root=DATA_PATH,
npoints=NUM_POINT,
split='train',
normal_channel=FLAGS.normal,
batch_size=BATCH_SIZE)
TEST_DATASET = modelnet_dataset.ModelNetDataset(
root=DATA_PATH,
npoints=NUM_POINT,
split='test',
normal_channel=FLAGS.normal,
batch_size=BATCH_SIZE)
else:
assert (NUM_POINT <= 2048)
TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(
os.path.join(BASE_DIR,
'data/modelnet40_ply_hdf5_2048/train_files.txt'),
batch_size=BATCH_SIZE,
npoints=NUM_POINT,
shuffle=True)
TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(
BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'),
batch_size=BATCH_SIZE,
npoints=NUM_POINT,
shuffle=False)
def log_string(out_str):
LOG_FOUT.write(out_str + '\n')
LOG_FOUT.flush()
print(out_str)
def eval_one_epoch(sess, ops, num_votes=1, topk=1):
is_training = False
TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, DATASET_DIR, 'modelnet40_ply_hdf5_2048/test_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False)
# Make sure batch data is of same size
cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel()))
cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32)
total_correct = 0
total_seen = 0
loss_sum = 0
batch_idx = 0
shape_ious = []
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
while TEST_DATASET.has_next_batch():
batch_data, batch_label = TEST_DATASET.next_batch(augment=False)
bsize = batch_data.shape[0]
# print('Batch: %03d, batch size: %d'%(batch_idx, bsize))
# for the last batch in the epoch, the bsize:end are from last batch
cur_batch_data[0:bsize,...] = batch_data
cur_batch_label[0:bsize] = batch_label
batch_pred_sum = np.zeros((BATCH_SIZE, NUM_CLASSES)) # score for classes
for vote_idx in range(num_votes):
# Shuffle point order to achieve different farthest samplings
shuffled_indices = np.arange(NUM_POINT)
#np.random.shuffle(shuffled_indices)
if FLAGS.normal:
rotated_data = provider.rotate_point_cloud_by_angle_with_normal(cur_batch_data[:, shuffled_indices, :],
vote_idx/float(num_votes) * np.pi * 2)
else:
rotated_data = provider.rotate_point_cloud_by_angle(cur_batch_data[:, shuffled_indices, :],
vote_idx/float(num_votes) * np.pi * 2)
feed_dict = {ops['pointclouds_pl']: rotated_data,
ops['labels_pl']: cur_batch_label,
ops['is_training_pl']: is_training}
loss_val, pred_val = sess.run([ops['loss'], ops['pred']], feed_dict=feed_dict)
batch_pred_sum += pred_val
pred_val = np.argmax(batch_pred_sum, 1)
correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize])
total_correct += correct
total_seen += bsize
loss_sum += loss_val
batch_idx += 1
for i in range(bsize):
l = batch_label[i]
total_seen_class[l] += 1
total_correct_class[l] += (pred_val[i] == l)
log_string('eval mean loss: %f' % (loss_sum / float(batch_idx)))
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]))
'''
return total_correct / float(total_seen), np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))
root=DATA_PATH,
npoints=NUM_POINT,
split='train',
normal_channel=FLAGS.normal,
batch_size=BATCH_SIZE)
TEST_DATASET = modelnet_dataset.ModelNetDataset(
root=DATA_PATH,
npoints=NUM_POINT,
split='test',
normal_channel=FLAGS.normal,
batch_size=BATCH_SIZE)
else:
assert (NUM_POINT <= 2048)
TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(
os.path.join('/volume/USERSTORE/jude_ra/master_thesis/pointnet2',
'data/modelnet40_ply_hdf5_2048/train_files.txt'),
batch_size=BATCH_SIZE,
npoints=NUM_POINT,
shuffle=True)
TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(
'/volume/USERSTORE/jude_ra/master_thesis/pointnet2',
'data/modelnet40_ply_hdf5_2048/test_files.txt'),
batch_size=BATCH_SIZE,
npoints=NUM_POINT,
shuffle=False)
def log_string(out_str):
LOG_FOUT.write(out_str + '\n')
LOG_FOUT.flush()
print(out_str)
normal_channel=config.normal,
batch_size=config.batch_size)
TEST_DATASET = modelnet_dataset.ModelNetDataset(
root=DATA_PATH,
npoints=config.num_points,
split='test',
normal_channel=config.normal,
batch_size=config.batch_size)
else:
assert (config.num_points <= 2048)
CHANNELS = 3
TRAIN_FILES = os.path.join(config.data, 'train_files.txt')
TEST_FILES = os.path.join(config.data, 'test_files.txt')
TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(
TRAIN_FILES,
batch_size=config.batch_size,
npoints=config.num_points,
shuffle=True)
TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(
TEST_FILES,
batch_size=config.batch_size,
npoints=config.num_points,
shuffle=False)
def get_learning_rate(batch):
learning_rate = tf.train.exponential_decay(
config.learning_rate, # Base learning rate.
batch * config.batch_size, # Current index into the dataset.
config.decay_step, # Decay step.
config.decay_rate, # Decay rate.
from model_cls import pointnet2
from util.provider import rotate_point_cloud_by_angle
import modelnet_h5_dataset
from tf_cls import plot_cm
num_classes = 40
num_point = 1024
batch_size = 16
shape_names = [
line.rstrip()
for line in open('data/modelnet40_ply_hdf5_2048/shape_names.txt')
]
test_dataset = modelnet_h5_dataset.ModelNetH5Dataset(
'data/modelnet40_ply_hdf5_2048/test_files.txt',
batch_size=batch_size,
npoints=num_point,
shuffle=False)
def evaluate(num_votes=1):
with tf.device('/gpu:0'):
point_cloud = tf.placeholder(dtype=np.float32,
shape=(batch_size, num_point, 3))
labels = tf.placeholder(dtype=np.int32, shape=batch_size)
is_training_pl = tf.placeholder(dtype=np.bool, shape=())
# simple model
logits = pointnet2(point_cloud, num_classes, is_training_pl)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=labels)
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
HOSTNAME = socket.gethostname()
NUM_CLASSES = 2
# Shapenet official train/test split
if FLAGS.normal:
assert(NUM_POINT<=10000)
DATA_PATH = os.path.join(ROOT_DIR, 'data/modelnet40_normal_resampled')
TRAIN_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE)
TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE)
else:
assert(NUM_POINT<=2048)
TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, "/home/gstavrinos/catkin_ws/src/new_hpr/pointcloud2_segments_tools/dataset0_training_75.txt"), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=True)
TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, "/home/gstavrinos/catkin_ws/src/new_hpr/pointcloud2_segments_tools/dataset0_testing_75.txt"), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False)
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
def get_learning_rate(batch):
learning_rate = tf.train.exponential_decay(
BASE_LEARNING_RATE, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
DECAY_STEP, # Decay step.
DECAY_RATE, # Decay rate.
staircase=True)
learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
