def prepare_args():
# parse args
parser = argparse.ArgumentParser(description="train cifar10",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
fit.add_fit_args(parser)
data.add_data_args(parser)
data.add_data_aug_args(parser)
# uncomment to set standard cifar augmentations
# set_cifar_aug(parser)
parser.set_defaults(
# network
network = 'resnet',
num_layers = 110,
# data
data_train = train_fname,
data_val = val_fname,
num_classes = 10,
num_examples = 50000,
image_shape = '3,28,28',
pad_size = 4,
# train
batch_size = 128,
num_epochs = 300,
lr = .05,
lr_step_epochs = '200,250',
)
return parser.parse_args(), fit, data
return (net, new_args)
def set_imagenet_aug(aug):
# standard data augmentation setting for imagenet training
aug.set_defaults(rgb_mean='123.68,116.779,103.939', rgb_std='58.393,57.12,57.375')
aug.set_defaults(random_crop=0, random_resized_crop=1, random_mirror=1)
aug.set_defaults(min_random_area=0.08)
aug.set_defaults(max_random_aspect_ratio=4./3., min_random_aspect_ratio=3./4.)
aug.set_defaults(brightness=0.4, contrast=0.4, saturation=0.4, pca_noise=0.1)
if __name__ == "__main__":
# parse args
parser = argparse.ArgumentParser(description="fine-tune a dataset",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
train = fit.add_fit_args(parser)
data.add_data_args(parser)
aug = data.add_data_aug_args(parser)
parser.add_argument('--pretrained-model', type=str,
help='the pre-trained model')
parser.add_argument('--layer-before-fullc', type=str, default='flatten0',
help='the name of the layer before the last fullc layer')
# use less augmentations for fine-tune
data.set_data_aug_level(parser, 1)
set_imagenet_aug(parser)
# use a small learning rate and less regularizations
parser.set_defaults(image_shape='3,224,224', num_epochs=30,
lr=.01, lr_step_epochs='20', wd=0, mom=0)
args = parser.parse_args()
import os
import argparse
import logging
logging.basicConfig(level=logging.DEBUG)
from common import find_mxnet, data, fit
from common.util import download_file
import mxnet as mx
if __name__ == '__main__':
# parse args
parser = argparse.ArgumentParser(
description="train cifar10",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
fit.add_fit_args(parser)
data.add_data_args(parser)
data.add_data_aug_args(parser)
# use a large aug level
data.set_data_aug_level(parser, 3)
parser.set_defaults(
# network
network='resnet',
num_layers=50,
# data
num_classes=1000,
num_examples=1281167,
image_shape='3,224,224',
min_random_scale=1, # if input image has min size k, suggest to use
# 256.0/x, e.g. 0.533 for 480
# train
num_epochs=80,
lr_step_epochs='30,60',
# specific language governing permissions and limitations
# under the License.
import os
import argparse
import logging
logging.basicConfig(level=logging.DEBUG)
from common import find_mxnet, data, fit
from common.util import download_file
import mxnet as mx
if __name__ == '__main__':
# parse args
parser = argparse.ArgumentParser(description="train imagenet-1k",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
fit.add_fit_args(parser)
data.add_data_args(parser)
data.add_data_aug_args(parser)
# uncomment to set standard augmentation for resnet training
# data.set_resnet_aug(parser)
parser.set_defaults(
# network
network = 'resnet',
num_layers = 50,
# data
num_classes = 1000,
num_examples = 1281167,
image_shape = '3,224,224',
min_random_scale = 1, # if input image has min size k, suggest to use
# 256.0/x, e.g. 0.533 for 480
# train
step_epochs = [int(l) for l in args.lr_step_epochs.split(',')]
lr = args.lr
for s in step_epochs:
if begin_epoch >= s:
lr *= args.lr_factor
if lr != args.lr:
logging.info('Adjust learning rate to %e for epoch %d' %(lr, begin_epoch))
steps = [epoch_size * (x-begin_epoch) for x in step_epochs if x-begin_epoch > 0]
return (lr, mx.lr_scheduler.MultiFactorScheduler(step=steps, factor=args.lr_factor))
if __name__ == "__main__":
# parse args
parser = argparse.ArgumentParser(description="fine-tune a dataset",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
train = fit.add_fit_args(parser)
data.add_data_args(parser)
aug = data.add_data_aug_args(parser)
parser.add_argument('--pretrained-model', type=str,
help='the pre-trained model')
parser.add_argument('--layer-before-fullc', type=str, default='flatten0',
help='the name of the layer before the last fullc layer')
# use less augmentations for fine-tune
data.set_data_aug_level(parser, 3)
# use a small learning rate and less regularizations
parser.set_defaults(
# network
network = 'inception-bn',
# data
num_classes = 1000,
def start_new_training():
# binding signals
SUPPORTED_SIGNALS = (
signal.SIGINT,
signal.SIGTERM,
)
for signum in SUPPORTED_SIGNALS:
try:
signal.signal(signum, signal_handler)
logger.info("Bind signal '%s' success to %s", signum,
signal_handler)
except Exception as identifier:
logger.warning("Bind signal '%s' failed, err: %s", signum,
identifier)
try:
# parse args
parser = argparse.ArgumentParser(
description="train imagenet-1k",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
fit.add_fit_args(parser)
data.add_data_args(parser)
data.add_data_aug_args(parser)
# use a large aug level
data.set_data_aug_level(parser, 3)
parser.set_defaults(
# network
network='resnet',
num_layers=50,
# data
num_classes=10,
num_examples=60000,
image_shape='3,28,28',
min_random_scale=1, # if input image has min size k, suggest to use
# 256.0/x, e.g. 0.533 for 480
# train
num_epochs=80,
lr_step_epochs='30,60',
dtype='float32',
batch_size=32)
args = parser.parse_args()
# AVA-SDK new an Instance
train_ins = train.TrainInstance()
# add CALLBACK
batch_end_cb = train_ins.get_monitor_callback(
"mxnet", batch_size=args.batch_size, batch_freq=10)
args.batch_end_callback = batch_end_cb
# load network
from importlib import import_module
net = import_module('symbols.' + args.network)
sym = net.get_symbol(**vars(args))
# train
fit.fit(args, sym, data.get_rec_iter)
logger.info("training finish")
err_msg = ""
if train_ins == None:
return
train_ins.done(err_msg=err_msg)
except Exception as err:
err_msg = "training failed, err: %s" % (err)
logger.info(err_msg)
traceback.print_exc(file=sys.stderr)
if train_ins == None:
return
train_ins.done(err_msg=err_msg)
