def generate_test(kitti_dir, is_hypotheses=True):
for sequence in os.listdir(kitti_dir):
# sequence = '2011_09_28_drive_0039_sync'
print("Processing %s" % sequence)
sequence_dir = os.path.join(kitti_dir, sequence)
detection_dir = os.path.join(sequence_dir, 'det')
if not os.path.isdir(sequence_dir):
continue
if is_hypotheses:
det_file = os.path.join(detection_dir, sequence + '_video_rgb_hypotheses.pickle')
output_file = os.path.join(detection_dir, sequence + '_video_rgb_hypotheses_3D.pickle')
result_dir = os.path.join(detection_dir, 'hypotheses_3D')
else:
det_file = os.path.join(detection_dir, sequence + '_video_rgb_detection.pickle')
output_file = os.path.join(detection_dir, sequence + '_video_rgb_detection_3D.pickle')
result_dir = os.path.join(detection_dir, 'detection_3D')
test_dataset = provider.FrustumDataset(npoints=NUM_POINT, split='val',
rotate_to_center=True, overwritten_data_path=det_file,
from_rgb_detection=True, one_hot=True,
is_hypotheses=is_hypotheses,)
test_from_video_detection(test_dataset, output_file, result_dir, sequence_dir)
parser.add_argument('--idx_path', default=None, help='filename of txt where each line is a data idx, used for rgb detection -- write <id>.txt for all frames. [default: None]')
parser.add_argument('--dump_result', action='store_true', help='If true, also dump results to .pickle file')
FLAGS = parser.parse_args()
# Set training configurations
BATCH_SIZE = FLAGS.batch_size
MODEL_PATH = FLAGS.model_path
GPU_INDEX = FLAGS.gpu
NUM_POINT = FLAGS.num_point
MODEL = importlib.import_module(FLAGS.model)
NUM_CLASSES = 2
NUM_CHANNEL = 4
# Load Frustum Datasets.
TEST_DATASET = provider.FrustumDataset(npoints=NUM_POINT, split='val',
rotate_to_center=True, overwritten_data_path=FLAGS.data_path,
from_rgb_detection=FLAGS.from_rgb_detection, one_hot=True)
def get_session_and_ops(batch_size, num_point):
''' Define model graph, load model parameters,
create session and return session handle and tensors
'''
with tf.Graph().as_default():
with tf.device('/gpu:'+str(GPU_INDEX)):
pointclouds_pl, one_hot_vec_pl, labels_pl, centers_pl, \
heading_class_label_pl, heading_residual_label_pl, \
size_class_label_pl, size_residual_label_pl = \
MODEL.placeholder_inputs(batch_size, num_point)
is_training_pl = tf.placeholder(tf.bool, shape=())
end_points = MODEL.get_model(pointclouds_pl, one_hot_vec_pl,
is_training_pl)
os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
os.system('cp %s %s' % (os.path.join(BASE_DIR, 'train.py'), LOG_DIR))
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS) + '\n')
# BN_INIT_DECAY = 0.5
# BN_DECAY_DECAY_RATE = 0.5
# BN_DECAY_DECAY_STEP = float(DECAY_STEP)
# BN_DECAY_CLIP = 0.99
# Load Frustum Datasets. Use default data paths.
if FLAGS.dataset == 'kitti':
TRAIN_DATASET = provider.FrustumDataset(
npoints=NUM_POINT,
split=FLAGS.train_sets,
rotate_to_center=True,
random_flip=True,
random_shift=True,
one_hot=True,
overwritten_data_path='kitti/frustum_' + FLAGS.objtype + '_' +
FLAGS.train_sets + '.pickle')
TEST_DATASET = provider.FrustumDataset(
npoints=NUM_POINT,
split=FLAGS.val_sets,
rotate_to_center=True,
one_hot=True,
overwritten_data_path='kitti/frustum_' + FLAGS.objtype + '_' +
FLAGS.val_sets + '.pickle')
elif FLAGS.dataset == 'nuscenes2kitti':
SENSOR = FLAGS.sensor
overwritten_data_path_prefix = 'nuscenes2kitti/frustum_' + FLAGS.objtype + '_' + SENSOR + '_'
TRAIN_DATASET = provider.FrustumDataset(
MODEL = importlib.import_module(FLAGS.model) # import network module
MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py')
LOG_DIR = FLAGS.log_dir
if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
os.system('cp %s %s' % (os.path.join(BASE_DIR, 'train.py'), LOG_DIR))
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
# Load Frustum Datasets. Use default data paths.
TRAIN_DATASET = provider.FrustumDataset(npoints=NUM_POINT, split='train',
rotate_to_center=True, random_flip=True, random_shift=True, one_hot=True)
TEST_DATASET = provider.FrustumDataset(npoints=NUM_POINT, split='val',
rotate_to_center=True, one_hot=True)
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)
pathsplit[4], pathsplit[5],
'results_train_eval_190/')
if not os.path.exists(OUTPUT_FILE):
os.mkdir(OUTPUT_FILE)
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
# Load Frustum Datasets. Use default data paths.
#TRAIN_DATASET = provider.FrustumDataset(npoints=NUM_POINT, split='train',res=0,
#rotate_to_center=False, random_flip=False, random_shift=True, one_hot=True)
EVAL_DATASET_224 = provider.FrustumDataset(npoints=NUM_POINT,
database="KITTI",
split='val',
res="224",
rotate_to_center=True,
one_hot=True)
EVAL_DATASET_704 = provider.FrustumDataset(npoints=NUM_POINT,
database="KITTI",
split='val',
res="704",
rotate_to_center=True,
one_hot=True)
#TEST_DATASET_224 = provider.FrustumDataset('pc_radar_2','KITTI_2',npoints=NUM_POINT, split='test',rotate_to_center=False, one_hot=True,all_batches = True, translate_radar_center=False, store_data=True, proposals_3 =False ,no_color=True)
TEST_DATASET_224 = provider.FrustumDataset(npoints=NUM_POINT,
database="KITTI_2",
split='test',
os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
os.system('cp %s %s' % (os.path.join(BASE_DIR, 'train.py'), LOG_DIR))
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS) + '\n')
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
# Load Frustum Datasets. Use default data paths.
TRAIN_DATASET = provider.FrustumDataset(
npoints=NUM_POINT,
split='train',
overwritten_data_path=
'/data/ssd/public/jlliu/frustum-pointnets/avod_prop/frustum_caronly_train%s.pickle'
% FLAGS.pickle,
rotate_to_center=True,
random_flip=False,
random_shift=True,
extra_feature=True)
TEST_DATASET = provider.FrustumDataset(
npoints=NUM_POINT,
split='val',
overwritten_data_path=
'/data/ssd/public/jlliu/frustum-pointnets/avod_prop/frustum_caronly_val%s.pickle'
% FLAGS.pickle,
rotate_to_center=True,
extra_feature=True)
def log_string(out_str):
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
# Load Frustum Datasets.
if FLAGS.two_frustum:
data_path_train = os.path.join(ROOT_DIR,'kitti/'+FLAGS.frustum_folder+'/two_frustum_carpedcyc_train.pickle')
data_path_val = os.path.join(ROOT_DIR,'kitti/'+FLAGS.frustum_folder+'/two_frustum_carpedcyc_val.pickle')
else:
data_path_train = os.path.join(ROOT_DIR,'kitti/'+FLAGS.frustum_folder+'/frustum_carpedcyc_train.pickle')
data_path_val = os.path.join(ROOT_DIR,'kitti/'+FLAGS.frustum_folder+'/frustum_carpedcyc_val.pickle')
print("Loading train frustums from: ",data_path_train)
print("Loading val frustums from: ",data_path_val)
TRAIN_DATASET = provider.FrustumDataset(overwritten_data_path= data_path_train,npoints=NUM_POINT, split='train',rotate_to_center=True, random_flip=True, random_shift=True, one_hot=True)
TEST_DATASET = provider.FrustumDataset(overwritten_data_path= data_path_val,npoints=NUM_POINT, split='val',rotate_to_center=True, one_hot=True)
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)
learing_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
else:
lstm_parameters['flags']['one_branch'] = False
lstm_parameters['flags']['only_whl'] = FLAGS.only_whl
lstm_parameters['flags']['temp_attention'] = FLAGS.temp_attention
lstm_parameters['flags']['add_center'] = FLAGS.add_center
lstm_parameters['flags']['output_attention'] = FLAGS.output_attention
lstm_parameters['flags']['time_indication'] = FLAGS.time_indication
lstm_parameters['flags']['dropout'] = FLAGS.dropout
lstm_parameters['flags']['random_time_sampling'] = False
lstm_parameters['random_n'] = FLAGS.random_n
# Load Frustum Datasets.
if VKITTI:
# Load Virtual KITTI dataset
overwritten_val_data_path = os.path.join(ROOT_DIR, 'kitti/frustum_caronly_vkitti_val.pickle')
TEST_DATASET = provider.FrustumDataset(npoints=NUM_POINT, split='val',
rotate_to_center=True, one_hot=True,
overwritten_data_path=overwritten_val_data_path,
tracks=FLAGS.tracking,tau=FLAGS.tau,feat_len=feat_len)
elif TRACKING:
# Load KITTI tracking dataset
if FLAGS.from_rgb_detection:
overwritten_val_data_path = os.path.join(ROOT_DIR, 'kitti/tracking_val_rgb_detection.pickle')
else:
overwritten_val_data_path = os.path.join(ROOT_DIR, 'kitti/frustum_carpedcyc_tracking_val.pickle')
TEST_DATASET = provider.FrustumDataset(npoints=NUM_POINT, split='val',
rotate_to_center=True, one_hot=True,
from_rgb_detection=FLAGS.from_rgb_detection,
overwritten_data_path=FLAGS.data_path,
tracks=FLAGS.tracking,tau=FLAGS.tau,feat_len=feat_len)
else:
# Load KITTI dataset
TEST_DATASET = provider.FrustumDataset(npoints=NUM_POINT, split='val',
BASE_LEARNING_RATE = FLAGS.learning_rate
GPU_INDEX = FLAGS.gpu
MOMENTUM = FLAGS.momentum
OPTIMIZER = FLAGS.optimizer
DECAY_STEP = FLAGS.decay_step
DECAY_RATE = FLAGS.decay_rate
NUM_CHANNEL = 3 if FLAGS.no_intensity else 4 # point feature channel
NUM_CLASSES = 2 # segmentation has two classes
"""
MODEL = importlib.import_module(FLAGS.model) # import network module
MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py')
LOG_DIR = FLAGS.log_dir
if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
os.system('cp %s %s' % (os.path.join(BASE_DIR, 'train.py'), LOG_DIR))
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
"""
# Load Frustum Datasets. Use default data paths.
#TRAIN_DATASET = provider.FrustumDataset(npoints=NUM_POINT, split='train',
# rotate_to_center=True, random_flip=False, random_shift=True, one_hot=True)
TEST_DATASET = provider.FrustumDataset(npoints=NUM_POINT,
split='val',
rotate_to_center=True,
one_hot=True)
else:
print("Wrong model parameter.")
exit(0)
if 'fusion' in MODEL_FILE:
with_image = True
else:
with_image = False
provider = import_from_file(DATA_FILE)
TRAIN_DATASET = provider.FrustumDataset(npoints=NUM_POINT,
split=TRAIN_SETS,
rotate_to_center=True,
random_flip=True,
random_shift=True,
one_hot=True,
overwritten_data_path=TRAIN_FILE,
gen_ref=gen_ref,
with_image=with_image)
TEST_DATASET = provider.FrustumDataset(npoints=NUM_POINT,
split=TEST_SETS,
rotate_to_center=True,
one_hot=True,
overwritten_data_path=TEST_FILE,
gen_ref=gen_ref,
with_image=with_image)
train_dataloader = DataLoader(TRAIN_DATASET,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=NUM_WORKERS,
if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
os.system('cp %s %s' % (os.path.join(BASE_DIR, 'extract_mistakes.py'), LOG_DIR))
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_extract_mistakes.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
# Load Frustum Datasets.
if FLAGS.two_frustum:
data_path_val = os.path.join(ROOT_DIR,'kitti/'+FLAGS.frustum_folder+'/two_frustum_carpedcyc_val.pickle')
else:
data_path_val = os.path.join(ROOT_DIR,'kitti/'+FLAGS.frustum_folder+'/frustum_carpedcyc_val.pickle')
print("Loading val frustums from: ",data_path_val)
TEST_DATASET = provider.FrustumDataset(overwritten_data_path= data_path_val,npoints=NUM_POINT, split='val',rotate_to_center=True, one_hot=True)
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
def main():
''' Main function for evaluation and determining mistakes. '''
with tf.Graph().as_default():
with tf.device('/gpu:'+str(GPU_INDEX)):
pointclouds_pl, one_hot_vec_pl, labels_pl, centers_pl, \
heading_class_label_pl, heading_residual_label_pl, \
size_class_label_pl, size_residual_label_pl = \
MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
if __name__ == '__main__':
# Load Frustum Datasets.
if 'frustum_pointnet' in MODEL_FILE:
gen_ref = False
elif 'frustum_convnet' in MODEL_FILE:
gen_ref = True
else:
print("Wrong model parameter.")
exit(0)
provider = import_from_file(DATA_FILE)
TEST_DATASET = provider.FrustumDataset(npoints=NUM_POINT,
split=TEST_SETS,
rotate_to_center=True,
one_hot=True,
overwritten_data_path=TEST_FILE,
gen_ref=gen_ref)
test_dataloader = DataLoader(TEST_DATASET,
batch_size=TEST_BATCH_SIZE,
shuffle=False,
num_workers=NUM_WORKERS,
pin_memory=True)
# Model
if 'frustum_pointnets_v1' in MODEL_FILE:
from frustum_pointnets_v1 import FrustumPointNetv1
model = FrustumPointNetv1(n_classes=NUM_CLASSES,
n_channel=NUM_CHANNEL).cuda()
elif 'frustum_convnet_v1' in MODEL_FILE:
ROOT_DIR, 'kitti/frustum_caronly_vkitti_train.pickle')
overwritten_val_data_path = os.path.join(
ROOT_DIR, 'kitti/frustum_caronly_vkitti_val.pickle')
if GROUND:
overwritten_train_data_path = os.path.join(
ROOT_DIR, 'kitti/ground_caronly_vkitti_train.pickle')
overwritten_val_data_path = os.path.join(
ROOT_DIR, 'kitti/ground_caronly_vkitti_val.pickle')
TRAIN_DATASET = provider.FrustumDataset(
npoints=NUM_POINT,
split='train',
rotate_to_center=True,
random_flip=True,
random_shift=True,
one_hot=True,
overwritten_data_path=overwritten_train_data_path,
tracks=FLAGS.tracks,
tau=FLAGS.tau,
feat_len=feat_len)
TEST_DATASET = provider.FrustumDataset(
npoints=NUM_POINT,
split='val',
rotate_to_center=True,
one_hot=True,
overwritten_data_path=overwritten_val_data_path,
tracks=FLAGS.tracks,
tau=FLAGS.tau,
feat_len=feat_len)
elif TRACKING:
os.system('cp %s %s' % (os.path.join(BASE_DIR, 'train.py'), LOG_DIR))
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS) + '\n')
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
OUTPUT_FILE = os.path.join(LOG_DIR, 'results/')
# Load Frustum Datasets. Use default data paths.
TRAIN_DATASET = provider.FrustumDataset(npoints=NUM_POINT,
database='KITTI',
split='train',
res=0,
rotate_to_center=True,
random_flip=False,
random_shift=True,
one_hot=True)
EVAL_DATASET_224 = provider.FrustumDataset(npoints=NUM_POINT,
database='KITTI',
split='val',
res="224",
rotate_to_center=True,
one_hot=True)
EVAL_DATASET_704 = provider.FrustumDataset(npoints=NUM_POINT,
database='KITTI',
split='val',
res="704",
rotate_to_center=True,
one_hot=True)
