optimizer, outputs = create_optimizer(network, config["optimizer"])
###############################
# START TRAINING
#############################
# Load config
batch_size = config['model']['batch_size']
no_epoch = config["optimizer"]["no_epoch"]
# create a saver to store/load checkpoint
saver = tf.train.Saver()
# Retrieve only resnet variabes
if use_resnet:
resnet_saver = create_resnet_saver([network])
# CPU/GPU option
cpu_pool = Pool(args.no_thread, maxtasksperchild=1000)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_ratio)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
allow_soft_placement=True)) as sess:
sources = network.get_sources(sess)
logger.info("Sources: " + ', '.join(sources))
sess.run(tf.global_variables_initializer())
start_epoch = load_checkpoint(sess, saver, args, save_path)
best_val_err = 0
logger.info('Building optimizer..')
optimizer, outputs = create_multi_gpu_optimizer(networks, config, finetune=finetune)
#optimizer, outputs = create_optimizer(networks[0], config, finetune=finetune)
###############################
# START TRAINING
#############################
# create a saver to store/load checkpoint
saver = tf.train.Saver()
resnet_saver = None
# Retrieve only resnet variabes
if use_resnet:
resnet_saver = create_resnet_saver(networks)
# CPU/GPU option
cpu_pool = Pool(args.no_thread, maxtasksperchild=1000)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_ratio)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True)) as sess:
# retrieve incoming sources
sources = networks[0].get_sources(sess)
scope_names = ['tower_{}/{}'.format(i, network.scope_name) for i, network in enumerate(networks)]
logger.info("Sources: " + ', '.join(sources))