def __init__(self, n, configs):
self.node = n
#self.pub = rospy.Publisher('tracking_result', Object_with_id, queue_size=10)
self.model = create_model(configs)
self.model.print_network()
print('\n\n' + '-*=' * 30 + '\n\n')
assert os.path.isfile(configs.pretrained_path), "No file at {}".format(configs.pretrained_path)
self.model.load_state_dict(torch.load(configs.pretrained_path,map_location='cuda:0'))
configs.device = torch.device('cpu' if configs.no_cuda else 'cuda:{}'.format(configs.gpu_idx))
self.model = self.model.to(device=configs.device)
self.model.eval()
rospy.Subscriber("detect_object", PointCloud2, self.callback) # your cloud topic name
rospy.spin()
def __init__(self):
self.configs = parse_test_configs()
self.configs.distributed = False # For testing
host = self.configs.host
port = self.configs.port
print("> server start.... ")
socketer = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# set the port reuesd
socketer.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
socketer.bind((host, port))
# define the max connection
socketer.listen(10)
self.sock = socketer
# Model Initial
self.model = create_model(self.configs)
self.model.print_network()
print('\n\n' + '-*=' * 30 + '\n\n')
device_string = 'cpu' if self.configs.no_cuda else 'cuda:{}'.format(
self.configs.gpu_idx)
assert os.path.isfile(self.configs.pretrained_path), "No file at {}".format(
self.configs.pretrained_path)
# model.load_state_dict(torch.load(configs.pretrained_path))
self.model.load_state_dict(torch.load(
self.configs.pretrained_path, map_location=device_string))
# configs.device = torch.device('cpu' if configs.no_cuda else 'cuda:{}'.format(configs.gpu_idx))
self.configs.device = torch.device(device_string)
self.model = self.model.to(device=self.configs.device)
self.model.eval()
# self.test_dataloader = create_test_dataloader(self.configs)
# self.test_dataloader_iter = self.test_dataloader._get_iterator()
self.test_dataset = create_test_dataset(self.configs)
self.batch_idx = 0
self.need_create_window = True
def main_worker(gpu_idx, configs):
configs.gpu_idx = gpu_idx
if configs.gpu_idx is not None:
print("Use GPU: {} for training".format(configs.gpu_idx))
configs.device = torch.device('cuda:{}'.format(configs.gpu_idx))
if configs.distributed:
if configs.dist_url == "env://" and configs.rank == -1:
configs.rank = int(os.environ["RANK"])
if configs.multiprocessing_distributed:
configs.rank = configs.rank * configs.ngpus_per_node + gpu_idx
dist.init_process_group(backend=configs.dist_backend,
init_method=configs.dist_url,
world_size=configs.world_size,
rank=configs.rank)
configs.is_master_node = (not configs.distributed) or (
configs.distributed and (configs.rank % configs.ngpus_per_node == 0))
# model
model = create_model(configs)
model = make_data_parallel(model, configs)
if configs.is_master_node:
num_parameters = get_num_parameters(model)
print('number of trained parameters of the model: {}'.format(
num_parameters))
if configs.pretrained_path is not None:
model = load_pretrained_model(model, configs.pretrained_path, gpu_idx,
configs.overwrite_global_2_local)
# Load dataset
test_loader = create_test_dataloader(configs)
test(test_loader, model, configs)
detections = post_processing(detections, configs.num_classes, configs.down_ratio, configs.peak_thresh)
t2 = time_synchronized()
detections = detections[0] # only first batch
# Draw prediction in the image
bev_map = (bev_maps.squeeze().permute(1, 2, 0).numpy() * 255).astype(np.uint8)
bev_map = cv2.resize(bev_map, (cnf.BEV_WIDTH, cnf.BEV_HEIGHT))
bev_map = draw_predictions(bev_map, detections.copy(), configs.num_classes)
bev_map = cv2.rotate(bev_map, cv2.ROTATE_180)
cv2.imshow("BEV", bev_map)
print('\tDone testing in time: {:.1f}ms, speed {:.2f}FPS'.format((t2 - t1) * 1000,1 / (t2 - t1)))
if __name__ == '__main__':
stereo_args= parse_config()
configs, args = parse_test_configs()
model = create_model(configs)
print('\n\n' + '-*=' * 30 + '\n\n')
assert os.path.isfile(configs.pretrained_path), "No file at {}".format(configs.pretrained_path)
model.load_state_dict(torch.load(configs.pretrained_path, map_location='cpu'))
print('Loaded weights from {}\n'.format(configs.pretrained_path))
configs.device = torch.device('cpu' if configs.no_cuda else 'cuda:0')
model = model.to(device=configs.device)
model.eval()
# ============================= Sersy Edit =======================================
stereo_model = Stereo_Depth_Estimation(stereo_args,None)
dataset_root = os.path.join(configs.dataset_dir, 'testing')
print(configs.dataset_dir)
KITTI_stereo = KittiDataset(dataset_root, stereo_mode=True)
KITTI = KittiDataset(dataset_root, stereo_mode=False)
def main_worker(gpu_idx, configs):
configs.gpu_idx = gpu_idx
if configs.gpu_idx is not None:
print("Use GPU: {} for training".format(configs.gpu_idx))
configs.device = torch.device('cuda:{}'.format(configs.gpu_idx))
if configs.distributed:
if configs.dist_url == "env://" and configs.rank == -1:
configs.rank = int(os.environ["RANK"])
if configs.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
configs.rank = configs.rank * configs.ngpus_per_node + gpu_idx
dist.init_process_group(backend=configs.dist_backend,
init_method=configs.dist_url,
world_size=configs.world_size,
rank=configs.rank)
configs.is_master_node = (not configs.distributed) or (
configs.distributed and (configs.rank % configs.ngpus_per_node == 0))
if configs.is_master_node:
logger = Logger(configs.logs_dir, configs.saved_fn)
logger.info('>>> Created a new logger')
logger.info('>>> configs: {}'.format(configs))
tb_writer = SummaryWriter(
log_dir=os.path.join(configs.logs_dir, 'tensorboard'))
else:
logger = None
tb_writer = None
# model
model = create_model(configs)
# Data Parallel
model = make_data_parallel(model, configs)
# Freeze model
model = freeze_model(model, configs.freeze_modules_list)
if configs.is_master_node:
num_parameters = get_num_parameters(model)
logger.info('number of trained parameters of the model: {}'.format(
num_parameters))
optimizer = create_optimizer(configs, model)
lr_scheduler = create_lr_scheduler(optimizer, configs)
best_val_loss = np.inf
earlystop_count = 0
is_best = False
# optionally load weight from a checkpoint
if configs.pretrained_path is not None:
model = load_pretrained_model(model, configs.pretrained_path, gpu_idx,
configs.overwrite_global_2_local)
if logger is not None:
logger.info('loaded pretrained model at {}'.format(
configs.pretrained_path))
# optionally resume from a checkpoint
if configs.resume_path is not None:
checkpoint = resume_model(configs.resume_path, configs.arch,
configs.gpu_idx)
if hasattr(model, 'module'):
model.module.load_state_dict(checkpoint['state_dict'])
else:
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
best_val_loss = checkpoint['best_val_loss']
earlystop_count = checkpoint['earlystop_count']
configs.start_epoch = checkpoint['epoch'] + 1
if logger is not None:
logger.info(">>> Loading dataset & getting dataloader...")
# Create dataloader
train_loader, val_loader, train_sampler = create_train_val_dataloader(
configs)
test_loader = create_test_dataloader(configs)
if logger is not None:
logger.info('number of batches in train set: {}'.format(
len(train_loader)))
if val_loader is not None:
logger.info('number of batches in val set: {}'.format(
len(val_loader)))
logger.info('number of batches in test set: {}'.format(
len(test_loader)))
if configs.evaluate:
assert val_loader is not None, "The validation should not be None"
val_loss = evaluate_one_epoch(val_loader, model,
configs.start_epoch - 1, configs, logger)
print('Evaluate, val_loss: {}'.format(val_loss))
return
for epoch in range(configs.start_epoch, configs.num_epochs + 1):
# Get the current learning rate
for param_group in optimizer.param_groups:
lr = param_group['lr']
if logger is not None:
logger.info('{}'.format('*-' * 40))
logger.info('{} {}/{} {}'.format('=' * 35, epoch,
configs.num_epochs, '=' * 35))
logger.info('{}'.format('*-' * 40))
logger.info('>>> Epoch: [{}/{}] learning rate: {:.2e}'.format(
epoch, configs.num_epochs, lr))
if configs.distributed:
train_sampler.set_epoch(epoch)
# train for one epoch
train_loss = train_one_epoch(train_loader, model, optimizer, epoch,
configs, logger)
loss_dict = {'train': train_loss}
if not configs.no_val:
val_loss = evaluate_one_epoch(val_loader, model, epoch, configs,
logger)
is_best = val_loss <= best_val_loss
best_val_loss = min(val_loss, best_val_loss)
loss_dict['val'] = val_loss
if not configs.no_test:
test_loss = evaluate_one_epoch(test_loader, model, epoch, configs,
logger)
loss_dict['test'] = test_loss
# Write tensorboard
if tb_writer is not None:
tb_writer.add_scalars('Loss', loss_dict, epoch)
# Save checkpoint
if configs.is_master_node and (is_best or (
(epoch % configs.checkpoint_freq) == 0)):
saved_state = get_saved_state(model, optimizer, lr_scheduler,
epoch, configs, best_val_loss,
earlystop_count)
save_checkpoint(configs.checkpoints_dir, configs.saved_fn,
saved_state, is_best, epoch)
# Check early stop training
if configs.earlystop_patience is not None:
earlystop_count = 0 if is_best else (earlystop_count + 1)
print_string = ' |||\t earlystop_count: {}'.format(earlystop_count)
if configs.earlystop_patience <= earlystop_count:
print_string += '\n\t--- Early stopping!!!'
break
else:
print_string += '\n\t--- Continue training..., earlystop_count: {}'.format(
earlystop_count)
if logger is not None:
logger.info(print_string)
# Adjust learning rate
if configs.lr_type == 'plateau':
assert (not configs.no_val
), "Only use plateau when having validation set"
lr_scheduler.step(val_loss)
else:
lr_scheduler.step()
if tb_writer is not None:
tb_writer.close()
if configs.distributed:
cleanup()
def main_worker(gpu_idx, configs):
configs.gpu_idx = gpu_idx
if configs.gpu_idx is not None:
print("Use GPU: {} for training".format(configs.gpu_idx))
configs.device = torch.device('cuda:{}'.format(configs.gpu_idx))
if configs.distributed:
if configs.dist_url == "env://" and configs.rank == -1:
configs.rank = int(os.environ["RANK"])
if configs.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
configs.rank = configs.rank * configs.ngpus_per_node + gpu_idx
dist.init_process_group(backend=configs.dist_backend,
init_method=configs.dist_url,
world_size=configs.world_size,
rank=configs.rank)
configs.is_master_node = (not configs.distributed) or (
configs.distributed and (configs.rank % configs.ngpus_per_node == 0))
if configs.is_master_node:
logger = Logger(configs.logs_dir, configs.saved_fn)
logger.info('>>> Created a new logger')
logger.info('>>> configs: {}'.format(configs))
tb_writer = SummaryWriter(
log_dir=os.path.join(configs.logs_dir, 'tensorboard'))
else:
logger = None
tb_writer = None
# model
model = create_model(configs)
# load weight from a checkpoint
if configs.pretrained_path is not None:
assert os.path.isfile(
configs.pretrained_path), "=> no checkpoint found at '{}'".format(
configs.pretrained_path)
model.load_weights(weightfile=configs.pretrained_path)
if logger is not None:
logger.info('loaded pretrained model at {}'.format(
configs.pretrained_path))
# resume weights of model from a checkpoint
if configs.resume_path is not None:
assert os.path.isfile(
configs.resume_path), "=> no checkpoint found at '{}'".format(
configs.resume_path)
model.load_weights(weightfile=configs.resume_path)
if logger is not None:
logger.info('resume training model from checkpoint {}'.format(
configs.pretrained_path))
# Data Parallel
model = make_data_parallel(model, configs)
# Make sure to create optimizer after moving the model to cuda
optimizer = create_optimizer(configs, model)
lr_scheduler = create_lr_scheduler(optimizer, configs)
# resume optimizer, lr_scheduler from a checkpoint
if configs.resume_path is not None:
utils_path = configs.resume_path.replace('Model_', 'Utils_')
assert os.path.isfile(
utils_path), "=> no checkpoint found at '{}'".format(utils_path)
utils_state_dict = torch.load(utils_path,
map_location='cuda:{}'.format(
configs.gpu_idx))
optimizer.load_state_dict(utils_state_dict['optimizer'])
lr_scheduler.load_state_dict(utils_state_dict['lr_scheduler'])
configs.start_epoch = utils_state_dict['epoch'] + 1
if configs.is_master_node:
num_parameters = get_num_parameters(model)
logger.info('number of trained parameters of the model: {}'.format(
num_parameters))
if logger is not None:
logger.info(">>> Loading dataset & getting dataloader...")
# Create dataloader
train_loader, val_loader, train_sampler = create_train_val_dataloader(
configs)
if logger is not None:
logger.info('number of batches in train set: {}'.format(
len(train_loader)))
if val_loader is not None:
logger.info('number of batches in val set: {}'.format(
len(val_loader)))
if configs.evaluate:
assert val_loader is not None, "The validation should not be None"
eval_metrics = evaluate_one_epoch(val_loader, model,
configs.start_epoch - 1, configs,
logger)
precision, recall, AP, f1, ap_class = eval_metrics
print(
'Evaluate - precision: {}, recall: {}, AP: {}, f1: {}, ap_class: {}'
.format(precision, recall, AP, f1, ap_class))
return
for epoch in range(configs.start_epoch, configs.num_epochs + 1):
if logger is not None:
logger.info('{}'.format('*-' * 40))
logger.info('{} {}/{} {}'.format('=' * 35, epoch,
configs.num_epochs, '=' * 35))
logger.info('{}'.format('*-' * 40))
logger.info('>>> Epoch: [{}/{}]'.format(epoch, configs.num_epochs))
if configs.distributed:
train_sampler.set_epoch(epoch)
# train for one epoch
train_one_epoch(train_loader, model, optimizer, lr_scheduler, epoch,
configs, logger, tb_writer)
if not configs.no_val:
precision, recall, AP, f1, ap_class = evaluate_one_epoch(
val_loader, model, epoch, configs, logger)
val_metrics_dict = {
'precision': precision,
'recall': recall,
'AP': AP,
'f1': f1,
'ap_class': ap_class
}
if tb_writer is not None:
tb_writer.add_scalars('Validation', val_metrics_dict, epoch)
# Save checkpoint
if configs.is_master_node and ((epoch % configs.checkpoint_freq) == 0):
model_state_dict, utils_state_dict = get_saved_state(
model, optimizer, lr_scheduler, epoch, configs)
save_checkpoint(configs.checkpoints_dir, configs.saved_fn,
model_state_dict, utils_state_dict, epoch)
if tb_writer is not None:
tb_writer.close()
if configs.distributed:
cleanup()
def main_worker(gpu_idx, configs):
configs.gpu_idx = gpu_idx
configs.device = torch.device('cpu' if configs.gpu_idx is None else 'cuda:{}'.format(configs.gpu_idx))
if configs.distributed:
if configs.dist_url == "env://" and configs.rank == -1:
configs.rank = int(os.environ["RANK"])
if configs.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
configs.rank = configs.rank * configs.ngpus_per_node + gpu_idx
dist.init_process_group(backend=configs.dist_backend, init_method=configs.dist_url,
world_size=configs.world_size, rank=configs.rank)
configs.subdivisions = int(64 / configs.batch_size / configs.ngpus_per_node)
else:
configs.subdivisions = int(64 / configs.batch_size)
configs.is_master_node = (not configs.distributed) or (
configs.distributed and (configs.rank % configs.ngpus_per_node == 0))
if configs.is_master_node:
logger = Logger(configs.logs_dir, configs.saved_fn)
logger.info('>>> Created a new logger')
logger.info('>>> configs: {}'.format(configs))
tb_writer = SummaryWriter(log_dir=os.path.join(configs.logs_dir, 'tensorboard'))
else:
logger = None
tb_writer = None
# model
model = create_model(configs)
# load weight from a checkpoint
if configs.pretrained_path is not None:
assert os.path.isfile(configs.pretrained_path), "=> no checkpoint found at '{}'".format(configs.pretrained_path)
model.load_state_dict(torch.load(configs.pretrained_path, map_location='cpu'))
if logger is not None:
logger.info('loaded pretrained model at {}'.format(configs.pretrained_path))
# resume weights of model from a checkpoint
if configs.resume_path is not None:
assert os.path.isfile(configs.resume_path), "=> no checkpoint found at '{}'".format(configs.resume_path)
model.load_state_dict(torch.load(configs.resume_path, map_location='cpu'))
if logger is not None:
logger.info('resume training model from checkpoint {}'.format(configs.resume_path))
# Data Parallel
model = make_data_parallel(model, configs)
# Make sure to create optimizer after moving the model to cuda
optimizer = create_optimizer(configs, model)
lr_scheduler = create_lr_scheduler(optimizer, configs)
configs.step_lr_in_epoch = False if configs.lr_type in ['multi_step', 'cosin', 'one_cycle'] else True
# resume optimizer, lr_scheduler from a checkpoint
if configs.resume_path is not None:
utils_path = configs.resume_path.replace('Model_', 'Utils_')
assert os.path.isfile(utils_path), "=> no checkpoint found at '{}'".format(utils_path)
utils_state_dict = torch.load(utils_path, map_location='cuda:{}'.format(configs.gpu_idx))
optimizer.load_state_dict(utils_state_dict['optimizer'])
lr_scheduler.load_state_dict(utils_state_dict['lr_scheduler'])
configs.start_epoch = utils_state_dict['epoch'] + 1
if configs.is_master_node:
num_parameters = get_num_parameters(model)
logger.info('number of trained parameters of the model: {}'.format(num_parameters))
if logger is not None:
logger.info(">>> Loading dataset & getting dataloader...")
# Create dataloader
train_dataloader, train_sampler = create_train_dataloader(configs)
if logger is not None:
logger.info('number of batches in training set: {}'.format(len(train_dataloader)))
if configs.evaluate:
val_dataloader = create_val_dataloader(configs)
val_loss = validate(val_dataloader, model, configs)
print('val_loss: {:.4e}'.format(val_loss))
return
for epoch in range(configs.start_epoch, configs.num_epochs + 1):
if logger is not None:
logger.info('{}'.format('*-' * 40))
logger.info('{} {}/{} {}'.format('=' * 35, epoch, configs.num_epochs, '=' * 35))
logger.info('{}'.format('*-' * 40))
logger.info('>>> Epoch: [{}/{}]'.format(epoch, configs.num_epochs))
if configs.distributed:
train_sampler.set_epoch(epoch)
# train for one epoch
train_one_epoch(train_dataloader, model, optimizer, lr_scheduler, epoch, configs, logger, tb_writer)
if (not configs.no_val) and (epoch % configs.checkpoint_freq == 0):
val_dataloader = create_val_dataloader(configs)
print('number of batches in val_dataloader: {}'.format(len(val_dataloader)))
val_loss = validate(val_dataloader, model, configs)
print('val_loss: {:.4e}'.format(val_loss))
if tb_writer is not None:
tb_writer.add_scalar('Val_loss', val_loss, epoch)
# Save checkpoint
if configs.is_master_node and ((epoch % configs.checkpoint_freq) == 0):
model_state_dict, utils_state_dict = get_saved_state(model, optimizer, lr_scheduler, epoch, configs)
save_checkpoint(configs.checkpoints_dir, configs.saved_fn, model_state_dict, utils_state_dict, epoch)
if not configs.step_lr_in_epoch:
lr_scheduler.step()
if tb_writer is not None:
tb_writer.add_scalar('LR', lr_scheduler.get_lr()[0], epoch)
if tb_writer is not None:
tb_writer.close()
if configs.distributed:
cleanup()
return configs
if __name__ == '__main__':
configs = parse_eval_configs()
configs.distributed = False # For evaluation
class_names = load_classes(configs.classnames_infor_path)
print(configs.iou_thresh)
voxel_generator = VoxelGeneratorV2(
voxel_size=list(configs.voxel_size),
point_cloud_range=list(configs.point_cloud_range),
max_num_points=configs.max_number_of_points_per_voxel,
max_voxels=20000)
model = create_model(configs, voxel_generator)
print('\n\n' + '-*=' * 30 + '\n\n')
assert os.path.isfile(configs.pretrained_path), "No file at {}".format(
configs.pretrained_path)
model.load_state_dict(
torch.load(configs.pretrained_path, map_location='cpu'))
print('Loaded weights from {}\n'.format(configs.pretrained_path))
configs.device = torch.device(
'cpu' if configs.no_cuda else 'cuda:{}'.format(configs.gpu_idx))
model = model.to(device=configs.device)
out_cap = None
model.eval()
def demo(configs):
video_loader = TTNet_Video_Loader(configs.video_path, configs.input_size, configs.num_frames_sequence)
result_filename = os.path.join(configs.save_demo_dir, 'results.txt')
frame_rate = video_loader.video_fps
if configs.save_demo_output:
configs.frame_dir = os.path.join(configs.save_demo_dir, 'frame')
if not os.path.isdir(configs.frame_dir):
os.makedirs(configs.frame_dir)
configs.device = torch.device('cuda:{}'.format(configs.gpu_idx))
# model
model = create_model(configs)
model.cuda()
assert configs.pretrained_path is not None, "Need to load the pre-trained model"
model = load_pretrained_model(model, configs.pretrained_path, configs.gpu_idx, configs.overwrite_global_2_local)
model.eval()
middle_idx = int(configs.num_frames_sequence / 2)
queue_frames = deque(maxlen=middle_idx + 1)
frame_idx = 0
w_original, h_original = 1920, 1080
w_resize, h_resize = 320, 128
w_ratio = w_original / w_resize
h_ratio = h_original / h_resize
with torch.no_grad():
for count, resized_imgs in video_loader:
# take the middle one
img = cv2.resize(resized_imgs[3 * middle_idx: 3 * (middle_idx + 1)].transpose(1, 2, 0), (w_original, h_original))
# Expand the first dim
resized_imgs = torch.from_numpy(resized_imgs).to(configs.device, non_blocking=True).float().unsqueeze(0)
t1 = time_synchronized()
pred_ball_global, pred_ball_local, pred_events, pred_seg = model.run_demo(resized_imgs)
t2 = time_synchronized()
prediction_global, prediction_local, prediction_seg, prediction_events = post_processing(
pred_ball_global, pred_ball_local, pred_events, pred_seg, configs.input_size[0],
configs.thresh_ball_pos_mask, configs.seg_thresh, configs.event_thresh)
prediction_ball_final = [
int(prediction_global[0] * w_ratio + prediction_local[0] - w_resize / 2),
int(prediction_global[1] * h_ratio + prediction_local[1] - h_resize / 2)
]
# Get infor of the (middle_idx + 1)th frame
if len(queue_frames) == middle_idx + 1:
frame_pred_infor = queue_frames.popleft()
seg_img = frame_pred_infor['seg'].astype(np.uint8)
ball_pos = frame_pred_infor['ball']
seg_img = cv2.resize(seg_img, (w_original, h_original))
ploted_img = plot_detection(img, ball_pos, seg_img, prediction_events)
ploted_img = cv2.cvtColor(ploted_img, cv2.COLOR_RGB2BGR)
if configs.show_image:
cv2.imshow('ploted_img', ploted_img)
cv2.waitKey(10)
if configs.save_demo_output:
cv2.imwrite(os.path.join(configs.frame_dir, '{:06d}.jpg'.format(frame_idx)), ploted_img)
frame_pred_infor = {
'seg': prediction_seg,
'ball': prediction_ball_final
}
queue_frames.append(frame_pred_infor)
frame_idx += 1
print('Done frame_idx {} - time {:.3f}s'.format(frame_idx, t2 - t1))
if configs.output_format == 'video':
output_video_path = os.path.join(configs.save_demo_dir, 'result.mp4')
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -b 5000k -c:v mpeg4 {}'.format(
os.path.join(configs.frame_dir), output_video_path)
os.system(cmd_str)
