Python parse_device_name Examples

Example #1

# model load & save configs
flags.DEFINE_string('summaries_dir',
                    'volume/TF_Logs/TensorflowModelZoo/resnet50_deform/',
                    'where to store summary log')

flags.DEFINE_string(
    'pretrained_ckpts',
    '/home/chenyifeng/TF_Models/ptrain/ILSVRC/ResNet_50_V1_GN/model.ckpt',
    'where to load pretrained model')

flags.DEFINE_string('last_ckpt', None, 'where to load last saved model')

FLAGS = flags.FLAGS

# config devices
store_device = parse_device_name(FLAGS.store_device)
run_device = parse_device_name(FLAGS.run_device)
config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)

if not 'CPU' in FLAGS.run_device:
    GPU_NUMS = len(FLAGS.run_device.split(','))
    print(
        '====================================Deploying Model on {} GPUs===================================='
        .format(GPU_NUMS))
    os.environ['CUDA_VISIBLE_DEVICES'] = ''.join(FLAGS.run_device)
    config.gpu_options.allow_growth = FLAGS.allow_growth
else:
    GPU_NUMS = 1
    print(
        '====================================Deploying Model on CPU===================================='
    )

Example #2

File: multi_gpu_train.py Project: jiabaocui/SEGS

                           '/home/chenyifeng/TF_Models/atrain/SEGS/fcn/mgpu',
                           'where to load last saved model')

tf.app.flags.DEFINE_string('next_ckpt',
                           '/home/chenyifeng/TF_Models/atrain/SEGS/fcn/mgpu',
                           'where to store current model')

tf.app.flags.DEFINE_integer('save_per_step', 1000, 'save model per xxx steps')

FLAGS = tf.app.flags.FLAGS

if (FLAGS.reshape_height is None
        or FLAGS.reshape_weight is None) and FLAGS.batch_size != 1:
    assert 0, 'Can' 't Stack Images Of Different Shapes, Please Speicify Reshape Size!'

store_device = parse_device_name(FLAGS.store_device)
run_device = parse_device_name(FLAGS.run_device)

config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
if FLAGS.run_device in '01234567':
    print('Deploying Model on {} GPU Card'.format(''.join(FLAGS.run_device)))
    # os.environ['CUDA_VISIBLE_DEVICES'] = ''.join(FLAGS.run_device)
    config.gpu_options.allow_growth = FLAGS.allow_growth
    config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_fraction
else:
    print('Deploying Model on CPU')

weight_reg = regularizer(mode=FLAGS.weight_reg_func,
                         scale=FLAGS.weight_reg_scale)
bias_reg = regularizer(mode=FLAGS.bias_reg_func, scale=FLAGS.bias_reg_scale)
net = get_net(FLAGS.net_name)