def get_parser(parser=None):
def str_list(x):
return x.split(',')
def int_list(x):
return list(map(int, x.split(',')))
def float_list(x):
return list(map(float, x.split(',')))
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Unsupported value encountered.')
if parser is None:
parser = argparse.ArgumentParser("flags for cnn benchmark")
parser.add_argument("--dtype",
type=str,
default='float32',
help="float16 float32")
# resouce
parser.add_argument("--gpu_num_per_node", type=int, default=1)
parser.add_argument('--num_nodes',
type=int,
default=1,
help='node/machine number for training')
parser.add_argument(
'--node_ips',
type=str_list,
default=['192.168.1.13', '192.168.1.14'],
help='nodes ip list for training, devided by ",", length >= num_nodes')
parser.add_argument("--model",
type=str,
default="resnet50",
help="resnet50")
parser.add_argument('--use_fp16',
type=str2bool,
nargs='?',
const=True,
help='Whether to use use fp16')
parser.add_argument('--channel_last',
type=str2bool,
nargs='?',
const=False,
help='Whether to use use channel last mode(nhwc)')
# train and validaion
parser.add_argument('--num_epochs',
type=int,
default=90,
help='number of epochs')
parser.add_argument("--model_load_dir",
type=str,
default=None,
help="model load directory if need")
parser.add_argument("--batch_size_per_device", type=int, default=64)
parser.add_argument("--val_batch_size_per_device", type=int, default=8)
# inference
parser.add_argument("--image_path",
type=str,
default='test_img/tiger.jpg',
help="image path")
# for data process
parser.add_argument("--num_classes",
type=int,
default=1000,
help="num of pic classes")
parser.add_argument("--num_examples",
type=int,
default=1281167,
help="train pic number")
parser.add_argument("--num_val_examples",
type=int,
default=50000,
help="validation pic number")
parser.add_argument('--rgb-mean',
type=float_list,
default=[123.68, 116.779, 103.939],
help='a tuple of size 3 for the mean rgb')
parser.add_argument('--rgb-std',
type=float_list,
default=[58.393, 57.12, 57.375],
help='a tuple of size 3 for the std rgb')
parser.add_argument("--input_layout",
type=str,
default='NHWC',
help="NCHW or NHWC")
parser.add_argument('--image-shape',
type=int_list,
default=[3, 224, 224],
help='the image shape feed into the network')
parser.add_argument('--label-smoothing',
type=float,
default=0.1,
help='label smoothing factor')
# snapshot
parser.add_argument(
"--model_save_dir",
type=str,
default="./output/snapshots/model_save-{}".format(
str(datetime.now().strftime("%Y%m%d%H%M%S"))),
help="model save directory",
)
# log and loss print
parser.add_argument("--log_dir",
type=str,
default="./output",
help="log info save directory")
parser.add_argument(
"--loss_print_every_n_iter",
type=int,
default=1,
help="print loss every n iteration",
)
add_ofrecord_args(parser)
add_optimizer_args(parser)
return parser
parser.add_argument("--model_save_dir", type=str,
default="./output/snapshots/model_save-{}".format(
str(datetime.now().strftime("%Y%m%d%H%M%S"))),
help="model save directory",
)
# log and loss print
parser.add_argument("--log_dir", type=str, default="./output", help="log info save directory")
parser.add_argument(
"--loss_print_every_n_iter",
type=int,
default=1,
help="print loss every n iteration",
)
add_ofrecord_args(parser)
add_optimizer_args(parser)
return parser
def print_args(args):
print("=".ljust(66, "="))
print("Running {}: num_gpu_per_node = {}, num_nodes = {}.".format(
args.model, args.gpu_num_per_node, args.num_nodes))
print("=".ljust(66, "="))
for arg in vars(args):
print("{} = {}".format(arg, getattr(args, arg)))
print("-".ljust(66, "-"))
print("Time stamp: {}".format(
str(datetime.now().strftime("%Y-%m-%d-%H:%M:%S"))))
def get_parser(parser=None):
def str_list(x):
return [i.strip() for i in x.split(',')]
def int_list(x):
return list(map(int, x.split(',')))
def float_list(x):
return list(map(float, x.split(',')))
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Unsupported value encountered.')
if parser is None:
parser = argparse.ArgumentParser("flags for cnn benchmark")
parser.add_argument("--dtype",
type=str,
default='float32',
help="float16 float32")
# resouce
parser.add_argument("--gpu_num_per_node", type=int, default=1)
parser.add_argument('--num_nodes',
type=int,
default=1,
help='node/machine number for training')
parser.add_argument(
'--node_ips',
type=str_list,
default=['192.168.1.13', '192.168.1.14'],
help='nodes ip list for training, devided by ",", length >= num_nodes')
parser.add_argument("--ctrl_port",
type=int,
default=50051,
help='ctrl_port for multinode job')
parser.add_argument("--model",
type=str,
default="resnet50",
help="resnet50")
parser.add_argument('--use_fp16',
type=str2bool,
nargs='?',
const=True,
help='Whether to use use fp16')
parser.add_argument('--use_xla',
type=str2bool,
nargs='?',
const=True,
help='Whether to use use xla')
parser.add_argument('--channel_last',
type=str2bool,
nargs='?',
const=False,
help='Whether to use use channel last mode(nhwc)')
parser.add_argument(
'--pad_output',
type=str2bool,
nargs='?',
const=True,
help='Whether to pad the output to number of image channels to 4.')
# train and validaion
parser.add_argument('--num_epochs',
type=int,
default=90,
help='number of epochs')
parser.add_argument("--model_load_dir",
type=str,
default=None,
help="model load directory if need")
parser.add_argument("--batch_size_per_device", type=int, default=64)
parser.add_argument("--val_batch_size_per_device", type=int, default=8)
parser.add_argument(
"--nccl_fusion_threshold_mb",
type=int,
default=0,
help=
"NCCL fusion threshold megabytes, set to 0 to compatible with previous version of OneFlow."
)
parser.add_argument(
"--nccl_fusion_max_ops",
type=int,
default=0,
help=
"Maximum number of ops of NCCL fusion, set to 0 to compatible with previous version of OneFlow."
)
# fuse bn relu or bn add relu
parser.add_argument(
'--fuse_bn_relu',
type=str2bool,
default=False,
help=
'Whether to use use fuse batch normalization relu. Currently supported in origin/master of OneFlow only.'
)
parser.add_argument(
'--fuse_bn_add_relu',
type=str2bool,
default=False,
help=
'Whether to use use fuse batch normalization add relu. Currently supported in origin/master of OneFlow only.'
)
parser.add_argument(
"--gpu_image_decoder",
type=str2bool,
default=False,
help='Whether to use use ImageDecoderRandomCropResize.')
# inference
parser.add_argument("--image_path",
type=str,
default='test_img/tiger.jpg',
help="image path")
# for data process
parser.add_argument("--num_classes",
type=int,
default=1000,
help="num of pic classes")
parser.add_argument("--num_examples",
type=int,
default=1281167,
help="train pic number")
parser.add_argument("--num_val_examples",
type=int,
default=50000,
help="validation pic number")
parser.add_argument('--rgb-mean',
type=float_list,
default=[123.68, 116.779, 103.939],
help='a tuple of size 3 for the mean rgb')
parser.add_argument('--rgb-std',
type=float_list,
default=[58.393, 57.12, 57.375],
help='a tuple of size 3 for the std rgb')
parser.add_argument('--image-shape',
type=int_list,
default=[3, 224, 224],
help='the image shape feed into the network')
parser.add_argument('--label_smoothing',
type=float,
default=0.1,
help='label smoothing factor')
# snapshot
parser.add_argument(
"--model_save_dir",
type=str,
default="./output/snapshots/model_save-{}".format(
str(datetime.now().strftime("%Y%m%d%H%M%S"))),
help="model save directory",
)
# log and loss print
parser.add_argument("--log_dir",
type=str,
default="./output",
help="log info save directory")
parser.add_argument(
"--loss_print_every_n_iter",
type=int,
default=1,
help="print loss every n iteration",
)
add_ofrecord_args(parser)
add_optimizer_args(parser)
return parser
