gqcnn_config = train_config['gqcnn_config']
def get_elapsed_time(time_in_seconds):
""" Helper function to get elapsed time """
if time_in_seconds < 60:
return '%.1f seconds' % (time_in_seconds)
elif time_in_seconds < 3600:
return '%.1f minutes' % (time_in_seconds / 60)
else:
return '%.1f hours' % (time_in_seconds / 3600)
###Possible Use-Cases###
# Training from Scratch
start_time = time.time()
gqcnn = GQCNN(gqcnn_config)
sgdOptimizer = SGDOptimizer(gqcnn, train_config)
with gqcnn.get_tf_graph().as_default():
sgdOptimizer.optimize()
logging.info('Total Training Time:' + str(get_elapsed_time(time.time() - start_time)))
# Prediction
"""
start_time = time.time()
model_dir = '/home/user/Data/models/grasp_quality/model_ewlohgukns'
gqcnn = GQCNN.load(model_dir)
output = gqcnn.predict(images, poses)
pred_p_success = output[:,1]
gqcnn.close_session()
logging.info('Total Prediction Time:' + str(get_elapsed_time(time.time() - start_time)))
"""
# open train config
train_config = YamlConfig(config_filename)
train_config['seed'] = seed
train_config['tensorboard_port'] = tensorboard_port
gqcnn_params = train_config['gqcnn']
# create a unique output folder based on the date and time
if save_datetime:
# create output dir
unique_name = time.strftime("%Y%m%d-%H%M%S")
output_dir = os.path.join(output_dir, unique_name)
utils.mkdir_safe(output_dir)
# set visible devices
if 'gpu_list' in train_config:
gqcnn_utils.set_cuda_visible_devices(train_config['gpu_list'])
# fine-tune the network
start_time = time.time()
gqcnn = GQCNN(gqcnn_params)
optimizer = GQCNNOptimizer(gqcnn,
dataset_dir,
split_name,
output_dir,
train_config,
name=name)
optimizer.finetune(model_dir)
logging.info('Total Fine-tuning Time:' +
str(utils.get_elapsed_time(time.time() - start_time)))