def train(assign_model_path=None):
is_training = True
bn_decay = 0.95
step = tf.Variable(0, trainable=False)
learning_rate = BASE_LEARNING_RATE
tf.summary.scalar('bn_decay', bn_decay)
tf.summary.scalar('learning_rate', learning_rate)
# get placeholder
pointclouds_pl, pointclouds_gt, pointclouds_gt_normal, pointclouds_radius = MODEL_GEN.placeholder_inputs(
BATCH_SIZE, NUM_POINT, UP_RATIO)
#create the generator model
pred, _ = MODEL_GEN.get_gen_model(pointclouds_pl,
is_training,
scope='generator',
bradius=pointclouds_radius,
reuse=None,
use_normal=False,
use_bn=False,
use_ibn=False,
bn_decay=bn_decay,
up_ratio=UP_RATIO)
#get emd loss
gen_loss_emd, matchl_out = model_utils.get_emd_loss(
pred, pointclouds_gt, pointclouds_radius)
#get repulsion loss
if USE_REPULSION_LOSS:
gen_repulsion_loss = model_utils.get_repulsion_loss4(pred)
tf.summary.scalar('loss/gen_repulsion_loss', gen_repulsion_loss)
else:
gen_repulsion_loss = 0.0
#get total loss function
pre_gen_loss = 100 * gen_loss_emd + gen_repulsion_loss + tf.losses.get_regularization_loss(
)
# create pre-generator ops
gen_update_ops = [
op for op in tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if op.name.startswith("generator")
]
gen_tvars = [
var for var in tf.trainable_variables()
if var.name.startswith("generator")
]
with tf.control_dependencies(gen_update_ops):
pre_gen_train = tf.train.AdamOptimizer(
learning_rate,
beta1=0.9).minimize(pre_gen_loss,
var_list=gen_tvars,
colocate_gradients_with_ops=True,
global_step=step)
# merge summary and add pointclouds summary
tf.summary.scalar('loss/gen_emd', gen_loss_emd)
tf.summary.scalar('loss/regularation', tf.losses.get_regularization_loss())
tf.summary.scalar('loss/pre_gen_total', pre_gen_loss)
pretrain_merged = tf.summary.merge_all()
pointclouds_image_input = tf.placeholder(tf.float32,
shape=[None, 500, 1500, 1])
pointclouds_input_summary = tf.summary.image('pointcloud_input',
pointclouds_image_input,
max_outputs=1)
pointclouds_image_pred = tf.placeholder(tf.float32,
shape=[None, 500, 1500, 1])
pointclouds_pred_summary = tf.summary.image('pointcloud_pred',
pointclouds_image_pred,
max_outputs=1)
pointclouds_image_gt = tf.placeholder(tf.float32,
shape=[None, 500, 1500, 1])
pointclouds_gt_summary = tf.summary.image('pointcloud_gt',
pointclouds_image_gt,
max_outputs=1)
image_merged = tf.summary.merge([
pointclouds_input_summary, pointclouds_pred_summary,
pointclouds_gt_summary
])
# Create a session
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:
train_writer = tf.summary.FileWriter(os.path.join(MODEL_DIR, 'train'),
sess.graph)
init = tf.global_variables_initializer()
sess.run(init)
ops = {
'pointclouds_pl': pointclouds_pl,
'pointclouds_gt': pointclouds_gt,
'pointclouds_gt_normal': pointclouds_gt_normal,
'pointclouds_radius': pointclouds_radius,
'pointclouds_image_input': pointclouds_image_input,
'pointclouds_image_pred': pointclouds_image_pred,
'pointclouds_image_gt': pointclouds_image_gt,
'pretrain_merged': pretrain_merged,
'image_merged': image_merged,
'gen_loss_emd': gen_loss_emd,
'pre_gen_train': pre_gen_train,
'pred': pred,
'step': step,
}
#restore the model
saver = tf.train.Saver(max_to_keep=6)
restore_epoch, checkpoint_path = model_utils.pre_load_checkpoint(
MODEL_DIR)
global LOG_FOUT
if restore_epoch == 0:
LOG_FOUT = open(os.path.join(MODEL_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(socket.gethostname()) + '\n')
LOG_FOUT.write(str(FLAGS) + '\n')
else:
LOG_FOUT = open(os.path.join(MODEL_DIR, 'log_train.txt'), 'a')
saver.restore(sess, checkpoint_path)
###assign the generator with another model file
if assign_model_path is not None:
print "Load pre-train model from %s" % (assign_model_path)
assign_saver = tf.train.Saver(var_list=[
var for var in tf.trainable_variables()
if var.name.startswith("generator")
])
assign_saver.restore(sess, assign_model_path)
##read data
input_data, gt_data, data_radius, _ = data_provider.load_patch_data(
skip_rate=1,
num_point=NUM_POINT,
norm=USE_DATA_NORM,
use_randominput=USE_RANDOM_INPUT)
fetchworker = data_provider.Fetcher(input_data, gt_data, data_radius,
BATCH_SIZE, NUM_POINT,
USE_RANDOM_INPUT, USE_DATA_NORM)
fetchworker.start()
for epoch in tqdm(range(restore_epoch, MAX_EPOCH + 1), ncols=55):
log_string('**** EPOCH %03d ****\t' % (epoch))
train_one_epoch(sess, ops, fetchworker, train_writer)
if epoch % 20 == 0:
saver.save(sess,
os.path.join(MODEL_DIR, "model"),
global_step=epoch)
fetchworker.shutdown()
def prediction_whole_model(data_folder=None, show=False, use_normal=False):
data_folder = '../data/test_data/our_collected_data/MC_5k'
phase = data_folder.split('/')[-2] + data_folder.split('/')[-1]
save_path = os.path.join(MODEL_DIR, 'result/' + phase)
if not os.path.exists(save_path):
os.makedirs(save_path)
samples = glob(data_folder + "/*.xyz")
samples.sort(reverse=True)
input = np.loadtxt(samples[0])
if use_normal:
pointclouds_ipt = tf.placeholder(tf.float32,
shape=(1, input.shape[0], 6))
else:
pointclouds_ipt = tf.placeholder(tf.float32,
shape=(1, input.shape[0], 3))
pred, _ = MODEL_GEN.get_gen_model(pointclouds_ipt,
is_training=False,
scope='generator',
bradius=1.0,
reuse=None,
use_normal=use_normal,
use_bn=False,
use_ibn=False,
bn_decay=0.95,
up_ratio=UP_RATIO)
saver = tf.train.Saver()
_, restore_model_path = model_utils.pre_load_checkpoint(MODEL_DIR)
print restore_model_path
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
with tf.Session(config=config) as sess:
saver.restore(sess, restore_model_path)
samples = glob(data_folder + "/*.xyz")
samples.sort()
total_time = 0
for i, item in enumerate(samples):
input = np.loadtxt(item)
gt = input
# input = data_provider.jitter_perturbation_point_cloud(np.expand_dims(input,axis=0),sigma=0.003,clip=0.006)
input = np.expand_dims(input, axis=0)
if not use_normal:
input = input[:, :, 0:3]
gt = gt[:, 0:3]
print item, input.shape
start_time = time.time()
pred_pl = sess.run(pred, feed_dict={pointclouds_ipt: input})
total_time += time.time() - start_time
norm_pl = np.zeros_like(pred_pl)
##--------------visualize predicted point cloud----------------------
path = os.path.join(save_path, item.split('/')[-1])
if show:
f, axis = plt.subplots(3)
axis[0].imshow(
pc_util.point_cloud_three_views(input[0, :, 0:3],
diameter=5))
axis[1].imshow(
pc_util.point_cloud_three_views(pred_pl[0, :, :],
diameter=5))
axis[2].imshow(
pc_util.point_cloud_three_views(gt[:, 0:3], diameter=5))
plt.show()
data_provider.save_pl(
path, np.hstack((pred_pl[0, ...], norm_pl[0, ...])))
path = path[:-4] + '_input.xyz'
data_provider.save_pl(path, input[0])
print total_time / 20