BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
HOSTNAME = socket.gethostname()
NUM_CLASSES = 40
# 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,
'data/modelnet40_ply_hdf5_2048/train_files.txt'),
batch_size=BATCH_SIZE,
npoints=NUM_POINT,
BN_DECAY_DECAY_STEP = float(config.decay_step)
BN_DECAY_CLIP = 0.99
# Shapenet official train/test split
if config.normal:
CHANNELS = 6
assert (config.num_points <= 2048)
"""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, normal_channel=True)
TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(TEST_FILES, batch_size=config.batch_size, npoints=config.num_points, shuffle=False, normal_channel=True)"""
assert (config.num_points <= 10000)
DATA_PATH = os.path.join(config.data, 'data/modelnet40_normal_resampled')
TRAIN_DATASET = modelnet_dataset.ModelNetDataset(
root=DATA_PATH,
npoints=config.num_points,
split='train',
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,
BN_INIT_DECAY = 0.5 BN_DECAY_DECAY_RATE = 0.5 BN_DECAY_DECAY_STEP = float(DECAY_STEP) BN_DECAY_CLIP = 0.99 HOSTNAME = socket.gethostname() NUM_CLASSES = 40 # Shapenet official train/test split if NORMAL_FLAG: 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=NORMAL_FLAG, batch_size=BATCH_SIZE, rotate=ROTATE_FLAG) TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', normal_channel=NORMAL_FLAG, 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, rotate=ROTATE_FLAG) 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)
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
HOSTNAME = socket.gethostname()
# 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=False,
modelnet10=True,
batch_size=BATCH_SIZE,
unsupervised=True)
TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH,
npoints=NUM_POINT,
split='test',
normal_channel=False,
modelnet10=True,
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,
