_batch_sampled, labels_batch_sampled, weights_batch_sampled = \
df_utils.group_sampling_fru(fru_batch, sample_num_train, label_weights_list)
xforms_np, rotations_np = pf.get_xforms(
batch_size_train,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
sess.run(reset_metrics_op)
sess.run(
[
train_op, loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op, t_1_mean_iou_update_op
],
feed_dict={
pts_fts_sampled: points_batch_sampled,
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter]),
labels_sampled: labels_batch_sampled,
labels_weights_sampled: weights_batch_sampled,
is_training: True,
})
if batch_idx_train % 10 == 0:
loss, t_1_acc, t_1_per_class_acc, t_1_mean_iou, summaries = sess.run(
[
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
t_1_mean_iou_op, summaries_op
])
summary_writer.add_summary(summaries, batch_idx_train)
print(
'{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f} T-1 mIOU: {:.4f}'
.format(datetime.now(), batch_idx_train, loss, t_1_acc,
t_1_per_class_acc, t_1_mean_iou))
######################################################################
print('{}-Done!'.format(datetime.now()))
def main():
modelname = "pointcnn_cls"
settingname = "mynet_2048"
load_ckpt = "../models/ckpts/iter-5007"
temp_dir = "../haimai/tmp" # 存放临时文件的位置
if(os.path.exists(temp_dir) == False):
os.makedirs(temp_dir)
temp_frame = os.path.join(temp_dir, "cur_frame.pcd") # 存放当前帧的位置
temp_isend = os.path.join(temp_dir, "isend")
temp_c_writing = os.path.join(temp_dir, "c_is_writing")
setting_path = os.path.join(os.path.dirname(__file__), modelname)
sys.path.append(setting_path)
setting = importlib.import_module(settingname) ### 载入超参数
batch_size_val = 1 # 一次只预测一帧点云
data_frame = np.zeros((batch_size_val, setting.sample_num, 3), dtype=np.float32)
### Placeholders
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
is_training = tf.placeholder(tf.bool, name='is_training')
data_frame_placeholder = tf.placeholder(data_frame.dtype, data_frame.shape, name="data_frame")
model = importlib.import_module(modelname) # 载入pointcnn
points_augmented = pf.augment(data_frame_placeholder, xforms, jitter_range)
net = model.Net(points=points_augmented, features=None, is_training=is_training, setting=setting) # 构建网络,暂时不使用其他特征
logits = net.logits
probs = tf.nn.softmax(logits, name='probs')
predict = tf.argmax(probs, axis=-1, name='predictions') # 网络的输出
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, load_ckpt) # Load the model
xforms_np, rotations_np = pf.get_xforms(batch_size_val, rotation_range=setting.rotation_range_val,
scaling_range=setting.scaling_range_val,
order=setting.rotation_order)
### 循环进行单帧的标签预测
while (os.path.exists(temp_isend) == False): # 非最后一帧
if (os.path.exists(temp_frame) == True): # 点云帧
while (os.path.exists(temp_c_writing) == True): # C++ is writing PCD
time.sleep(0.05)
data_frame[0, ...] = load_pcd(temp_frame) # 读取点云
label = sess.run(predict, feed_dict={data_frame_placeholder: data_frame,
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([setting.jitter_val]),
is_training: False,
})
### 传递预测结果给mapping
label_file = open(temp_dir + "/label_" + str(label[0][0]), 'w')
label_file.close()
if delete_file(temp_frame) == False:
print("Delete frame error.")
return
delete_file(temp_isend) # 删除通信文件
return
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dir_train', '-t', help='Path to dir of train set', required=True)
parser.add_argument('--dir_val', '-v', help='Path to dir of val set', required=False)
parser.add_argument('--load_ckpt', '-l', help='Path to a check point file for load')
parser.add_argument('--save_folder', '-s', help='Path to folder for saving check points and summary', required=True)
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting', '-x', help='Setting to use', required=True)
args = parser.parse_args()
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(args.save_folder, '%s_%s_%d_%s' % (args.model, args.setting, os.getpid(), time_string))
if not os.path.exists(root_folder):
os.makedirs(root_folder)
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
print(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
step_val = setting.step_val
num_parts = setting.num_parts
label_weights_list = setting.label_weights
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
if args.dir_val is None:
print("only load train")
index_length_train, points_ele_train, intensities_train, labels_train, _ = data_utils.load_bin(args.dir_train)
index_length_val = index_length_train
points_ele_val = points_ele_train
intensities_val = intensities_train
labels_val = labels_train
else:
print("load train and val")
index_length_train, points_ele_train, intensities_train, labels_train, _ = data_utils.load_bin(args.dir_train)
index_length_val, points_ele_val, intensities_val, labels_val, _ = data_utils.load_bin(args.dir_val)
# shuffle
index_length_train = data_utils.index_shuffle(index_length_train)
index_length_val = data_utils.index_shuffle(index_length_val)
num_train = index_length_train.shape[0]
point_num = max(np.max(index_length_train[:, 1]), np.max(index_length_val[:, 1]))
num_val = index_length_val.shape[0]
print('{}-{:d}/{:d} training/validation samples.'.format(datetime.now(), num_train, num_val))
batch_num = (num_train * num_epochs + batch_size - 1) // batch_size
print('{}-{:d} training batches.'.format(datetime.now(), batch_num))
batch_num_val = math.ceil(num_val / batch_size)
print('{}-{:d} testing batches per test.'.format(datetime.now(), batch_num_val))
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
pts = tf.placeholder(tf.float32, shape=(None, point_num, setting.point_dim), name='pts')
fts = tf.placeholder(tf.float32, shape=(None, point_num, setting.extra_dim), name='fts')
labels_seg = tf.placeholder(tf.int32, shape=(None, point_num), name='labels_seg')
labels_weights = tf.placeholder(tf.float32, shape=(None, point_num), name='labels_weights')
######################################################################
# Set Inputs(points,features_sampled)
features_sampled = None
if setting.extra_dim == 1:
points = pts
features = fts
if setting.use_extra_features:
features_sampled = tf.gather_nd(features, indices=indices, name='features_sampled')
elif setting.extra_dim == 0:
points = pts
points_sampled = tf.gather_nd(points, indices=indices, name='points_sampled')
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
labels_sampled = tf.gather_nd(labels_seg, indices=indices, name='labels_sampled')
labels_weights_sampled = tf.gather_nd(labels_weights, indices=indices, name='labels_weight_sampled')
# Build net
net = model.Net(points_augmented, features_sampled, None, None, num_parts, is_training, setting)
logits, probs = net.logits, net.probs
# Define Loss Func
loss_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_sampled, logits=logits,
weights=labels_weights_sampled)
_ = tf.summary.scalar('loss/train_seg', tensor=loss_op, collections=['train'])
# for vis t1 acc
t_1_acc_op = pf.top_1_accuracy(probs, labels_sampled)
_ = tf.summary.scalar('t_1_acc/train_seg', tensor=t_1_acc_op, collections=['train'])
# for vis instance acc
t_1_acc_instance_op = pf.top_1_accuracy(probs, labels_sampled, labels_weights_sampled, 0.6)
_ = tf.summary.scalar('t_1_acc/train_seg_instance', tensor=t_1_acc_instance_op, collections=['train'])
# for vis other acc
t_1_acc_others_op = pf.top_1_accuracy(probs, labels_sampled, labels_weights_sampled, 0.6, "less")
_ = tf.summary.scalar('t_1_acc/train_seg_others', tensor=t_1_acc_others_op, collections=['train'])
loss_val_avg = tf.placeholder(tf.float32)
_ = tf.summary.scalar('loss/val_seg', tensor=loss_val_avg, collections=['val'])
t_1_acc_val_avg = tf.placeholder(tf.float32)
_ = tf.summary.scalar('t_1_acc/val_seg', tensor=t_1_acc_val_avg, collections=['val'])
t_1_acc_val_instance_avg = tf.placeholder(tf.float32)
_ = tf.summary.scalar('t_1_acc/val_seg_instance', tensor=t_1_acc_val_instance_avg, collections=['val'])
t_1_acc_val_others_avg = tf.placeholder(tf.float32)
_ = tf.summary.scalar('t_1_acc/val_seg_others', tensor=t_1_acc_val_others_avg, collections=['val'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
# lr decay
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base, global_step, setting.decay_steps,
setting.decay_rate, staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate', tensor=lr_clip_op, collections=['train'])
# Optimizer
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op, epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op, momentum=0.9, use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss_op + reg_loss, global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
# backup this file, model and setting
if not os.path.exists(os.path.join(root_folder, args.model)):
os.makedirs(os.path.join(root_folder, args.model))
shutil.copy(__file__, os.path.join(root_folder, os.path.basename(__file__)))
shutil.copy(os.path.join(os.path.dirname(__file__), args.model + '.py'),
os.path.join(root_folder, args.model + '.py'))
shutil.copy(os.path.join(os.path.dirname(__file__), args.model.split("_")[0] + "_kitti" + '.py'),
os.path.join(root_folder, args.model.split("_")[0] + "_kitti" + '.py'))
shutil.copy(os.path.join(setting_path, args.setting + '.py'),
os.path.join(root_folder, args.model, args.setting + '.py'))
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum([np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
# Session Run
with tf.Session() as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(datetime.now(), args.load_ckpt))
for batch_idx in range(batch_num):
if (batch_idx != 0 and batch_idx % step_val == 0) or batch_idx == batch_num - 1:
######################################################################
# Validation
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
print('{}-Checkpoint saved to {}!'.format(datetime.now(), filename_ckpt))
losses_val = []
t_1_accs = []
t_1_accs_instance = []
t_1_accs_others = []
for batch_val_idx in range(math.ceil(num_val / batch_size)):
start_idx = batch_size * batch_val_idx
end_idx = min(start_idx + batch_size, num_val)
batch_size_val = end_idx - start_idx
index_length_val_batch = index_length_val[start_idx:end_idx]
points_batch = np.zeros((batch_size_val, point_num, 3), np.float32)
intensity_batch = np.zeros((batch_size_val, point_num, 1), np.float32)
points_num_batch = np.zeros(batch_size_val, np.int32)
labels_batch = np.zeros((batch_size_val, point_num), np.int32)
for i, index_length in enumerate(index_length_val_batch):
points_batch[i, 0:index_length[1], :] = \
points_ele_val[index_length[0] * 3:
index_length[0] * 3 + index_length[1] * 3].reshape(index_length[1], 3)
intensity_batch[i, 0:index_length[1], :] = \
intensities_val[index_length[0]:
index_length[0] + index_length[1]].reshape(index_length[1], 1)
points_num_batch[i] = index_length[1].astype(np.int32)
labels_batch[i, 0:index_length[1]] = \
labels_val[index_length[0]:index_length[0] + index_length[1]].astype(np.int32)
weights_batch = np.array(label_weights_list)[labels_batch]
xforms_np, rotations_np = pf.get_xforms(batch_size_val, scaling_range=scaling_range_val)
sess_op_list = [loss_op, t_1_acc_op, t_1_acc_instance_op, t_1_acc_others_op]
sess_feed_dict = {pts: points_batch,
fts: intensity_batch,
indices: pf.get_indices(batch_size_val, sample_num, points_num_batch),
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter_val]),
labels_seg: labels_batch,
labels_weights: weights_batch,
is_training: False}
loss_val, t_1_acc_val, t_1_acc_val_instance, t_1_acc_val_others = sess.run(sess_op_list,
feed_dict=sess_feed_dict)
print('{}-[Val ]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f}'.format(datetime.now(), batch_val_idx,
loss_val, t_1_acc_val))
losses_val.append(loss_val * batch_size_val)
t_1_accs.append(t_1_acc_val * batch_size_val)
t_1_accs_instance.append(t_1_acc_val_instance * batch_size_val)
t_1_accs_others.append(t_1_acc_val_others * batch_size_val)
loss_avg = sum(losses_val) / num_val
t_1_acc_avg = sum(t_1_accs) / num_val
t_1_acc_instance_avg = sum(t_1_accs_instance) / num_val
t_1_acc_others_avg = sum(t_1_accs_others) / num_val
summaries_feed_dict = {loss_val_avg: loss_avg,
t_1_acc_val_avg: t_1_acc_avg,
t_1_acc_val_instance_avg: t_1_acc_instance_avg,
t_1_acc_val_others_avg: t_1_acc_others_avg}
summaries_val = sess.run(summaries_val_op, feed_dict=summaries_feed_dict)
summary_writer.add_summary(summaries_val, batch_idx)
print('{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f}'
.format(datetime.now(), loss_avg, t_1_acc_avg))
######################################################################
######################################################################
# Training
start_idx = (batch_size * batch_idx) % num_train
end_idx = min(start_idx + batch_size, num_train)
batch_size_train = end_idx - start_idx
index_length_train_batch = index_length_train[start_idx:end_idx]
points_batch = np.zeros((batch_size_train, point_num, 3), np.float32)
intensity_batch = np.zeros((batch_size_train, point_num, 1), np.float32)
points_num_batch = np.zeros(batch_size_train, np.int32)
labels_batch = np.zeros((batch_size_train, point_num), np.int32)
for i, index_length in enumerate(index_length_train_batch):
points_batch[i, 0:index_length[1], :] = \
points_ele_train[index_length[0] * 3:
index_length[0] * 3 + index_length[1] * 3].reshape(index_length[1], 3)
intensity_batch[i, 0:index_length[1], :] = \
intensities_train[index_length[0]:
index_length[0] + index_length[1]].reshape(index_length[1], 1)
points_num_batch[i] = index_length[1].astype(np.int32)
labels_batch[i, 0:index_length[1]] = \
labels_train[index_length[0]:index_length[0] + index_length[1]].astype(np.int32)
weights_batch = np.array(label_weights_list)[labels_batch]
if start_idx + batch_size_train == num_train:
index_length_train = data_utils.index_shuffle(index_length_train)
offset = int(random.gauss(0, sample_num // 8))
offset = max(offset, -sample_num // 4)
offset = min(offset, sample_num // 4)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(batch_size_train, scaling_range=scaling_range)
sess_op_list = [train_op, loss_op, t_1_acc_op, t_1_acc_instance_op, t_1_acc_others_op, summaries_op]
sess_feed_dict = {pts: points_batch,
fts: intensity_batch,
indices: pf.get_indices(batch_size_train, sample_num_train, points_num_batch),
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter]),
labels_seg: labels_batch,
labels_weights: weights_batch,
is_training: True}
_, loss, t_1_acc, t_1_acc_instance, t_1_acc_others, summaries = sess.run(sess_op_list,
feed_dict=sess_feed_dict)
print('{}-[Train]-Iter: {:06d} Loss_seg: {:.4f} T-1 Acc: {:.4f}'
.format(datetime.now(), batch_idx, loss, t_1_acc))
summary_writer.add_summary(summaries, batch_idx)
######################################################################
print('{}-Done!'.format(datetime.now()))
def evaluate(self, data=None, export=False):
print("Running evaluation....................")
is_training = False
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0] * self.data_config.num_classes
total_correct_class = [0] * self.data_config.num_classes
points_idx = range(self.data_config.num_points)
current_step = 0
pointcloud_data = self.test_data['pointcloud_data']
labels = self.test_data['labels']
true_labels = []
all_pred_labels = []
file_size = pointcloud_data.shape[0]
num_batches = file_size / self.train_config.batch_size
for batch_idx in range(int(num_batches)):
start_idx = batch_idx * self.train_config.batch_size
end_idx = (batch_idx + 1) * self.train_config.batch_size
points_batch = pointcloud_data[start_idx:end_idx, ...]
points_batch = utils.scale_to_unit_sphere(points_batch)
label_batch = labels[start_idx:end_idx]
xforms_np, rotations_np = pf.get_xforms(
self.train_config.batch_size,
rotation_range=setting.rotation_range,
scaling_range=setting.scaling_range,
order=setting.rotation_order)
feed_dict = {
self.ops['points_pl']:
points_batch,
self.ops['labels_pl']:
label_batch,
self.ops['w_pl']:
self.gmm.weights_ if self.model == "3DmFV" else [1],
self.ops['mu_pl']:
self.gmm.means_ if self.model == "3DmFV" else [1],
self.ops['sigma_pl']:
np.sqrt(self.gmm.covariances_ if self.model ==
"3DmFV" else [1]),
self.ops['is_training_pl']:
is_training,
self.ops['xforms']:
xforms_np if self.model == "PointCNN" else [1],
self.ops['rotations']:
rotations_np if self.model == "PointCNN" else [1],
self.ops['jitter_range']:
np.array([setting.jitter])
if self.model == "PointCNN" else [1]
}
summary, _, loss_val, pred_val, probs, accuracy = self.sess.run(
[
self.ops['summary_op'], self.ops['metrics_op'],
self.ops['loss'], self.ops['predictions'],
self.ops['probabilities'], self.ops['accuracy']
],
feed_dict=feed_dict)
self.sv.summary_computed(self.sess, summary)
correct = np.sum(pred_val == label_batch)
true_labels.extend(label_batch)
all_pred_labels.extend(pred_val)
total_correct += correct
total_seen += self.train_config.batch_size
loss_sum += (loss_val * self.train_config.batch_size)
for i in range(start_idx, end_idx):
l = labels[i]
total_seen_class[l] += 1
total_correct_class[l] += (
pred_val[i - start_idx]
== l)[0] if self.model == "PointCNN" else (
pred_val[i - start_idx] == l)
current_step += 1
total_correct_class
acc = total_correct / float(total_seen)
acc_per_class = np.asarray(total_correct_class) / np.asarray(
total_seen_class)
acc_avg_cls = np.mean(
np.array(total_correct_class) /
np.array(total_seen_class, dtype=np.float))
print("current eval accuracy: ", accuracy)
print("correct ", total_correct_class)
print("seen ", total_seen_class)
print("acc per class ", acc_per_class)
print('eval mean loss: %f' % (loss_sum / float(total_seen)))
print('eval accuracy: %f' % (acc))
print('eval avg class acc: %f' % (acc_avg_cls))
if export:
true_labels = np.asarray(true_labels)
all_pred_labels = np.asarray(all_pred_labels)
label_map = []
for i, label in enumerate(self.label_map.values()):
if "_" in label:
label_split = label.split("_")
label_map.append("-".join(label_split))
else:
label_map.append(label)
label_map = np.asarray(label_map)
visualization.visualize_confusion_matrix(
true_labels,
all_pred_labels,
classes=label_map,
normalize=True,
export=True,
display=False,
filename=os.path.join(
'./log/images/',
'confusion_mat_{}_{}'.format(self.data_config.dataset_name,
self.model)),
n_classes=self.data_config.num_classes,
acc_fontsize=10,
label_fontsize=12)
visualization.visualize_histogram(
total_correct_class,
total_seen_class,
label_map,
self.model,
filepath=os.path.abspath(
'./log/images/histogram_{}_{}.pdf'.format(
self.data_config.dataset_name, self.model)))
return (acc, acc_avg_cls)
def train(self):
print("**** Start training ")
is_training = True
print('**** Training with max epoch %03d' %
(self.train_config.max_epoch))
for epoch in range(self.train_config.max_epoch):
pointcloud_data, labels, idx = provider.shuffle_data(
self.train_pointcloud_data, self.train_labels)
file_size = pointcloud_data.shape[0]
num_batches = file_size / self.train_config.batch_size
curr_step = 0
total_correct = 0
total_seen = 0
loss_sum = 0
for batch_idx in range(int(num_batches)):
start_idx = batch_idx * self.train_config.batch_size
end_idx = (batch_idx + 1) * self.train_config.batch_size
points_batch = pointcloud_data[start_idx:end_idx, ...]
if self.data_config.augment_scale:
points_batch = provider.scale_point_cloud(points_batch,
smin=0.66,
smax=1.5)
if self.data_config.augment_rotation:
points_batch = provider.rotate_point_cloud(points_batch)
if self.data_config.augment_translation:
points_batch = provider.translate_point_cloud(points_batch,
tval=0.2)
if self.data_config.augment_jitter:
points_batch = provider.jitter_point_cloud(points_batch,
sigma=0.01,
clip=0.05)
if self.data_config.augment_outlier:
points_batch = provider.insert_outliers_to_point_cloud(
points_batch, outlier_ratio=0.02)
points_batch = utils.scale_to_unit_sphere(points_batch)
label_batch = labels[start_idx:end_idx]
xforms_np, rotations_np = pf.get_xforms(
self.train_config.batch_size,
rotation_range=setting.rotation_range,
scaling_range=setting.scaling_range,
order=setting.rotation_order)
feed_dict = {
self.ops['points_pl']:
points_batch,
self.ops['labels_pl']:
label_batch,
self.ops['w_pl']:
self.gmm.weights_ if self.model == "3DmFV" else [1],
self.ops['mu_pl']:
self.gmm.means_ if self.model == "3DmFV" else [1],
self.ops['sigma_pl']:
np.sqrt(self.gmm.covariances_ if self.model ==
"3DmFV" else [1]),
self.ops['is_training_pl']:
is_training,
self.ops['xforms']:
xforms_np if self.model == "PointCNN" else [1],
self.ops['rotations']:
rotations_np if self.model == "PointCNN" else [1],
self.ops['jitter_range']:
np.array([setting.jitter] if self.model ==
"PointCNN" else [1])
}
#Log the summaries every 100 step.
if curr_step % 10 == 0 and curr_step > 0:
summary, step, gstep, _top, _mop, loss_val, pred_val = self.sess.run(
[
self.ops['summary_op'], self.ops['step'],
self.ops['global_step'], self.ops['train_op'],
self.ops['metrics_op'], self.ops['loss'],
self.ops['predictions']
],
feed_dict=feed_dict)
self.sv.summary_computed(self.sess, summary)
else:
step, gstep, _top, _mop, loss_val, pred_val = self.sess.run(
[
self.ops['step'], self.ops['global_step'],
self.ops['train_op'], self.ops['metrics_op'],
self.ops['loss'], self.ops['predictions']
],
feed_dict=feed_dict)
if curr_step % 100 == 0 or curr_step % 75 == 0:
print('global step {}: loss: {} '.format(
gstep, loss_val))
correct = np.sum(pred_val == label_batch)
total_correct += correct
total_seen += self.train_config.batch_size
loss_sum += loss_val
curr_step += 1
#evaluate
if epoch % 2 == 0 or epoch == self.train_config.max_epoch - 1:
acc, acc_avg_cls = self.evaluate()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--filelist',
'-t',
help='Path to training set ground truth (.txt)',
required=True)
parser.add_argument('--filelist_val',
'-v',
help='Path to validation set ground truth (.txt)',
required=True)
parser.add_argument('--filelist_unseen',
'-u',
help='Path to validation set ground truth (.txt)',
required=True)
parser.add_argument('--load_ckpt',
'-l',
help='Path to a check point file for load')
parser.add_argument(
'--save_folder',
'-s',
help='Path to folder for saving check points and summary',
required=True)
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting',
'-x',
help='Setting to use',
required=True)
args = parser.parse_args()
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(
args.save_folder,
'%s_%s_%s_%d' % (args.model, args.setting, time_string, os.getpid()))
if not os.path.exists(root_folder):
os.makedirs(root_folder)
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
step_val = setting.step_val
label_weights_list = setting.label_weights
label_weights_val = [1.0] * 1 + [1.0] * (setting.num_class - 1)
rotation_range = setting.rotation_range
rotation_range_val = setting.rotation_range_val
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
is_list_of_h5_list = not data_utils.is_h5_list(args.filelist)
if is_list_of_h5_list:
seg_list = data_utils.load_seg_list(args.filelist)
seg_list_idx = 0
filelist_train = seg_list[seg_list_idx]
seg_list_idx = seg_list_idx + 1
else:
filelist_train = args.filelist
data_train, _, data_num_train, label_train, _ = data_utils.load_seg(
filelist_train)
data_val, _, data_num_val, label_val, _ = data_utils.load_seg(
args.filelist_val)
data_unseen, _, data_num_unseen, label_unseen, _ = data_utils.load_seg(
args.filelist_unseen)
# shuffle
data_unseen, data_num_unseen, label_unseen = \
data_utils.grouped_shuffle([data_unseen, data_num_unseen, label_unseen])
# shuffle
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
num_train = data_train.shape[0]
point_num = data_train.shape[1]
num_val = data_val.shape[0]
num_unseen = data_unseen.shape[0]
batch_num_unseen = int(math.ceil(num_unseen / batch_size))
print('{}-{:d}/{:d} training/validation samples.'.format(
datetime.now(), num_train, num_val))
batch_num = (num_train * num_epochs + batch_size - 1) // batch_size
print('{}-{:d} training batches.'.format(datetime.now(), batch_num))
batch_num_val = int(math.ceil(num_val / batch_size))
print('{}-{:d} testing batches per test.'.format(datetime.now(),
batch_num_val))
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32,
shape=(None, 3, 3),
name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
is_labelled_data = tf.placeholder(tf.bool, name='is_labelled_data')
pts_fts = tf.placeholder(tf.float32,
shape=(None, point_num, setting.data_dim),
name='pts_fts')
labels_seg = tf.placeholder(tf.int64,
shape=(None, point_num),
name='labels_seg')
labels_weights = tf.placeholder(tf.float32,
shape=(None, point_num),
name='labels_weights')
######################################################################
pts_fts_sampled = tf.gather_nd(pts_fts,
indices=indices,
name='pts_fts_sampled')
features_augmented = None
if setting.data_dim > 3:
points_sampled, features_sampled = tf.split(
pts_fts_sampled, [3, setting.data_dim - 3],
axis=-1,
name='split_points_features')
if setting.use_extra_features:
if setting.with_normal_feature:
if setting.data_dim < 6:
print('Only 3D normals are supported!')
exit()
elif setting.data_dim == 6:
features_augmented = pf.augment(features_sampled,
rotations)
else:
normals, rest = tf.split(features_sampled,
[3, setting.data_dim - 6])
normals_augmented = pf.augment(normals, rotations)
features_augmented = tf.concat([normals_augmented, rest],
axis=-1)
else:
features_augmented = features_sampled
else:
points_sampled = pts_fts_sampled
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
labels_sampled = tf.gather_nd(labels_seg,
indices=indices,
name='labels_sampled')
labels_weights_sampled = tf.gather_nd(labels_weights,
indices=indices,
name='labels_weight_sampled')
net = model.Net(points_augmented, features_augmented, is_training, setting)
logits = net.logits
probs = tf.nn.softmax(logits, name='prob')
predictions = tf.argmax(probs, axis=-1, name='predictions')
with tf.variable_scope('xcrf_ker_weights'):
_, point_indices = pf.knn_indices_general(points_augmented,
points_augmented, 1024, True)
xcrf = learningBlock(num_points=sample_num,
num_classes=setting.num_class,
theta_alpha=float(5),
theta_beta=float(2),
theta_gamma=float(1),
num_iterations=5,
name='xcrf',
point_indices=point_indices)
_logits1 = xcrf.call(net.logits, points_augmented, features_augmented,
setting.data_dim)
_logits2 = xcrf.call(net.logits,
points_augmented,
features_augmented,
setting.data_dim,
D=2)
_logits3 = xcrf.call(net.logits,
points_augmented,
features_augmented,
setting.data_dim,
D=3)
_logits4 = xcrf.call(net.logits,
points_augmented,
features_augmented,
setting.data_dim,
D=4)
_logits5 = xcrf.call(net.logits,
points_augmented,
features_augmented,
setting.data_dim,
D=8)
_logits6 = xcrf.call(net.logits,
points_augmented,
features_augmented,
setting.data_dim,
D=16)
_logits = _logits1 + _logits2 + _logits3 + _logits4 + _logits5 + _logits6
_probs = tf.nn.softmax(_logits, name='probs_crf')
_predictions = tf.argmax(_probs, axis=-1, name='predictions_crf')
logits = tf.cond(
is_training,
lambda: tf.cond(is_labelled_data, lambda: _logits, lambda: logits),
lambda: _logits)
predictions = tf.cond(
is_training, lambda: tf.cond(is_labelled_data, lambda: _predictions,
lambda: predictions),
lambda: _predictions)
labels_sampled = tf.cond(
is_training, lambda: tf.cond(is_labelled_data, lambda: labels_sampled,
lambda: _predictions),
lambda: labels_sampled)
loss_op = tf.losses.sparse_softmax_cross_entropy(
labels=labels_sampled, logits=logits, weights=labels_weights_sampled)
with tf.name_scope('metrics'):
loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op)
t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy(
labels_sampled, predictions, weights=labels_weights_sampled)
t_1_per_class_acc_op, t_1_per_class_acc_update_op = \
tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class,
weights=labels_weights_sampled)
t_1_per_mean_iou_op, t_1_per_mean_iou_op_update_op = \
tf.metrics.mean_iou(labels_sampled, predictions, setting.num_class,
weights=labels_weights_sampled)
reset_metrics_op = tf.variables_initializer([
var for var in tf.local_variables()
if var.name.split('/')[0] == 'metrics'
])
_ = tf.summary.scalar('loss/train',
tensor=loss_mean_op,
collections=['train'])
_ = tf.summary.scalar('t_1_acc/train',
tensor=t_1_acc_op,
collections=['train'])
_ = tf.summary.scalar('t_1_per_class_acc/train',
tensor=t_1_per_class_acc_op,
collections=['train'])
_ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val',
tensor=t_1_acc_op,
collections=['val'])
_ = tf.summary.scalar('t_1_per_class_acc/val',
tensor=t_1_per_class_acc_op,
collections=['val'])
_ = tf.summary.scalar('t_1_mean_iou/val',
tensor=t_1_per_mean_iou_op,
collections=['val'])
all_variable = tf.global_variables()
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base,
global_step,
setting.decay_steps,
setting.decay_rate,
staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate',
tensor=lr_clip_op,
collections=['train'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op,
epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op,
momentum=setting.momentum,
use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
xcrf_ker_weights = [
var for var in tf.global_variables() if 'xcrf_ker_weights' in var.name
]
no_xcrf_ker_weights = [
var for var in tf.global_variables()
if 'xcrf_ker_weights' not in var.name
]
#print(restore_values)
#train_op = optimizer.minimize( loss_op+reg_loss, global_step=global_step)
with tf.control_dependencies(update_ops):
train_op_xcrf = optimizer.minimize(loss_op + reg_loss,
var_list=no_xcrf_ker_weights,
global_step=global_step)
train_op_all = optimizer.minimize(loss_op + reg_loss,
var_list=all_variable,
global_step=global_step)
train_op = tf.cond(is_labelled_data, lambda: train_op_all,
lambda: train_op_xcrf)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
saver = tf.train.Saver(var_list=no_xcrf_ker_weights, max_to_keep=5)
saver_best = tf.train.Saver(max_to_keep=5)
# backup all code
code_folder = os.path.abspath(os.path.dirname(__file__))
shutil.copytree(code_folder,
os.path.join(root_folder, os.path.basename(code_folder)))
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_ckpt_best = os.path.join(root_folder, 'ckpt-best')
if not os.path.exists(folder_ckpt_best):
os.makedirs(folder_ckpt_best)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum(
[np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(),
int(parameter_num)))
_highest_val = 0.0
max_val = 10
with tf.Session() as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_values_op = tf.summary.merge_all('summary_values')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(
datetime.now(), args.load_ckpt))
batch_num = 50000
for batch_idx_train in range(batch_num):
if (batch_idx_train % step_val == 0 and (batch_idx_train != 0 or args.load_ckpt is not None)) \
or batch_idx_train == batch_num - 1:
######################################################################
# Validation
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
print('{}-Checkpoint saved to {}!'.format(
datetime.now(), filename_ckpt))
sess.run(reset_metrics_op)
summary_hist = None
_idxVal = np.arange(num_val)
np.random.shuffle(_idxVal)
_pred = []
_label = []
for batch_val_idx in tqdm(range(batch_num_val)):
start_idx = batch_size * batch_val_idx
end_idx = min(start_idx + batch_size, num_val)
batch_size_val = end_idx - start_idx
points_batch = data_val[_idxVal[start_idx:end_idx], ...]
points_num_batch = data_num_val[_idxVal[start_idx:end_idx],
...]
labels_batch = label_val[_idxVal[start_idx:end_idx], ...]
weights_batch = np.array(label_weights_val)[labels_batch]
xforms_np, rotations_np = pf.get_xforms(
batch_size_val,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
_labels_sampled, _predictions, _, _, _, _ = sess.run(
[
labels_sampled, predictions, loss_mean_update_op,
t_1_acc_update_op, t_1_per_class_acc_update_op,
t_1_per_mean_iou_op_update_op
],
feed_dict={
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_val, sample_num,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter_val]),
labels_seg:
labels_batch,
is_labelled_data:
True,
labels_weights:
weights_batch,
is_training:
False,
})
_pred.append(_predictions.flatten())
_label.append(_labels_sampled.flatten())
loss_val, t_1_acc_val, t_1_per_class_acc_val, t1__mean_iou, summaries_val = sess.run(
[
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
t_1_per_mean_iou_op, summaries_val_op
])
img_d_summary = pf.plot_confusion_matrix(
_label,
_pred, ["Environment", "Pedestrian", "Car", "Cyclist"],
tensor_name='confusion_matrix')
max_val = max_val - 1
if (t_1_per_class_acc_val > _highest_val):
max_val = 10
_highest_val = t_1_per_class_acc_val
filename_ckpt = os.path.join(folder_ckpt_best,
str(_highest_val) + "-iter-")
saver_best.save(sess,
filename_ckpt,
global_step=global_step)
if (max_val < 0):
sys.exit(0)
summary_writer.add_summary(summaries_val, batch_idx_train)
summary_writer.add_summary(img_d_summary, batch_idx_train)
#summary_writer.add_summary(summary_hist, batch_idx_train)
print(
'{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} Diff-Best: {:.4f} T-1 mAcc: {:.4f} T-1 mIoU: {:.4f}'
.format(datetime.now(), loss_val, t_1_acc_val,
_highest_val - t_1_per_class_acc_val,
t_1_per_class_acc_val, t1__mean_iou))
sys.stdout.flush()
######################################################################
# Unseen Data
sess.run(reset_metrics_op)
summary_hist = None
_idxunseen = np.arange(num_unseen)
np.random.shuffle(_idxunseen)
_pred = []
_label = []
unseenIndices = np.arange(batch_num_unseen)
np.random.shuffle(unseenIndices)
for batch_val_idx in tqdm(unseenIndices[:500]):
start_idx = batch_size * batch_val_idx
end_idx = min(start_idx + batch_size, num_unseen)
batch_size_train = end_idx - start_idx
points_batch = data_unseen[_idxunseen[start_idx:end_idx],
...]
points_num_batch = data_num_unseen[
_idxunseen[start_idx:end_idx], ...]
labels_batch = np.zeros(points_batch.shape[0:2],
dtype=np.int32)
weights_batch = np.array(label_weights_list)[labels_batch]
xforms_np, rotations_np = pf.get_xforms(
batch_size_train,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
offset = int(
random.gauss(0,
sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
sess.run(
[
train_op, loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op,
t_1_per_mean_iou_op_update_op
],
feed_dict={
pts_fts:
points_batch,
is_labelled_data:
False,
indices:
pf.get_indices(batch_size_train, sample_num_train,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
True,
})
if batch_val_idx % 100 == 0:
loss, t_1_acc, t_1_per_class_acc, t_1__mean_iou = sess.run(
[
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
t_1_per_mean_iou_op
],
feed_dict={
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_train,
sample_num_train,
points_num_batch),
xforms:
xforms_np,
is_labelled_data:
False,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
True,
})
print(
'{}-[Train]-Unseen: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f} T-1 mIoU: {:.4f}'
.format(datetime.now(), batch_val_idx, loss,
t_1_acc, t_1_per_class_acc, t_1__mean_iou))
sys.stdout.flush()
######################################################################
# Training
start_idx = (batch_size * batch_idx_train) % num_train
end_idx = min(start_idx + batch_size, num_train)
batch_size_train = end_idx - start_idx
points_batch = data_train[start_idx:end_idx, ...]
points_num_batch = data_num_train[start_idx:end_idx, ...]
labels_batch = label_train[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[labels_batch]
if start_idx + batch_size_train == num_train:
if is_list_of_h5_list:
filelist_train_prev = seg_list[(seg_list_idx - 1) %
len(seg_list)]
filelist_train = seg_list[seg_list_idx % len(seg_list)]
if filelist_train != filelist_train_prev:
data_train, _, data_num_train, label_train, _ = data_utils.load_seg(
filelist_train)
num_train = data_train.shape[0]
seg_list_idx = seg_list_idx + 1
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
offset = int(
random.gauss(0, sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(
batch_size_train,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
sess.run(reset_metrics_op)
sess.run(
[
train_op, loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op, t_1_per_mean_iou_op_update_op
],
feed_dict={
pts_fts:
points_batch,
is_labelled_data:
True,
indices:
pf.get_indices(batch_size_train, sample_num_train,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
True,
})
if batch_idx_train % 100 == 0:
loss, t_1_acc, t_1_per_class_acc, t_1__mean_iou, summaries = sess.run(
[
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
t_1_per_mean_iou_op, summaries_op
],
feed_dict={
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_train, sample_num_train,
points_num_batch),
xforms:
xforms_np,
is_labelled_data:
True,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
True,
})
summary_writer.add_summary(summaries, batch_idx_train)
print(
'{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f} T-1 mIoU: {:.4f}'
.format(datetime.now(), batch_idx_train, loss, t_1_acc,
t_1_per_class_acc, t_1__mean_iou))
sys.stdout.flush()
######################################################################
print('{}-Done!'.format(datetime.now()))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--path', '-t', help='Path to data', required=True)
parser.add_argument('--path_val', '-v', help='Path to validation data')
parser.add_argument('--load_ckpt',
'-l',
help='Path to a check point file for load')
parser.add_argument(
'--save_folder',
'-s',
help='Path to folder for saving check points and summary',
required=True)
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting',
'-x',
help='Setting to use',
required=True)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(
args.save_folder,
'%s_%s_%s_%d' % (args.model, args.setting, time_string, os.getpid()))
if not os.path.exists(root_folder):
os.makedirs(root_folder)
sys.stdout = open(os.path.join(root_folder, 'log.txt'), 'w')
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
point_num = 2048
rotation_range = setting.rotation_range
scaling_range = setting.scaling_range
jitter = setting.jitter
pool_setting_train = None if not hasattr(
setting, 'pool_setting_train') else setting.pool_setting_train
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
sys.stdout.flush()
read_path, write_path, len = data_utils.read_path(args.path)
num_train = len
print('{}-{:d} training samples.'.format(datetime.now(), len))
sys.stdout.flush()
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32,
shape=(None, 3, 3),
name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
weight_train_placeholder = tf.placeholder(tf.float32,
shape=(batch_size),
name="weight")
### add weight
data_train_placeholder = tf.placeholder(tf.float32,
shape=(batch_size, point_num, 6),
name='data_train')
label_train_placeholder = tf.placeholder(tf.int64,
shape=(batch_size),
name='label_train')
########################################################################
batch_num_per_epoch = math.floor(num_train / batch_size)
print('{}-{:d} training batches per_epoch.'.format(datetime.now(),
batch_num_per_epoch))
sys.stdout.flush()
pts_fts_sampled = tf.gather_nd(data_train_placeholder,
indices=indices,
name='pts_fts_sampled')
features_augmented = None
if setting.data_dim > 3:
points_sampled, features_sampled = tf.split(
pts_fts_sampled, [3, setting.data_dim - 3],
axis=-1,
name='split_points_features')
if setting.use_extra_features:
if setting.with_normal_feature:
if setting.data_dim < 6:
print('Only 3D normals are supported!')
exit()
elif setting.data_dim == 6:
features_augmented = pf.augment(features_sampled,
rotations)
else:
normals, rest = tf.split(features_sampled,
[3, setting.data_dim - 6])
normals_augmented = pf.augment(normals, rotations)
features_augmented = tf.concat([normals_augmented, rest],
axis=-1)
else:
features_augmented = features_sampled
else:
points_sampled = pts_fts_sampled
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
net = model.Net(points=points_augmented,
features=features_augmented,
is_training=is_training,
setting=setting)
logits = net.logits
feature = net.fc_layers[-1]
probs = tf.nn.softmax(logits, name='probs')
predictions = tf.argmax(probs,
axis=-1,
name='predictions',
output_type=tf.int32)
predictions = tf.squeeze(predictions)
labels_2d = tf.expand_dims(label_train_placeholder,
axis=-1,
name='labels_2d')
labels_tile = tf.tile(labels_2d, (1, tf.shape(logits)[1]),
name='labels_tile')
# loss_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_tile, logits=logits)
weights_2d = tf.expand_dims(weight_train_placeholder,
axis=-1,
name='weights_2d')
loss_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_tile,
logits=logits,
weights=weights_2d)
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base,
global_step,
setting.decay_steps,
setting.decay_rate,
staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate', tensor=lr_clip_op)
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op,
epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op,
momentum=setting.momentum,
use_nesterov=True)
train_op = optimizer.minimize(loss_op + reg_loss, global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum(
[np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
sys.stdout.flush()
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(
datetime.now(), args.load_ckpt))
print('total-[Train]-Iter: ', num_epochs)
sys.stdout.flush()
cloud_features = []
for batch_idx_train in range(batch_num_per_epoch):
dataset_train = []
for i in range(batch_size):
k = batch_idx_train * batch_size + i
count = 0
data = []
with open(read_path[k]) as fpts:
while 1:
line = fpts.readline()
if not line:
break
L = line.split(' ')
L = [float(i) for i in L]
data.append(np.array(L))
count = count + 1
data = np.array(data)
trans_x = (min(data[:, 0]) + max(data[:, 0])) / 2
trans_y = (min(data[:, 1]) + max(data[:, 1])) / 2
trans_z = (min(data[:, 2]) + max(data[:, 2])) / 2
data[:, 3] = data[:, 3] / 255
data[:, 4] = data[:, 4] / 255
data[:, 5] = data[:, 5] / 255
data = data - [trans_x, trans_y, trans_z, 0.5, 0.5, 0.5]
if (count >= 2048):
index = np.random.choice(count,
size=2048,
replace=False)
# index = random.sample(range(0, count), 2048)
dataset = data[index, :]
else:
# k = random.sample(range(0, count), count)
index = np.random.choice(count,
size=2048,
replace=True)
dataset = data[index, :]
dataset_train.append(dataset)
data_batch = np.array(dataset_train)
######################################################################
# TESting
offset = int(
random.gauss(0, sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(
batch_size,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
cloud_feature = sess.run(
[feature],
feed_dict={
data_train_placeholder:
data_batch,
indices:
pf.get_indices(batch_size, sample_num_train, point_num,
pool_setting_train),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
is_training:
True,
})
cloud_feature = np.array(cloud_feature)
cloud_feature = cloud_feature.reshape((batch_size, -1))
for i in range(batch_size):
wr_index = batch_idx_train * batch_size + i
np.savetxt(write_path[wr_index],
cloud_feature[i],
fmt='%.6e',
newline=' ')
print("{} has writed".format(write_path[wr_index]))
sys.stdout.flush()
print('{}-Done!'.format(datetime.now()))
def eval_one_epoch(sess, ops, num_votes=1, topk=1):
error_cnt = 0
is_training = False
total_correct = 0
total_seen = 0
loss_sum = 0
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
fout = open(os.path.join(DUMP_DIR, 'pred_label.txt'), 'w')
if (".h5" in TEST_FILE):
current_data, current_label = data_utils.get_current_data_h5(
TEST_DATA, TEST_LABELS, NUM_POINT)
else:
current_data, current_label = data_utils.get_current_data(
TEST_DATA, TEST_LABELS, NUM_POINT)
current_label = np.squeeze(current_label)
num_batches = current_data.shape[0] // BATCH_SIZE
current_pred = []
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx + 1) * BATCH_SIZE
cur_batch_size = end_idx - start_idx
# Aggregating BEG
batch_loss_sum = 0 # sum of losses for the batch
batch_pred_sum = np.zeros(
(cur_batch_size, NUM_CLASSES)) # score for classes
batch_pred_classes = np.zeros(
(cur_batch_size, NUM_CLASSES)) # 0/1 for classes
for vote_idx in range(num_votes):
rotated_data = provider.rotate_point_cloud_by_angle(
current_data[start_idx:end_idx, :, :],
vote_idx / float(num_votes) * np.pi * 2)
xforms_np, rotations_np = pf.get_xforms(
BATCH_SIZE,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
# Augment batched point clouds by rotation and jittering
feed_dict = {
ops['pointclouds_pl']: rotated_data,
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training,
ops['xforms']: xforms_np,
ops['rotations']: rotations_np,
ops['jitter_range']: np.array([jitter_val])
}
loss_val, pred_val = sess.run([ops['loss'], ops['pred']],
feed_dict=feed_dict)
pred_val = np.sum(pred_val, axis=1)
# pred_val = np.argmax(pred_val, 1)
batch_pred_sum += pred_val
batch_pred_val = np.argmax(pred_val, 1)
for el_idx in range(cur_batch_size):
batch_pred_classes[el_idx, batch_pred_val[el_idx]] += 1
batch_loss_sum += (loss_val * cur_batch_size / float(num_votes))
# pred_val_topk = np.argsort(batch_pred_sum, axis=-1)[:,-1*np.array(range(topk))-1]
# pred_val = np.argmax(batch_pred_classes, 1)
pred_val = np.argmax(batch_pred_sum, 1)
# Aggregating END
correct = np.sum(pred_val == current_label[start_idx:end_idx])
# correct = np.sum(pred_val_topk[:,0:topk] == label_val)
total_correct += correct
total_seen += cur_batch_size
loss_sum += batch_loss_sum
for i in range(start_idx, end_idx):
l = current_label[i]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i - start_idx] == l)
fout.write('%s, %s\n' %
(SHAPE_NAMES[pred_val[i - start_idx]], SHAPE_NAMES[l]))
current_pred.append(pred_val[i - start_idx])
log_string('total seen: %d' % (total_seen))
log_string('eval mean loss: %f' % (loss_sum / float(total_seen)))
log_string('eval accuracy: %f' % (total_correct / float(total_seen)))
log_string('eval avg class acc: %f' % (np.mean(
np.array(total_correct_class) /
np.array(total_seen_class, dtype=np.float))))
class_accuracies = np.array(total_correct_class) / np.array(
total_seen_class, dtype=np.float)
for i, name in enumerate(SHAPE_NAMES):
log_string('%10s:\t%0.3f' % (name, class_accuracies[i]))
#Plot confusion matrix
current_pred = np.array(current_pred)
groundtruth = current_label.flatten()
predictions = current_pred.flatten()
mat = confusion_matrix(groundtruth, predictions)
plt.style.use('seaborn-paper')
plt.rcParams["figure.figsize"] = (10, 10)
ax = plt.subplot(111)
cmap = plt.cm.Reds
mat = mat.astype('float') / mat.sum(axis=1)[:, np.newaxis]
mat = np.nan_to_num(mat, copy=True)
plt.imshow(mat, interpolation='nearest', cmap=cmap)
# cbar = plt.colorbar(fraction=0.03, pad=0.05, aspect=30)
# cbar.ax.tick_params(labelsize=10)
tick_marks = np.arange(len(SHAPE_NAMES))
plt.xticks(tick_marks, SHAPE_NAMES, rotation=90)
plt.yticks(tick_marks, SHAPE_NAMES)
plt.ylabel('Ground truth')
plt.xlabel('Prediction')
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(36)
plt.tight_layout()
plt.savefig(os.path.join(DUMP_DIR, 'matrix.pdf'))
plt.show()
filelist_train = seg_list[seg_list_idx % len(seg_list)]
if filelist_train != filelist_train_prev:
# Load the train data and labels
data_train, _, data_num_train, label_train, _ = data_utils.load_seg(
filelist_train)
num_train = data_train.shape[0]
seg_list_idx = seg_list_idx + 1
data_train, data_num_train, label_train = data_utils.grouped_shuffle(
[data_train, data_num_train, label_train])
offset = int(random.gauss(0, sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(batch_size_train,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
sess.run(reset_metrics_op)
sess.run(
[
train_op, loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op
],
feed_dict={
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_train, sample_num_train,
points_num_batch),
xforms:
xforms_np,
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--filelist',
'-t',
help='Path to training set ground truth (.txt)',
required=True)
parser.add_argument('--filelist_val',
'-v',
help='Path to validation set ground truth (.txt)',
required=True)
parser.add_argument('--load_ckpt',
'-l',
help='Path to a check point file for load')
parser.add_argument(
'--save_folder',
'-s',
help='Path to folder for saving check points and summary',
required=True)
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting',
'-x',
help='Setting to use',
required=True)
parser.add_argument('--startpoint',
'-b',
help='Setting to use',
required=True)
args = parser.parse_args()
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(
args.save_folder,
'%s_%s_%s_%d' % (args.model, args.setting, time_string, os.getpid()))
if not os.path.exists(root_folder):
os.makedirs(root_folder)
#sys.stdout = open(os.path.join(root_folder, 'log.txt'), 'w')
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
step_val = setting.step_val
label_weights_list = setting.label_weights
label_weights_val = [1.0] * 1 + [1.0] * (setting.num_class - 1)
rotation_range = setting.rotation_range
rotation_range_val = setting.rotation_range_val
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
is_list_of_h5_list = not data_utils.is_h5_list(args.filelist)
if is_list_of_h5_list:
seg_list = data_utils.load_seg_list(args.filelist)
seg_list_idx = 0
filelist_train = seg_list[seg_list_idx]
seg_list_idx = seg_list_idx + 1
else:
filelist_train = args.filelist
#data_train, _, data_num_train, label_train, _ = data_utils.load_seg(filelist_train)
data_val, _, data_num_val, label_val, _ = data_utils.load_seg(
args.filelist_val)
data_train = data_val
data_num_train = data_num_val
label_train = label_val
# shuffle
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
num_train = data_train.shape[0]
point_num = data_train.shape[1]
num_val = data_val.shape[0]
print('{}-{:d}/{:d} training/validation samples.'.format(
datetime.now(), num_train, num_val))
batch_num = (num_train * num_epochs + batch_size - 1) // batch_size
print('{}-{:d} training batches.'.format(datetime.now(), batch_num))
batch_num_val = int(math.ceil(num_val / batch_size))
print('{}-{:d} testing batches per test.'.format(datetime.now(),
batch_num_val))
######################
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32,
shape=(None, 3, 3),
name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
pts_fts = tf.placeholder(tf.float32,
shape=(None, point_num, setting.data_dim),
name='pts_fts')
labels_seg = tf.placeholder(tf.int64,
shape=(None, point_num),
name='labels_seg')
labels_weights = tf.placeholder(tf.float32,
shape=(None, point_num),
name='labels_weights')
######################################################################
pts_fts_sampled = tf.gather_nd(pts_fts,
indices=indices,
name='pts_fts_sampled')
features_augmented = None
if setting.data_dim > 3:
points_sampled, features_sampled = tf.split(
pts_fts_sampled, [3, setting.data_dim - 3],
axis=-1,
name='split_points_features')
if setting.use_extra_features:
if setting.with_normal_feature:
if setting.data_dim < 6:
print('Only 3D normals are supported!')
exit()
elif setting.data_dim == 6:
features_augmented = pf.augment(features_sampled,
rotations)
else:
normals, rest = tf.split(features_sampled,
[3, setting.data_dim - 6])
normals_augmented = pf.augment(normals, rotations)
features_augmented = tf.concat([normals_augmented, rest],
axis=-1)
else:
features_augmented = features_sampled
else:
points_sampled = pts_fts_sampled
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
labels_sampled = tf.gather_nd(labels_seg,
indices=indices,
name='labels_sampled')
labels_weights_sampled = tf.gather_nd(labels_weights,
indices=indices,
name='labels_weight_sampled')
net = model.Net(points_augmented, features_augmented, is_training, setting)
logits = net.logits
probs = tf.nn.softmax(logits, name='probs')
predictions = tf.argmax(probs, axis=-1, name='predictions')
loss_op = tf.losses.sparse_softmax_cross_entropy(
labels=labels_sampled, logits=logits, weights=labels_weights_sampled)
with tf.name_scope('metrics'):
loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op)
t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy(
labels_sampled, predictions, weights=labels_weights_sampled)
t_1_per_class_acc_op, t_1_per_class_acc_update_op = \
tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class,
weights=labels_weights_sampled)
t_1_per_mean_iou_op, t_1_per_mean_iou_op_update_op = \
tf.metrics.mean_iou(labels_sampled, predictions, setting.num_class,
weights=labels_weights_sampled)
reset_metrics_op = tf.variables_initializer([
var for var in tf.local_variables()
if var.name.split('/')[0] == 'metrics'
])
_ = tf.summary.scalar('loss/train',
tensor=loss_mean_op,
collections=['train'])
_ = tf.summary.scalar('t_1_acc/train',
tensor=t_1_acc_op,
collections=['train'])
_ = tf.summary.scalar('t_1_per_class_acc/train',
tensor=t_1_per_class_acc_op,
collections=['train'])
_ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val',
tensor=t_1_acc_op,
collections=['val'])
_ = tf.summary.scalar('t_1_per_class_acc/val',
tensor=t_1_per_class_acc_op,
collections=['val'])
_ = tf.summary.scalar('t_1_mean_iou/val',
tensor=t_1_per_mean_iou_op,
collections=['val'])
#_ = tf.summary.histogram('summary/Add_F2', Add_F2, collections=['summary_values'])
#_ = tf.summary.histogram('summary/Add_F3', Add_F3, collections=['summary_values'])
#_ = tf.summary.histogram('summary/Z', Z, collections=['summary_values'])
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base,
global_step,
setting.decay_steps,
setting.decay_rate,
staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate',
tensor=lr_clip_op,
collections=['train'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op,
epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op,
momentum=setting.momentum,
use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss_op + reg_loss,
global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=5)
saver_best = tf.train.Saver(max_to_keep=5)
# backup all code
code_folder = os.path.abspath(os.path.dirname(__file__))
shutil.copytree(code_folder,
os.path.join(root_folder, os.path.basename(code_folder)))
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_ckpt_best = os.path.join(root_folder, 'ckpt-best')
if not os.path.exists(folder_ckpt_best):
os.makedirs(folder_ckpt_best)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
folder_cm_matrix = os.path.join(root_folder, 'cm-matrix')
if not os.path.exists(folder_cm_matrix):
os.makedirs(folder_cm_matrix)
parameter_num = np.sum(
[np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
_highest_val = 0.0
max_val = 10
with tf.Session() as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_values_op = tf.summary.merge_all('summary_values')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
#img_d_summary_writer = tf.summary.FileWriter(folder_cm_matrix, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(
datetime.now(), args.load_ckpt))
batch_num = 1
batch_idx_train = 1
######################################################################
# Validation
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
print('{}-Checkpoint saved to {}!'.format(datetime.now(),
filename_ckpt))
sess.run(reset_metrics_op)
summary_hist = None
_idxVal = np.arange(num_val)
np.random.shuffle(_idxVal)
_dataX = []
_dataY = []
_dataZ = []
_dataD = []
_pred = []
_label = []
for batch_val_idx in tqdm(range(batch_num_val)):
start_idx = batch_size * batch_val_idx
end_idx = min(start_idx + batch_size, num_val)
batch_size_val = end_idx - start_idx
points_batch = data_val[_idxVal[start_idx:end_idx], ...]
points_num_batch = data_num_val[_idxVal[start_idx:end_idx], ...]
labels_batch = label_val[_idxVal[start_idx:end_idx], ...]
weights_batch = np.array(label_weights_val)[labels_batch]
xforms_np, rotations_np = pf.get_xforms(
batch_size_val,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
_labels_sampled, _predictions, _, _, _, _ = sess.run(
[
labels_sampled, predictions, loss_mean_update_op,
t_1_acc_update_op, t_1_per_class_acc_update_op,
t_1_per_mean_iou_op_update_op
],
feed_dict={
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_val, sample_num,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter_val]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
False,
})
_dataX.append(points_batch[:, :, 0].flatten())
_dataY.append(points_batch[:, :, 1].flatten())
_dataZ.append(points_batch[:, :, 2].flatten())
_pred.append(_predictions.flatten())
_label.append(_labels_sampled.flatten())
loss_val, t_1_acc_val, t_1_per_class_acc_val, t1__mean_iou, summaries_val = sess.run(
[
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
t_1_per_mean_iou_op, summaries_val_op
])
img_d_summary = pf.plot_confusion_matrix(
_label,
_pred, ["Environment", "Pedestrian", "Car", "Cyclist"],
tensor_name='confusion_matrix',
normalize=False)
_dataX = np.concatenate(_dataX, axis=0).flatten()
_dataY = np.concatenate(_dataY, axis=0).flatten()
_dataZ = np.concatenate(_dataZ, axis=0).flatten()
correct_labels = np.concatenate(_label, axis=0).flatten()
predict_labels = np.concatenate(_pred, axis=0).flatten()
filename_pred = args.filelist_val + '_cm.h5'
print('{}-Saving {}...'.format(datetime.now(), filename_pred))
file = h5py.File(filename_pred, 'w')
file.create_dataset('dataX', data=_dataX)
file.create_dataset('dataY', data=_dataY)
file.create_dataset('dataZ', data=_dataZ)
file.create_dataset('correct_labels', data=correct_labels)
file.create_dataset('predict_labels', data=predict_labels)
file.close()
summary_writer.add_summary(img_d_summary, batch_idx_train)
summary_writer.add_summary(summaries_val, batch_idx_train)
y_test = correct_labels
prediction = predict_labels
print('Accuracy:', accuracy_score(y_test, prediction))
print('F1 score:', f1_score(y_test, prediction, average=None))
print('Recall:', recall_score(y_test, prediction, average=None))
print('Precision:', precision_score(y_test, prediction, average=None))
print('\n clasification report:\n',
classification_report(y_test, prediction))
print('\n confussion matrix:\n', confusion_matrix(y_test, prediction))
print(
'{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} Diff-Best: {:.4f} T-1 mAcc: {:.4f} T-1 mIoU: {:.4f}'
.format(datetime.now(), loss_val, t_1_acc_val,
_highest_val - t_1_per_class_acc_val,
t_1_per_class_acc_val, t1__mean_iou))
sys.stdout.flush()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--path', '-t', help='Path to data', required=True)
parser.add_argument('--path_val', '-v', help='Path to validation data')
parser.add_argument('--load_ckpt',
'-l',
help='Path to a check point file for load')
parser.add_argument(
'--save_folder',
'-s',
help='Path to folder for saving check points and summary',
required=True)
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting',
'-x',
help='Setting to use',
required=True)
parser.add_argument('--gpu', '-gpu', help='Setting to use', required='0')
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(
args.save_folder,
'%s_%s_%s_%d' % (args.model, args.setting, time_string, os.getpid()))
if not os.path.exists(root_folder):
os.makedirs(root_folder)
sys.stdout = open(os.path.join(root_folder, 'log.txt'), 'w')
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
point_num = 2048
rotation_range = setting.rotation_range
scaling_range = setting.scaling_range
jitter = setting.jitter
pool_setting_train = None if not hasattr(
setting, 'pool_setting_train') else setting.pool_setting_train
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
sys.stdout.flush()
data_train, label_train, weight_train, box_sizes, len = data_utils.load_file(
args.path)
num_train = len
print('{}-{:d} training samples.'.format(datetime.now(), len))
sys.stdout.flush()
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32,
shape=(None, 3, 3),
name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
weight_train_placeholder = tf.placeholder(tf.float32,
shape=(batch_size),
name="weight")
### add weight
data_train_placeholder = tf.placeholder(tf.float32,
shape=(batch_size, point_num, 6),
name='data_train')
label_train_placeholder = tf.placeholder(tf.int64,
shape=(batch_size),
name='label_train')
size_train_placeholder = tf.placeholder(tf.float32,
shape=(batch_size, 1, 3),
name="weight")
########################################################################
batch_num_per_epoch = math.floor(num_train / batch_size)
print('{}-{:d} training batches per_epoch.'.format(datetime.now(),
batch_num_per_epoch))
sys.stdout.flush()
pts_fts_sampled = tf.gather_nd(data_train_placeholder,
indices=indices,
name='pts_fts_sampled')
features_augmented = None
if setting.data_dim > 3:
points_sampled, features_sampled = tf.split(
pts_fts_sampled, [3, setting.data_dim - 3],
axis=-1,
name='split_points_features')
if setting.use_extra_features:
if setting.with_normal_feature:
if setting.data_dim < 6:
print('Only 3D normals are supported!')
exit()
elif setting.data_dim == 6:
features_augmented = pf.augment(features_sampled,
rotations)
else:
normals, rest = tf.split(features_sampled,
[3, setting.data_dim - 6])
normals_augmented = pf.augment(normals, rotations)
features_augmented = tf.concat([normals_augmented, rest],
axis=-1)
else:
features_augmented = features_sampled
else:
points_sampled = pts_fts_sampled
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
net = model.Net(points=points_augmented,
features=features_augmented,
is_training=is_training,
setting=setting)
#logits = net.logits
feature = net.fc_layers[-1]
####
box_size = size_train_placeholder
#box_size = tf.expand_dims(size_train_placeholder, axis=1, name='box_size')
box_feature = tf.layers.dense(inputs=box_size, units=20)
feature_concat = tf.concat((feature, box_feature), 2)
output = tf.layers.dense(inputs=feature_concat, units=256)
logits = tf.layers.dense(inputs=output, units=100)
####
probs = tf.nn.softmax(logits, name='probs')
predictions = tf.argmax(probs,
axis=-1,
name='predictions',
output_type=tf.int32)
predictions = tf.squeeze(predictions)
labels_2d = tf.expand_dims(label_train_placeholder,
axis=-1,
name='labels_2d')
labels_tile = tf.tile(labels_2d, (1, tf.shape(logits)[1]),
name='labels_tile')
# loss_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_tile, logits=logits)
weights_2d = tf.expand_dims(weight_train_placeholder,
axis=-1,
name='weights_2d')
loss_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_tile,
logits=logits,
weights=weights_2d)
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base,
global_step,
setting.decay_steps,
setting.decay_rate,
staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate', tensor=lr_clip_op)
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op,
epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op,
momentum=setting.momentum,
use_nesterov=True)
train_op = optimizer.minimize(loss_op + reg_loss, global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum(
[np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
sys.stdout.flush()
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(
datetime.now(), args.load_ckpt))
print('total-[Train]-Iter: ', num_epochs)
sys.stdout.flush()
num_epochs = 1 # test mode
dataset = 'ScanNet'
if dataset == 'S3DIS':
categories = [6, 8, 9, 14, 99] # chair,board,table,sofa
elif dataset == 'Matterport':
categories = [3, 5, 7, 8, 11, 15, 18, 22, 25, 28]
elif dataset == 'ScanNet':
categories = [
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39
]
categories = np.array(categories)
TP = np.zeros(categories.shape[0])
FP = np.zeros(categories.shape[0])
FN = np.zeros(categories.shape[0])
TN = np.zeros(categories.shape[0])
recall = np.zeros(categories.shape[0])
precision = np.zeros(categories.shape[0])
for epoch_idx_train in range(num_epochs):
print('xxxx')
total_correct = 0
total_seen = 0
loss_sum = 0
if epoch_idx_train == num_epochs - 1:
confidences = []
cloud_features = []
for batch_idx_train in range(batch_num_per_epoch):
print('batch_idx_train', batch_idx_train)
index_ch = np.arange(len)
# do not shuttle
label = []
weight = []
size = []
dataset_train = []
for i in range(batch_size):
#print('i',i)
k = batch_idx_train * batch_size + i
label.append(label_train[index_ch[k]])
weight.append(weight_train[index_ch[k]])
size.append(box_sizes[index_ch[k]])
data = []
count = 0
with open(data_train[index_ch[k]]) as fpts:
while 1:
line = fpts.readline()
if not line:
break
L = line.split(' ')
L = [float(i) for i in L]
data.append(np.array(L))
count = count + 1
data = np.array(data)
data = data[:, :6]
trans_x = (min(data[:, 0]) + max(data[:, 0])) / 2
trans_y = (min(data[:, 1]) + max(data[:, 1])) / 2
trans_z = (min(data[:, 2]) + max(data[:, 2])) / 2
data = data - [
trans_x, trans_y, trans_z, 0.5, 0.5, 0.5
]
if (count >= 2048):
index = np.random.choice(count,
size=2048,
replace=False)
# index = random.sample(range(0, count), 2048)
dataset = data[index, :]
else:
# k = random.sample(range(0, count), count)
index = np.random.choice(count,
size=2048,
replace=True)
dataset = data[index, :]
dataset_train.append(dataset)
data_batch = np.array(dataset_train)
label_batch = np.array(label)
weight_batch = np.array(weight)
size_batch = np.array(size)
######################################################################
# TESting
offset = int(
random.gauss(0,
sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(
batch_size,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
loss, prediction, confidence, cloud_feature = sess.run(
[loss_op, predictions, probs, feature],
feed_dict={
data_train_placeholder:
data_batch,
label_train_placeholder:
label_batch,
indices:
pf.get_indices(batch_size, sample_num_train,
point_num, pool_setting_train),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
is_training:
True,
weight_train_placeholder:
weight_batch,
size_train_placeholder:
size_batch,
})
print('confidence.shape', confidence.shape)
confidences.append(confidence)
cloud_features.append(cloud_feature)
correct = np.sum(prediction == label_batch)
total_correct += correct
total_seen += batch_size
loss_sum += loss
for i in range(categories.shape[0]):
for j in range(label_batch.shape[0]):
pred = prediction[j]
label = label_batch[j]
cat = categories[i]
if label == cat and pred == cat:
TP[i] += 1
elif label == cat and pred != cat:
FN[i] += 1
elif label != cat and pred == cat:
FP[i] += 1
elif label != cat and pred != cat:
TN[i] += 1
for i in range(categories.shape[0]):
recall[i] = TP[i] / (TP[i] + FN[i])
precision[i] = TP[i] / (TP[i] + FP[i])
print('precision', precision)
print('recall', recall)
for i in range(categories.shape[0]):
recall[i] = TP[i] / (TP[i] + FN[i])
precision[i] = TP[i] / (TP[i] + FP[i])
print('precision', precision)
print('recall', recall)
confidences = np.array(confidences).reshape((-1, 101))
cloud_features = np.array(cloud_features)
cloud_features = cloud_features.reshape(
(-1, cloud_features.shape[-1]))
# class num :101
np.savetxt(os.path.join(folder_summary, 'confidence.txt'),
confidences)
np.savetxt(os.path.join(folder_summary, 'feature.txt'),
cloud_features)
print('confidences and features saved to {}!'.format(
folder_summary))
print('confidences shape is {}!'.format(confidences.shape))
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
print('{}-Checkpoint saved to {}!'.format(
datetime.now(), filename_ckpt))
print(
'{}-[test]-done: {:06d} Loss: {:.4f} Acc: {:.4f} lr:{:.4f}'
.format(datetime.now(), epoch_idx_train, loss_sum,
(total_correct / float(total_seen)), learningrate))
sys.stdout.flush()
else:
for batch_idx_train in range(batch_num_per_epoch):
########################################################################
#sample
index_ch = np.arange(len)
np.random.shuffle(index_ch)
label = []
weight = []
dataset_train = []
size = []
for i in range(batch_size):
k = batch_idx_train * batch_size + i
label.append(label_train[index_ch[k]])
#weight.append(pow(weight_train[index_ch[k]], 2))
weight.append(weight_train[index_ch[k]])
size.append(box_sizes[index_ch[k]])
data = []
count = 0
with open(data_train[index_ch[k]]) as fpts:
while 1:
line = fpts.readline()
if not line:
break
L = line.split(' ')
L = [float(i) for i in L]
data.append(np.array(L))
count = count + 1
data = np.array(data)
data = data[:, :6]
trans_x = (min(data[:, 0]) + max(data[:, 0])) / 2
trans_y = (min(data[:, 1]) + max(data[:, 1])) / 2
trans_z = (min(data[:, 2]) + max(data[:, 2])) / 2
data = data - [
trans_x, trans_y, trans_z, 0.5, 0.5, 0.5
]
######################################
if (count >= 2048):
index = np.random.choice(count,
size=2048,
replace=False)
dataset = data[index, :]
else:
# k = random.sample(range(0, count), count)
index = np.random.choice(count,
size=2048,
replace=True)
dataset = data[index, :]
dataset_train.append(dataset)
data_batch = np.array(dataset_train)
label_batch = np.array(label)
weight_batch = np.array(weight)
size_batch = np.array(size)
######################################################################
# Training
offset = int(
random.gauss(0,
sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(
batch_size,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
_, loss, prediction, learningrate, bs, bf = sess.run(
[
train_op, loss_op, predictions, lr_clip_op,
box_size, box_feature
],
feed_dict={
data_train_placeholder:
data_batch,
label_train_placeholder:
label_batch,
indices:
pf.get_indices(batch_size, sample_num_train,
point_num, pool_setting_train),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
is_training:
True,
weight_train_placeholder:
weight_batch,
size_train_placeholder:
size_batch,
})
correct = np.sum(prediction == label_batch)
total_correct += correct
total_seen += batch_size
loss_sum += loss
if batch_idx_train % 50 == 0 or 1:
print(
'{}-[Train]-Iter:{:06d} batch_idx:{:06d} Loss: {:.4f} Acc: {:.4f} lr:{:.4f}'
.format(datetime.now(), epoch_idx_train,
batch_idx_train, loss,
(total_correct / float(total_seen)),
learningrate))
sys.stdout.flush()
print(
'{}-[Train]-Iter: {:06d} Loss: {:.4f} Acc: {:.4f} lr:{:.4f}'
.format(datetime.now(), epoch_idx_train, loss_sum,
(total_correct / float(total_seen)), learningrate))
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
print('{}-Checkpoint saved to {}!'.format(
datetime.now(), filename_ckpt))
sys.stdout.flush()
####################################################################
print('{}-Done!'.format(datetime.now()))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--load_ckpt', '-l', help='Path to a check point file for load')
parser.add_argument('--save_folder', '-s', default='log/seg', help='Path to folder for saving check points and summary')
parser.add_argument('--model', '-m', default='shellconv', help='Model to use')
parser.add_argument('--setting', '-x', default='seg_s3dis', help='Setting to use')
parser.add_argument('--log', help='Log to FILE in save folder; use - for stdout (default is log.txt)', metavar='FILE', default='log.txt')
args = parser.parse_args()
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(args.save_folder, '%s_%s_%s' % (args.model, args.setting, time_string))
if not os.path.exists(root_folder):
os.makedirs(root_folder)
global LOG_FOUT
if args.log != '-':
LOG_FOUT = open(os.path.join(root_folder, args.log), 'w')
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
step_val = setting.step_val
label_weights_list = setting.label_weights
rotation_range = setting.rotation_range
rotation_range_val = setting.rotation_range_val
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
is_list_of_h5_list = data_utils.is_h5_list(setting.filelist)
if is_list_of_h5_list:
seg_list = [setting.filelist] # for train
else:
seg_list = data_utils.load_seg_list(setting.filelist) # for train
data_val, _, data_num_val, label_val, _ = data_utils.load_seg(setting.filelist_val)
if data_val.shape[-1] > 3:
data_val = data_val[:,:,:3] # only use the xyz coordinates
point_num = data_val.shape[1]
num_val = data_val.shape[0]
batch_num_val = num_val // batch_size
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, sample_num, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
pts_fts = tf.placeholder(tf.float32, shape=(None, point_num, setting.data_dim), name='pts_fts')
labels_seg = tf.placeholder(tf.int64, shape=(None, point_num), name='labels_seg')
labels_weights = tf.placeholder(tf.float32, shape=(None, point_num), name='labels_weights')
######################################################################
points_sampled = tf.gather_nd(pts_fts, indices=indices, name='pts_fts_sampled')
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
labels_sampled = tf.gather_nd(labels_seg, indices=indices, name='labels_sampled')
labels_weights_sampled = tf.gather_nd(labels_weights, indices=indices, name='labels_weight_sampled')
bn_decay_exp_op = tf.train.exponential_decay(0.5, global_step, setting.decay_steps,
0.5, staircase=True)
bn_decay_op = tf.minimum(0.99, 1 - bn_decay_exp_op)
logits_op = model.get_model(points_augmented, is_training, setting.sconv_params, setting.sdconv_params, setting.fc_params,
sampling=setting.sampling,
weight_decay=setting.weight_decay,
bn_decay = bn_decay_op,
part_num=setting.num_class)
predictions = tf.argmax(logits_op, axis=-1, name='predictions')
loss_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_sampled, logits=logits_op,
weights=labels_weights_sampled)
with tf.name_scope('metrics'):
loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op)
t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy(labels_sampled, predictions, weights=labels_weights_sampled)
t_1_per_class_acc_op, t_1_per_class_acc_update_op = \
tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class,
weights=labels_weights_sampled)
reset_metrics_op = tf.variables_initializer([var for var in tf.local_variables()
if var.name.split('/')[0] == 'metrics'])
_ = tf.summary.scalar('loss/train', tensor=loss_mean_op, collections=['train'])
_ = tf.summary.scalar('t_1_acc/train', tensor=t_1_acc_op, collections=['train'])
_ = tf.summary.scalar('t_1_per_class_acc/train', tensor=t_1_per_class_acc_op, collections=['train'])
_ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val', tensor=t_1_acc_op, collections=['val'])
_ = tf.summary.scalar('t_1_per_class_acc/val', tensor=t_1_per_class_acc_op, collections=['val'])
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base, global_step, setting.decay_steps,
setting.decay_rate, staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate', tensor=lr_clip_op, collections=['train'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op, momentum=setting.momentum, use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss_op + reg_loss, global_step=global_step)
saver = tf.train.Saver(max_to_keep=None)
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum([np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
with tf.Session(config=config) as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(tf.global_variables_initializer())
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(datetime.now(), args.load_ckpt))
else:
latest_ckpt = tf.train.latest_checkpoint(folder_ckpt)
if latest_ckpt:
print('{}-Found checkpoint {}'.format(datetime.now(), latest_ckpt))
saver.restore(sess, latest_ckpt)
print('{}-Checkpoint loaded from {} (Iter {})'.format(
datetime.now(), latest_ckpt, sess.run(global_step)))
best_acc = 0
best_epoch = 0
for epoch in range(num_epochs):
############################### train #######################################
# Shuffle train files
np.random.shuffle(seg_list)
for file_idx_train in range(len(seg_list)):
print('----epoch:'+str(epoch) + '--train file:' + str(file_idx_train) + '-----')
filelist_train = seg_list[file_idx_train]
data_train, _, data_num_train, label_train, _ = data_utils.load_seg(filelist_train)
num_train = data_train.shape[0]
if data_train.shape[-1] > 3:
data_train = data_train[:,:,:3]
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
# data_train, label_train, _ = provider.shuffle_data_seg(data_train, label_train)
batch_num = (num_train + batch_size - 1) // batch_size
for batch_idx_train in range(batch_num):
# Training
start_idx = (batch_size * batch_idx_train) % num_train
end_idx = min(start_idx + batch_size, num_train)
batch_size_train = end_idx - start_idx
points_batch = data_train[start_idx:end_idx, ...]
points_num_batch = data_num_train[start_idx:end_idx, ...]
labels_batch = label_train[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[labels_batch]
offset = int(random.gauss(0, sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(batch_size_train,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
sess.run(reset_metrics_op)
sess.run([train_op, loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op],
feed_dict={
pts_fts: points_batch,
indices: pf.get_indices(batch_size_train, sample_num_train, points_num_batch),
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter]),
labels_seg: labels_batch,
labels_weights: weights_batch,
is_training: True,
})
loss, t_1_acc, t_1_per_class_acc, summaries, step = sess.run([loss_mean_op,
t_1_acc_op,
t_1_per_class_acc_op,
summaries_op,
global_step])
summary_writer.add_summary(summaries, step)
log_string('{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}'
.format(datetime.now(), step, loss, t_1_acc, t_1_per_class_acc))
sys.stdout.flush()
######################################################################
filename_ckpt = os.path.join(folder_ckpt, 'epoch')
saver.save(sess, filename_ckpt, global_step=epoch)
print('{}-Checkpoint saved to {}!'.format(datetime.now(), filename_ckpt))
sess.run(reset_metrics_op)
for batch_val_idx in range(batch_num_val):
start_idx = batch_size * batch_val_idx
end_idx = min(start_idx + batch_size, num_val)
batch_size_val = end_idx - start_idx
points_batch = data_val[start_idx:end_idx, ...]
points_num_batch = data_num_val[start_idx:end_idx, ...]
labels_batch = label_val[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[labels_batch]
xforms_np, rotations_np = pf.get_xforms(batch_size_val,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
sess.run([loss_mean_update_op, t_1_acc_update_op, t_1_per_class_acc_update_op],
feed_dict={
pts_fts: points_batch,
indices: pf.get_indices(batch_size_val, sample_num, points_num_batch),
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter_val]),
labels_seg: labels_batch,
labels_weights: weights_batch,
is_training: False,
})
loss_val, t_1_acc_val, t_1_per_class_acc_val, summaries_val, step = sess.run(
[loss_mean_op, t_1_acc_op, t_1_per_class_acc_op, summaries_val_op, global_step])
summary_writer.add_summary(summaries_val, step)
if t_1_per_class_acc_val > best_acc:
best_acc = t_1_per_class_acc_val
best_epoch = epoch
log_string('{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f} best epoch: {} Current epoch: {}'
.format(datetime.now(), loss_val, t_1_acc_val, t_1_per_class_acc_val, best_epoch, epoch))
sys.stdout.flush()
######################################################################
print('{}-Done!'.format(datetime.now()))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--filelist',
'-t',
help='Path to training set ground truth (.txt)',
required=True)
parser.add_argument('--filelist_val',
'-v',
help='Path to validation set ground truth (.txt)',
required=True)
parser.add_argument('--load_ckpt',
'-l',
help='Path to a check point file for load')
parser.add_argument(
'--save_folder',
'-s',
help='Path to folder for saving check points and summary',
required=True)
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting',
'-x',
help='Setting to use',
required=True)
parser.add_argument(
'--epochs',
help='Number of training epochs (default defined in setting)',
type=int)
parser.add_argument('--batch_size',
help='Batch size (default defined in setting)',
type=int)
parser.add_argument(
'--log',
help=
'Log to FILE in save folder; use - for stdout (default is log.txt)',
metavar='FILE',
default='log.txt')
parser.add_argument('--no_timestamp_folder',
help='Dont save to timestamp folder',
action='store_true')
parser.add_argument('--no_code_backup',
help='Dont backup code',
action='store_true')
args = parser.parse_args()
if not args.no_timestamp_folder:
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(
args.save_folder, '%s_%s_%s_%d' %
(args.model, args.setting, time_string, os.getpid()))
else:
root_folder = args.save_folder
if not os.path.exists(root_folder):
os.makedirs(root_folder)
if args.log != '-':
sys.stdout = open(os.path.join(root_folder, args.log), 'w')
#print('PID:', os.getpid())
#print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = args.epochs or setting.num_epochs
batch_size = args.batch_size or setting.batch_size
sample_num = setting.sample_num
step_val = setting.step_val
label_weights_list = setting.label_weights
rotation_range = setting.rotation_range
rotation_range_val = setting.rotation_range_val
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
# Prepare inputs
#print('{}-Preparing datasets...'.format(datetime.now()))
is_list_of_h5_list = not data_utils.is_h5_list(args.filelist)
if is_list_of_h5_list:
seg_list = data_utils.load_seg_list(args.filelist)
seg_list_idx = 0
filelist_train = seg_list[seg_list_idx]
seg_list_idx = seg_list_idx + 1
else:
filelist_train = args.filelist
data_train, _, data_num_train, label_train, _ = data_utils.load_seg(
filelist_train)
data_val, _, data_num_val, label_val, _ = data_utils.load_seg(
args.filelist_val)
# shuffle
# data_num_train is the number of points in each point cloud
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
num_train = data_train.shape[0]
point_num = data_train.shape[1]
num_val = data_val.shape[0]
#print('{}-{:d}/{:d} training/validation samples.'.format(datetime.now(), num_train, num_val))
batch_num = (num_train * num_epochs + batch_size - 1) // batch_size
#print('{}-{:d} training batches.'.format(datetime.now(), batch_num))
batch_num_val = math.ceil(num_val / batch_size)
#print('{}-{:d} testing batches per test.'.format(datetime.now(), batch_num_val))
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32,
shape=(None, 3, 3),
name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
pts_fts = tf.placeholder(tf.float32,
shape=(None, point_num, setting.data_dim),
name='pts_fts')
labels_seg = tf.placeholder(tf.int64,
shape=(None, point_num),
name='labels_seg')
labels_weights = tf.placeholder(tf.float32,
shape=(None, point_num),
name='labels_weights')
######################################################################
pts_fts_sampled = tf.gather_nd(pts_fts,
indices=indices,
name='pts_fts_sampled')
features_augmented = None
if setting.data_dim > 3:
points_sampled, features_sampled = tf.split(
pts_fts_sampled, [3, setting.data_dim - 3],
axis=-1,
name='split_points_features')
if setting.use_extra_features:
if setting.with_normal_feature:
if setting.data_dim < 6:
print('Only 3D normals are supported!')
exit()
elif setting.data_dim == 6:
features_augmented = pf.augment(features_sampled,
rotations)
else:
normals, rest = tf.split(features_sampled,
[3, setting.data_dim - 6])
normals_augmented = pf.augment(normals, rotations)
features_augmented = tf.concat([normals_augmented, rest],
axis=-1)
else:
features_augmented = features_sampled
else:
points_sampled = pts_fts_sampled
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
labels_sampled = tf.gather_nd(labels_seg,
indices=indices,
name='labels_sampled')
labels_weights_sampled = tf.gather_nd(labels_weights,
indices=indices,
name='labels_weight_sampled')
net = model.Net(points_augmented, features_augmented, is_training, setting)
logits = net.logits
probs = tf.nn.softmax(logits, name='probs')
predictions = tf.argmax(probs, axis=-1, name='predictions')
loss_op = tf.losses.sparse_softmax_cross_entropy(
labels=labels_sampled, logits=logits, weights=labels_weights_sampled)
with tf.name_scope('metrics'):
loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op)
t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy(
labels_sampled, predictions, weights=labels_weights_sampled)
t_1_per_class_acc_op, t_1_per_class_acc_update_op = \
tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class,
weights=labels_weights_sampled)
reset_metrics_op = tf.variables_initializer([
var for var in tf.local_variables()
if var.name.split('/')[0] == 'metrics'
])
_ = tf.summary.scalar('loss/train',
tensor=loss_mean_op,
collections=['train'])
_ = tf.summary.scalar('t_1_acc/train',
tensor=t_1_acc_op,
collections=['train'])
_ = tf.summary.scalar('t_1_per_class_acc/train',
tensor=t_1_per_class_acc_op,
collections=['train'])
_ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val',
tensor=t_1_acc_op,
collections=['val'])
_ = tf.summary.scalar('t_1_per_class_acc/val',
tensor=t_1_per_class_acc_op,
collections=['val'])
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base,
global_step,
setting.decay_steps,
setting.decay_rate,
staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate',
tensor=lr_clip_op,
collections=['train'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op,
epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op,
momentum=setting.momentum,
use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss_op + reg_loss,
global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
# backup all code
if not args.no_code_backup:
code_folder = os.path.abspath(os.path.dirname(__file__))
shutil.copytree(
code_folder,
os.path.join(root_folder, os.path.basename(code_folder)))
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum(
[np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
with tf.Session() as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(
datetime.now(), args.load_ckpt))
else:
latest_ckpt = tf.train.latest_checkpoint(folder_ckpt)
if latest_ckpt:
print('{}-Found checkpoint {}'.format(datetime.now(),
latest_ckpt))
saver.restore(sess, latest_ckpt)
print('{}-Checkpoint loaded from {} (Iter {})'.format(
datetime.now(), latest_ckpt, sess.run(global_step)))
for batch_idx_train in tqdm(range(batch_num)):
######################################################################
# Validation
if (batch_idx_train % step_val == 0 and (batch_idx_train != 0 or args.load_ckpt is not None)) \
or batch_idx_train == batch_num - 1:
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
print('{}-Checkpoint saved to {}!'.format(
datetime.now(), filename_ckpt))
sess.run(reset_metrics_op)
for batch_val_idx in range(batch_num_val):
start_idx = batch_size * batch_val_idx
end_idx = min(start_idx + batch_size, num_val)
batch_size_val = end_idx - start_idx
points_batch = data_val[start_idx:end_idx, ...]
points_num_batch = data_num_val[start_idx:end_idx, ...]
labels_batch = label_val[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[labels_batch]
xforms_np, rotations_np = pf.get_xforms(
batch_size_val,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
sess.run(
[
loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op
],
feed_dict={
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_val, sample_num,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter_val]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
False,
})
loss_val, t_1_acc_val, t_1_per_class_acc_val, summaries_val, step = sess.run(
[
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
summaries_val_op, global_step
])
summary_writer.add_summary(summaries_val, step)
print(
'{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}'
.format(datetime.now(), loss_val, t_1_acc_val,
t_1_per_class_acc_val))
sys.stdout.flush()
######################################################################
######################################################################
# Training
start_idx = (batch_size * batch_idx_train) % num_train
end_idx = min(start_idx + batch_size, num_train)
batch_size_train = end_idx - start_idx
points_batch = data_train[start_idx:end_idx, ...]
points_num_batch = data_num_train[start_idx:end_idx, ...]
labels_batch = label_train[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[labels_batch]
if start_idx + batch_size_train == num_train:
if is_list_of_h5_list:
filelist_train_prev = seg_list[(seg_list_idx - 1) %
len(seg_list)]
filelist_train = seg_list[seg_list_idx % len(seg_list)]
if filelist_train != filelist_train_prev:
data_train, _, data_num_train, label_train, _ = data_utils.load_seg(
filelist_train)
num_train = data_train.shape[0]
seg_list_idx = seg_list_idx + 1
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
offset = int(
random.gauss(0, sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(
batch_size_train,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
sess.run(reset_metrics_op)
sess.run(
[
train_op, loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op
],
feed_dict={
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_train, sample_num_train,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
True,
})
if batch_idx_train % 10 == 0:
loss, t_1_acc, t_1_per_class_acc, summaries, step = sess.run([
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
summaries_op, global_step
])
summary_writer.add_summary(summaries, step)
print(
'{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}'
.format(datetime.now(), step, loss, t_1_acc,
t_1_per_class_acc))
sys.stdout.flush()
######################################################################
print('{}-Done!'.format(datetime.now()))
def train_one_epoch(sess, ops, train_writer):
is_training = True
current_data, current_label, current_mask = data_utils.get_current_data_withmask_h5(
TRAIN_DATA, TRAIN_LABELS, TRAIN_MASKS, sample_num)
current_label = np.squeeze(current_label)
current_mask = np.squeeze(current_mask)
num_batches = current_data.shape[0] // batch_size
total_correct = 0
total_seen = 0
loss_sum = 0
total_correct_seg = 0
classify_loss_sum = 0
seg_loss_sum = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * batch_size
end_idx = (batch_idx + 1) * batch_size
xforms_np, rotations_np = pf.get_xforms(batch_size,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
# Augment batched point clouds by rotation and jittering
feed_dict = {
ops['pointclouds_pl']: current_data[start_idx:end_idx, :, :],
ops['labels_pl']: current_label[start_idx:end_idx],
ops['masks_pl']: current_mask[start_idx:end_idx],
ops['is_training_pl']: is_training,
ops['xforms']: xforms_np,
ops['rotations']: rotations_np,
ops['jitter_range']: np.array([jitter])
}
summary, step, _, loss_val, pred_val, seg_val, classify_loss, seg_loss = sess.run(
[
ops['merged'], ops['step'], ops['train_op'], ops['loss'],
ops['pred'], ops['seg_pred'], ops['classify_loss'],
ops['seg_loss']
],
feed_dict=feed_dict)
train_writer.add_summary(summary, step)
pred_val = np.sum(pred_val, axis=1)
pred_val = np.argmax(pred_val, 1)
# print(pred_val)
# print(current_label[start_idx:end_idx])
correct = np.sum(pred_val == current_label[start_idx:end_idx])
seg_val = np.argmax(seg_val, 2)
seg_correct = np.sum(seg_val == current_mask[start_idx:end_idx])
total_correct_seg += seg_correct
total_correct += correct
total_seen += batch_size
loss_sum += loss_val
classify_loss_sum += classify_loss
seg_loss_sum += seg_loss
log_string('mean loss: %f' % (loss_sum / float(num_batches)))
log_string('classify mean loss: %f' %
(classify_loss_sum / float(num_batches)))
log_string('seg mean loss: %f' % (seg_loss_sum / float(num_batches)))
log_string('accuracy: %f' % (total_correct / float(total_seen)))
log_string('seg accuracy: %f' % (total_correct_seg /
(float(total_seen) * sample_num)))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--path', '-t', help='Path to data', required=True)
parser.add_argument('--path_val', '-v', help='Path to validation data')
parser.add_argument('--load_ckpt', '-l', help='Path to a check point file for load')
parser.add_argument('--model', '-m', help='Model to use', required=True)
parser.add_argument('--setting', '-x', help='Setting to use', required=True)
parser.add_argument('--train_name', '-n', help='train name')
parser.add_argument('--save_folder_chenzhixing_original', '-s', help='Path to folder for saving check points and summary', required=True)
args = parser.parse_args()
save_folder_chenzhixing = args.save_folder_chenzhixing_original
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
root_folder = os.path.join(save_folder_chenzhixing, '%s_%s_%s' % (args.model, args.setting, args.train_name))
if not os.path.exists(root_folder):
os.makedirs(root_folder)
sys.stdout = open(os.path.join(root_folder, 'log.txt'), 'w')
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(os.path.dirname(__file__), args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
step_val = setting.step_val
num_class = setting.num_class
rotation_range = setting.rotation_range
rotation_range_val = setting.rotation_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
data_train, label_train, data_val, label_val = setting.load_fn(args.path, args.path_val)
if setting.save_ply_fn is not None:
folder = os.path.join(root_folder, 'pts')
print('{}-Saving samples as .ply files to {}...'.format(datetime.now(), folder))
sample_num_for_ply = min(512, data_train.shape[0])
if setting.map_fn is None:
data_sample = data_train[:sample_num_for_ply]
else:
data_sample_list = []
for idx in range(sample_num_for_ply):
data_sample_list.append(setting.map_fn(data_train[idx], 0)[0])
data_sample = np.stack(data_sample_list)
setting.save_ply_fn(data_sample, folder)
num_train = data_train.shape[0]
point_num = data_train.shape[1]
num_val = data_val.shape[0]
print('{}-{:d}/{:d} training/validation samples.'.format(datetime.now(), num_train, num_val))
######################################################################
# Placeholders
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32, shape=(None, 3, 3), name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
data_train_placeholder = tf.placeholder(data_train.dtype, data_train.shape)
label_train_placeholder = tf.placeholder(label_train.dtype, label_train.shape)
data_val_placeholder = tf.placeholder(data_val.dtype, data_val.shape)
label_val_placeholder = tf.placeholder(label_val.dtype, label_val.shape)
handle = tf.placeholder(tf.string, shape=[])
######################################################################
dataset_train = tf.data.Dataset.from_tensor_slices((data_train_placeholder, label_train_placeholder))
if setting.map_fn is not None:
dataset_train = dataset_train.map(lambda data, label: tuple(tf.py_func(
setting.map_fn, [data, label], [tf.float32, label.dtype])), num_parallel_calls=setting.num_parallel_calls)
dataset_train = dataset_train.shuffle(buffer_size=batch_size * 4)
if setting.keep_remainder:
dataset_train = dataset_train.batch(batch_size)
batch_num_per_epoch = math.ceil(num_train / batch_size)
else:
dataset_train = dataset_train.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
batch_num_per_epoch = math.floor(num_train / batch_size)
batch_num = batch_num_per_epoch * num_epochs
print('{}-{:d} training batches.'.format(datetime.now(), batch_num))
dataset_train = dataset_train.repeat()
iterator_train = dataset_train.make_initializable_iterator()
dataset_val = tf.data.Dataset.from_tensor_slices((data_val_placeholder, label_val_placeholder))
if setting.map_fn is not None:
dataset_val = dataset_val.map(lambda data, label: tuple(tf.py_func(
setting.map_fn, [data, label], [tf.float32, label.dtype])), num_parallel_calls=setting.num_parallel_calls)
if setting.keep_remainder:
dataset_val = dataset_val.batch(batch_size)
batch_num_val = math.ceil(num_val / batch_size)
else:
dataset_val = dataset_val.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
batch_num_val = math.floor(num_val / batch_size)
iterator_val = dataset_val.make_initializable_iterator()
iterator = tf.data.Iterator.from_string_handle(handle, dataset_train.output_types, dataset_train.output_shapes)
(pts_fts, labels) = iterator.get_next()
features_augmented = None
if setting.data_dim > 3:
points, features = tf.split(pts_fts, [3, setting.data_dim - 3], axis=-1, name='split_points_features')
if setting.use_extra_features:
features_sampled = tf.gather_nd(features, indices=indices, name='features_sampled')
if setting.with_normal_feature:
features_augmented = pf.augment(features_sampled, rotations)
else:
features_augmented = features_sampled
else:
points = pts_fts
points_sampled = tf.gather_nd(points, indices=indices, name='points_sampled')
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
net = model.Net(points=points_augmented, features=features_augmented, num_class=num_class,
is_training=is_training, setting=setting)
logits, probs = net.logits, net.probs
labels_2d = tf.expand_dims(labels, axis=-1, name='labels_2d')
labels_tile = tf.tile(labels_2d, (1, tf.shape(probs)[1]), name='labels_tile')
loss_op = tf.losses.sparse_softmax_cross_entropy(labels=labels_tile, logits=logits)
t_1_acc_op = pf.top_1_accuracy(probs, labels_tile)
_ = tf.summary.scalar('loss/train', tensor=loss_op, collections=['train'])
_ = tf.summary.scalar('t_1_acc/train', tensor=t_1_acc_op, collections=['train'])
loss_val_avg = tf.placeholder(tf.float32)
t_1_acc_val_avg = tf.placeholder(tf.float32)
_ = tf.summary.scalar('loss/val', tensor=loss_val_avg, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val', tensor=t_1_acc_val_avg, collections=['val'])
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base, global_step, setting.decay_steps,
setting.decay_rate, staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate', tensor=lr_clip_op, collections=['train'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op, epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op, momentum=0.9, use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss_op + reg_loss, global_step=global_step)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
# backup this file, model and setting
shutil.copy(__file__, os.path.join(root_folder, os.path.basename(__file__)))
shutil.copy(os.path.join(os.path.dirname(__file__), args.model + '.py'),
os.path.join(root_folder, args.model + '.py'))
if not os.path.exists(os.path.join(root_folder, args.model)):
os.makedirs(os.path.join(root_folder, args.model))
shutil.copy(os.path.join(setting_path, args.setting + '.py'),
os.path.join(root_folder, args.model, args.setting + '.py'))
folder_ckpt = os.path.join(root_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(root_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum([np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Parameter number: {:d}.'.format(datetime.now(), parameter_num))
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
saver.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded from {}!'.format(datetime.now(), args.load_ckpt))
handle_train = sess.run(iterator_train.string_handle())
handle_val = sess.run(iterator_val.string_handle())
sess.run(iterator_train.initializer, feed_dict={
data_train_placeholder: data_train,
label_train_placeholder: label_train,
})
######################################################################
# Predict
outer_predict_acc = 0
outer_predict_num = 10
for opi in range(outer_predict_num):
predict_acc = 0
predict_num = 20
predict_ave_probs = []
labels_list = []
total_time = 0
for pre_i in range(predict_num):
sess.run(iterator_val.initializer, feed_dict={
data_val_placeholder: data_val,
label_val_placeholder: label_val,
})
losses = []
t_1_accs = []
pre_i_probs = []
for batch_idx_val in range(batch_num_val):
if not setting.keep_remainder or num_val % batch_size == 0 or batch_idx_val != batch_num_val - 1:
batch_size_val = batch_size
else:
batch_size_val = num_val % batch_size
xforms_np, rotations_np = pf.get_xforms(batch_size_val, rotation_range=rotation_range_val,
order=setting.order)
time1 = time.time()
_, loss_val, t_1_acc_val, predict_probs, true_labels = \
sess.run([update_ops, loss_op, t_1_acc_op, probs, labels],
feed_dict={
handle: handle_val,
indices: pf.get_indices(batch_size_val, sample_num, point_num),#, False),
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter_val]),
is_training: False,
})
time2 = time.time()
total_time += time2 - time1
losses.append(loss_val * batch_size_val)
t_1_accs.append(t_1_acc_val * batch_size_val)
pre_i_probs.append(predict_probs)
if (pre_i == 0):
labels_list.append(true_labels)
#print('{}-[Val ]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f}'.format
# (datetime.now(), batch_idx_val, loss_val, t_1_acc_val))
#sys.stdout.flush()
predict_ave_probs.append(pre_i_probs)
#loss_avg = sum(losses) / num_val
#t_1_acc_avg = sum(t_1_accs) / num_val
#print('{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f}'
# .format(datetime.now(), loss_avg, t_1_acc_avg))
#sys.stdout.flush()
#predict_acc += t_1_acc_avg
#predict_acc /= predict_num
#print('{}-[Mean ]-Average: T-1 Acc: {:.4f}'
# .format(datetime.now(), predict_acc))
predict_ave_probs = np.argmax(np.mean(np.squeeze(np.array(predict_ave_probs)), axis=0), axis=1)
labels_list = np.squeeze(np.array(labels_list))
print('predict:')
print(predict_ave_probs)
print('label:')
print(labels_list)
true_num = np.count_nonzero(predict_ave_probs == labels_list)
total_num = labels_list.shape[0]
outer_predict_acc += true_num/total_num*100
print('Acc: %.2f (%d/%d)' % (true_num/total_num*100, true_num, total_num))
print('average time: %f' % (total_time/(predict_num*batch_num_val)))
sys.stdout.flush()
outer_predict_acc /= outer_predict_num
print('\n Average Acc: %.2f' % outer_predict_acc)
sys.stdout.flush()
exit()
######################################################################
best_acc = -np.inf
for batch_idx_train in range(batch_num):
######################################################################
# Validation
if (batch_idx_train != 0 and batch_idx_train % step_val == 0) or batch_idx_train == batch_num - 1:
filename_ckpt = os.path.join(folder_ckpt, 'last_model')
saver.save(sess, filename_ckpt, global_step=None)
print('{}-Checkpoint saved to {}!'.format(datetime.now(), filename_ckpt))
predict_num = 20
predict_ave_probs = []
labels_list = []
losses = []
for pre_i in range(predict_num):
sess.run(iterator_val.initializer, feed_dict={
data_val_placeholder: data_val,
label_val_placeholder: label_val,
})
pre_i_probs = []
for batch_idx_val in range(batch_num_val):
if not setting.keep_remainder or num_val % batch_size == 0 or batch_idx_val != batch_num_val - 1:
batch_size_val = batch_size
else:
batch_size_val = num_val % batch_size
xforms_np, rotations_np = pf.get_xforms(batch_size_val, rotation_range=rotation_range_val,
order=setting.order)
_, loss_val, t_1_acc_val, predict_probs, true_labels = \
sess.run([update_ops, loss_op, t_1_acc_op, probs, labels],
feed_dict={
handle: handle_val,
indices: pf.get_indices(batch_size_val, sample_num, point_num),#, False),
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter_val]),
is_training: False,
})
losses.append(loss_val * batch_size_val)
pre_i_probs.append(predict_probs)
if (pre_i == 0):
labels_list.append(true_labels)
predict_ave_probs.append(pre_i_probs)
loss_avg = sum(losses) / (predict_num * num_val)
predict_ave_probs = np.argmax(np.mean(np.reshape(np.array(predict_ave_probs), (predict_num, num_val, num_class)), axis=0), axis=1)
labels_list = np.reshape(np.array(labels_list), (num_val,))
true_num = np.count_nonzero(predict_ave_probs == labels_list)
t_1_acc_avg = true_num / num_val
print('{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f}'
.format(datetime.now(), loss_avg, t_1_acc_avg))
if t_1_acc_avg > (best_acc-1e-6):
best_acc = t_1_acc_avg
print('{}-[Val ]-best: Loss: {:.4f} T-1 Acc: {:.4f}'
.format(datetime.now(), loss_avg, t_1_acc_avg))
filename_ckpt = os.path.join(folder_ckpt, 'best_model')
saver.save(sess, filename_ckpt, global_step=None)
print('{}-Checkpoint saved to {}!'.format(datetime.now(), filename_ckpt))
sys.stdout.flush()
#############################################################################
# Original Validation
# sess.run(iterator_val.initializer, feed_dict={
# data_val_placeholder: data_val,
# label_val_placeholder: label_val,
# })
# filename_ckpt = os.path.join(folder_ckpt, 'last_model')
# saver.save(sess, filename_ckpt, global_step=None)
# print('{}-Checkpoint saved to {}!'.format(datetime.now(), filename_ckpt))
#
# losses = []
# t_1_accs = []
# for batch_idx_val in range(batch_num_val):
# if not setting.keep_remainder or num_val % batch_size == 0 or batch_idx_val != batch_num_val - 1:
# batch_size_val = batch_size
# else:
# batch_size_val = num_val % batch_size
# xforms_np, rotations_np = pf.get_xforms(batch_size_val, rotation_range=rotation_range_val,
# order=setting.order)
# _, loss_val, t_1_acc_val = \
# sess.run([update_ops, loss_op, t_1_acc_op],
# feed_dict={
# handle: handle_val,
# indices: pf.get_indices(batch_size_val, sample_num, point_num),#, False),
# xforms: xforms_np,
# rotations: rotations_np,
# jitter_range: np.array([jitter_val]),
# is_training: False,
# })
# losses.append(loss_val * batch_size_val)
# t_1_accs.append(t_1_acc_val * batch_size_val)
# print('{}-[Val ]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f}'.format
# (datetime.now(), batch_idx_val, loss_val, t_1_acc_val))
# sys.stdout.flush()
#
# loss_avg = sum(losses) / num_val
# t_1_acc_avg = sum(t_1_accs) / num_val
# summaries_val = sess.run(summaries_val_op,
# feed_dict={
# loss_val_avg: loss_avg,
# t_1_acc_val_avg: t_1_acc_avg,
# })
# summary_writer.add_summary(summaries_val, batch_idx_train)
# print('{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f}'
# .format(datetime.now(), loss_avg, t_1_acc_avg))
# if t_1_acc_avg > (best_acc-1e-6):
# best_acc = t_1_acc_avg
# print('{}-[Val ]-best: Loss: {:.4f} T-1 Acc: {:.4f}'
# .format(datetime.now(), loss_avg, t_1_acc_avg))
# filename_ckpt = os.path.join(folder_ckpt, 'best_model')
# saver.save(sess, filename_ckpt, global_step=None)
# print('{}-Checkpoint saved to {}!'.format(datetime.now(), filename_ckpt))
# sys.stdout.flush()
######################################################################
######################################################################
# Training
if not setting.keep_remainder or num_train % batch_size == 0 or (batch_idx_train % batch_num_per_epoch) != (batch_num_per_epoch - 1):
batch_size_train = batch_size
else:
batch_size_train = num_train % batch_size
offset = int(random.gauss(0, sample_num // 8))
offset = max(offset, -sample_num // 4)
offset = min(offset, sample_num // 4)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(batch_size_train, rotation_range=rotation_range,
order=setting.order)
_, loss, t_1_acc, summaries = \
sess.run([train_op, loss_op, t_1_acc_op, summaries_op],
feed_dict={
handle: handle_train,
indices: pf.get_indices(batch_size_train, sample_num_train, point_num),
xforms: xforms_np,
rotations: rotations_np,
jitter_range: np.array([jitter]),
is_training: True,
})
summary_writer.add_summary(summaries, batch_idx_train)
print('{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f}'
.format(datetime.now(), batch_idx_train, loss, t_1_acc))
sys.stdout.flush()
######################################################################
print('{}-Done!'.format(datetime.now()))
def main():
args = AttrDict()
# Path of training set ground truth file list (.txt)
args.filelist = os.path.join(SCENENN_DIR, 'train_files.txt')
# Path of validation set ground truth file list (.txt)
args.filelist_val = os.path.join(SCENENN_DIR, 'test_files.txt')
# Path of a check point file to load
args.load_ckpt = os.path.join(ROOT_DIR, '..', 'models',
'pretrained_scannet', 'ckpts', 'iter-354000')
# Base directory where model checkpoint and summary files get saved in separate subdirectories
args.save_folder = os.path.join(ROOT_DIR, '..', 'models')
# PointCNN model to use
args.model = 'pointcnn_seg'
# Model setting to use
args.setting = 'scenenn_x8_2048_fps'
time_string = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
model_save_folder = os.path.join(
args.save_folder,
'%s_%s_%s_%d' % (args.model, args.setting, time_string, os.getpid()))
if not os.path.exists(model_save_folder):
os.makedirs(model_save_folder)
# sys.stdout = open(os.path.join(model_save_folder, 'log.txt'), 'w')
print('PID:', os.getpid())
print(args)
model = importlib.import_module(args.model)
setting_path = os.path.join(ROOT_DIR, args.model)
sys.path.append(setting_path)
setting = importlib.import_module(args.setting)
num_epochs = setting.num_epochs
batch_size = setting.batch_size
sample_num = setting.sample_num
step_val = setting.step_val
label_weights_list = setting.label_weights
rotation_range = setting.rotation_range
rotation_range_val = setting.rotation_range_val
scaling_range = setting.scaling_range
scaling_range_val = setting.scaling_range_val
jitter = setting.jitter
jitter_val = setting.jitter_val
# Prepare inputs
print('{}-Preparing datasets...'.format(datetime.now()))
is_list_of_h5_list = not data_utils.is_h5_list(args.filelist)
if is_list_of_h5_list:
seg_list = data_utils.load_seg_list(args.filelist)
seg_list_idx = 0
filelist_train = seg_list[seg_list_idx]
seg_list_idx = seg_list_idx + 1
else:
filelist_train = args.filelist
data_train, _, data_num_train, label_train, _ = data_utils.load_seg(
filelist_train)
data_val, _, data_num_val, label_val, _ = data_utils.load_seg(
args.filelist_val)
# shuffle
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
num_train = data_train.shape[0]
point_num = data_train.shape[1]
num_val = data_val.shape[0]
print('{}-{:d}/{:d} training/validation samples.'.format(
datetime.now(), num_train, num_val))
batch_num = (num_train * num_epochs + batch_size - 1) // batch_size
print('{}-{:d} training batches.'.format(datetime.now(), batch_num))
batch_num_val = math.ceil(num_val / batch_size)
print('{}-{:d} testing batches per test.'.format(datetime.now(),
batch_num_val))
######################################################################
# Placeholders
print('{}-Initializing TF-placeholders...'.format(datetime.now()))
indices = tf.placeholder(tf.int32, shape=(None, None, 2), name="indices")
xforms = tf.placeholder(tf.float32, shape=(None, 3, 3), name="xforms")
rotations = tf.placeholder(tf.float32,
shape=(None, 3, 3),
name="rotations")
jitter_range = tf.placeholder(tf.float32, shape=(1), name="jitter_range")
global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = tf.placeholder(tf.bool, name='is_training')
pts_fts = tf.placeholder(tf.float32,
shape=(None, point_num, setting.data_dim),
name='pts_fts')
labels_seg = tf.placeholder(tf.int64,
shape=(None, point_num),
name='labels_seg')
labels_weights = tf.placeholder(tf.float32,
shape=(None, point_num),
name='labels_weights')
######################################################################
pts_fts_sampled = tf.gather_nd(pts_fts,
indices=indices,
name='pts_fts_sampled')
features_augmented = None
if setting.data_dim > 3:
points_sampled, features_sampled = tf.split(
pts_fts_sampled, [3, setting.data_dim - 3],
axis=-1,
name='split_points_features')
if setting.use_extra_features:
if setting.with_normal_feature:
if setting.data_dim < 6:
print('Only 3D normals are supported!')
exit()
elif setting.data_dim == 6:
features_augmented = pf.augment(features_sampled,
rotations)
else:
normals, rest = tf.split(features_sampled,
[3, setting.data_dim - 6])
normals_augmented = pf.augment(normals, rotations)
features_augmented = tf.concat([normals_augmented, rest],
axis=-1)
else:
features_augmented = features_sampled
else:
points_sampled = pts_fts_sampled
points_augmented = pf.augment(points_sampled, xforms, jitter_range)
labels_sampled = tf.gather_nd(labels_seg,
indices=indices,
name='labels_sampled')
labels_weights_sampled = tf.gather_nd(labels_weights,
indices=indices,
name='labels_weight_sampled')
print('{}-Initializing net...'.format(datetime.now()))
net = model.Net(points_augmented, features_augmented, is_training, setting)
logits = net.logits
probs = tf.nn.softmax(logits, name='probs')
predictions = tf.argmax(probs, axis=-1, name='predictions')
loss_op = tf.losses.sparse_softmax_cross_entropy(
labels=labels_sampled, logits=logits, weights=labels_weights_sampled)
with tf.name_scope('metrics'):
loss_mean_op, loss_mean_update_op = tf.metrics.mean(loss_op)
t_1_acc_op, t_1_acc_update_op = tf.metrics.accuracy(
labels_sampled, predictions, weights=labels_weights_sampled)
t_1_per_class_acc_op, t_1_per_class_acc_update_op = \
tf.metrics.mean_per_class_accuracy(labels_sampled, predictions, setting.num_class,
weights=labels_weights_sampled)
reset_metrics_op = tf.variables_initializer([
var for var in tf.local_variables()
if var.name.split('/')[0] == 'metrics'
])
_ = tf.summary.scalar('loss/train',
tensor=loss_mean_op,
collections=['train'])
_ = tf.summary.scalar('t_1_acc/train',
tensor=t_1_acc_op,
collections=['train'])
_ = tf.summary.scalar('t_1_per_class_acc/train',
tensor=t_1_per_class_acc_op,
collections=['train'])
_ = tf.summary.scalar('loss/val', tensor=loss_mean_op, collections=['val'])
_ = tf.summary.scalar('t_1_acc/val',
tensor=t_1_acc_op,
collections=['val'])
_ = tf.summary.scalar('t_1_per_class_acc/val',
tensor=t_1_per_class_acc_op,
collections=['val'])
lr_exp_op = tf.train.exponential_decay(setting.learning_rate_base,
global_step,
setting.decay_steps,
setting.decay_rate,
staircase=True)
lr_clip_op = tf.maximum(lr_exp_op, setting.learning_rate_min)
_ = tf.summary.scalar('learning_rate',
tensor=lr_clip_op,
collections=['train'])
reg_loss = setting.weight_decay * tf.losses.get_regularization_loss()
print('{}-Setting up optimizer...'.format(datetime.now()))
if setting.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate=lr_clip_op,
epsilon=setting.epsilon)
elif setting.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate=lr_clip_op,
momentum=setting.momentum,
use_nesterov=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
last_layer_train_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, 'logits')
last_layer_train_op = optimizer.minimize(
loss_op + reg_loss,
global_step=global_step,
var_list=last_layer_train_vars)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
saver = tf.train.Saver(max_to_keep=None)
variables_to_restore = get_variables_to_restore(exclude=['logits'])
restorer = tf.train.Saver(var_list=variables_to_restore, max_to_keep=None)
# backup all code
# code_folder = os.path.abspath(os.path.dirname(__file__))
# shutil.copytree(code_folder, os.path.join(model_save_folder, os.path.basename(code_folder)), symlinks=True)
folder_ckpt = os.path.join(model_save_folder, 'ckpts')
if not os.path.exists(folder_ckpt):
os.makedirs(folder_ckpt)
folder_summary = os.path.join(model_save_folder, 'summary')
if not os.path.exists(folder_summary):
os.makedirs(folder_summary)
parameter_num = np.sum(
[np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
print('{}-Number of model parameters: {:d}.'.format(
datetime.now(), parameter_num))
with tf.Session() as sess:
summaries_op = tf.summary.merge_all('train')
summaries_val_op = tf.summary.merge_all('val')
summary_writer = tf.summary.FileWriter(folder_summary, sess.graph)
print('{}-Initializing variables...'.format(datetime.now()))
sess.run(init_op)
# Load the model
if args.load_ckpt is not None:
print('{}-Loading checkpoint from {}...'.format(
datetime.now(), args.load_ckpt))
restorer.restore(sess, args.load_ckpt)
print('{}-Checkpoint loaded.'.format(datetime.now()))
for batch_idx_train in tqdm(range(batch_num), ncols=60):
if (batch_idx_train % step_val == 0 and (batch_idx_train != 0 or args.load_ckpt is not None)) \
or batch_idx_train == batch_num - 1:
######################################################################
# Validation
filename_ckpt = os.path.join(folder_ckpt, 'iter')
saver.save(sess, filename_ckpt, global_step=global_step)
tqdm.write('{}-Checkpoint saved to {}!'.format(
datetime.now(), filename_ckpt))
sess.run(reset_metrics_op)
for batch_val_idx in range(batch_num_val):
start_idx = batch_size * batch_val_idx
end_idx = min(start_idx + batch_size, num_val)
batch_size_val = end_idx - start_idx
points_batch = data_val[start_idx:end_idx, ...]
points_num_batch = data_num_val[start_idx:end_idx, ...]
labels_batch = label_val[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[labels_batch]
xforms_np, rotations_np = pf.get_xforms(
batch_size_val,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
sess.run(
[
loss_mean_update_op, t_1_acc_update_op,
t_1_per_class_acc_update_op
],
feed_dict={
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_val, sample_num,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter_val]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
False,
})
loss_val, t_1_acc_val, t_1_per_class_acc_val, summaries_val = sess.run(
[
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
summaries_val_op
])
summary_writer.add_summary(summaries_val, batch_idx_train)
tqdm.write(
'{}-[Val ]-Average: Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}'
.format(datetime.now(), loss_val, t_1_acc_val,
t_1_per_class_acc_val))
sys.stdout.flush()
######################################################################
######################################################################
# Training
start_idx = (batch_size * batch_idx_train) % num_train
end_idx = min(start_idx + batch_size, num_train)
batch_size_train = end_idx - start_idx
points_batch = data_train[start_idx:end_idx, ...]
points_num_batch = data_num_train[start_idx:end_idx, ...]
labels_batch = label_train[start_idx:end_idx, ...]
weights_batch = np.array(label_weights_list)[labels_batch]
if start_idx + batch_size_train == num_train:
if is_list_of_h5_list:
filelist_train_prev = seg_list[(seg_list_idx - 1) %
len(seg_list)]
filelist_train = seg_list[seg_list_idx % len(seg_list)]
if filelist_train != filelist_train_prev:
data_train, _, data_num_train, label_train, _ = data_utils.load_seg(
filelist_train)
num_train = data_train.shape[0]
seg_list_idx = seg_list_idx + 1
data_train, data_num_train, label_train = \
data_utils.grouped_shuffle([data_train, data_num_train, label_train])
offset = int(
random.gauss(0, sample_num * setting.sample_num_variance))
offset = max(offset, -sample_num * setting.sample_num_clip)
offset = min(offset, sample_num * setting.sample_num_clip)
sample_num_train = sample_num + offset
xforms_np, rotations_np = pf.get_xforms(
batch_size_train,
rotation_range=rotation_range,
scaling_range=scaling_range,
order=setting.rotation_order)
sess.run(reset_metrics_op)
sess.run(
[
last_layer_train_op, loss_mean_update_op,
t_1_acc_update_op, t_1_per_class_acc_update_op
],
feed_dict={
pts_fts:
points_batch,
indices:
pf.get_indices(batch_size_train, sample_num_train,
points_num_batch),
xforms:
xforms_np,
rotations:
rotations_np,
jitter_range:
np.array([jitter]),
labels_seg:
labels_batch,
labels_weights:
weights_batch,
is_training:
True,
})
if batch_idx_train % 10 == 0:
loss, t_1_acc, t_1_per_class_acc, summaries = sess.run([
loss_mean_op, t_1_acc_op, t_1_per_class_acc_op,
summaries_op
])
summary_writer.add_summary(summaries, batch_idx_train)
# tqdm.write('{}-[Train]-Iter: {:06d} Loss: {:.4f} T-1 Acc: {:.4f} T-1 mAcc: {:.4f}'
# .format(datetime.now(), batch_idx_train, loss, t_1_acc, t_1_per_class_acc))
sys.stdout.flush()
######################################################################
print('{}-Done!'.format(datetime.now()))
