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train_resnet.py
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import argparse
import logging
import os
import time
import mxnet as mx
from symbol_resnet import resnet
fname = time.strftime("%Y%m%d%H%M%S", time.localtime())
logging.basicConfig(level=logging.INFO,
filename='log/'+fname+'.log', filemode='w')
logger = logging.getLogger()
data_type = 'cifar10'
def multi_factor_scheduler(begin_epoch, epoch_size, step=[60, 75, 90], factor=0.1):
step_ = [epoch_size * (x-begin_epoch) for x in step if x-begin_epoch > 0]
return mx.lr_scheduler.MultiFactorScheduler(step=step_, factor=factor) if len(step_) else None
def main():
if (args.depth-2) % 9 == 0: # and args.depth >= 164:
per_unit = [(args.depth-2) / 9]
filter_list = [16, 64, 128, 256]
bottle_neck = True
# elif (args.depth-2) % 6 == 0 and args.depth < 164:
# per_unit = [(args.depth-2) / 6]
# filter_list = [16, 16, 32, 64]
# bottle_neck = False
else:
raise ValueError(
"no experiments done on detph {}, you can do it youself".format(args.depth))
units = per_unit*3
symbol = resnet(units=units, num_stage=3, filter_list=filter_list, num_class=args.num_classes,
bottle_neck=bottle_neck, bn_mom=args.bn_mom, workspace=args.workspace, memonger=args.memonger)
kv = mx.kvstore.create(args.kv_store)
devs = mx.cpu() if args.gpus is None else [
mx.gpu(int(i)) for i in args.gpus.split(',')]
epoch_size = max(
int(args.num_examples / args.batch_size / kv.num_workers), 1)
begin_epoch = args.model_load_epoch if args.model_load_epoch else 0
if not os.path.exists("./model"):
os.mkdir("./model")
model_prefix = "model/resnet-{}-{}-{}".format(
data_type, args.depth, kv.rank)
checkpoint = mx.callback.do_checkpoint(model_prefix)
arg_params = None
aux_params = None
if args.retrain:
_, arg_params, aux_params = mx.model.load_checkpoint(
model_prefix, args.model_load_epoch)
if args.memonger:
import memonger
symbol = memonger.search_plan(
symbol, data=(args.batch_size, 3, 32, 32))
train = mx.io.ImageRecordIter(
path_imgrec = os.path.join(args.data_dir, "cifar10_train.rec"),
label_width = 1,
data_shape = (3, 32, 32),
num_parts = kv.num_workers,
part_index = kv.rank,
shuffle = True,
batch_size = args.batch_size,
rand_crop = True,
fill_value = 127, # only used when pad is valid
pad = 4,
rand_mirror = True,
)
val = mx.io.ImageRecordIter(
path_imgrec = os.path.join(args.data_dir, "cifar10_val.rec"),
label_width = 1,
data_shape = (3, 32, 32),
num_parts = kv.num_workers,
part_index = kv.rank,
batch_size = args.batch_size,
)
model = mx.mod.Module(
symbol = symbol,
data_names = ('data', ),
label_names = ('softmax_label', ),
context = devs,
)
model.fit(
train_data = train,
eval_data = val,
eval_metric = ['acc'],
epoch_end_callback = checkpoint,
batch_end_callback = mx.callback.Speedometer(args.batch_size, args.frequent),
kvstore = kv,
optimizer = 'nag',
optimizer_params = (('learning_rate', args.lr), ('momentum', args.mom), ('wd', args.wd), (
'lr_scheduler', multi_factor_scheduler(begin_epoch, epoch_size, step=[80], factor=0.1))),
initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="in", magnitude=2),
arg_params = arg_params,
aux_params = aux_params,
allow_missing = True,
begin_epoch = begin_epoch,
num_epoch = args.end_epoch,
)
# logging.info("top-1 and top-5 acc is {}".format(model.score(X = val,
# eval_metric = ['acc', mx.metric.create('top_k_accuracy', top_k = 5)])))
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="command for training resnet-v2")
parser.add_argument('--gpus', type=str, default=None,
help='the gpus will be used, e.g "0,1,2,3"')
parser.add_argument('--data-dir', type=str, default='./data/cifar10/',
help='the input data directory')
parser.add_argument('--lr', type=float, default=0.1,
help='initialization learning reate')
parser.add_argument('--mom', type=float, default=0.9,
help='momentum for sgd')
parser.add_argument('--bn-mom', type=float, default=0.9,
help='momentum for batch normlization')
parser.add_argument('--wd', type=float, default=0.0001,
help='weight decay for sgd')
parser.add_argument('--batch-size', type=int, default=128,
help='the batch size')
parser.add_argument('--workspace', type=int, default=512,
help='memory space size(MB) used in convolution, if xpu '
' memory is oom, then you can try smaller vale, such as --workspace 256')
parser.add_argument('--depth', type=int, default=164,
help='the depth of resnet')
parser.add_argument('--num-classes', type=int, default=10,
help='the class number of your task')
parser.add_argument('--num-examples', type=int, default=50000,
help='the number of training examples')
parser.add_argument('--kv-store', type=str, default='device',
help='the kvstore type')
parser.add_argument('--model-load-epoch', type=int, default=0,
help='load the model on an epoch using the model-load-prefix')
parser.add_argument('--end-epoch', type=int, default=120,
help='training ends at this num of epoch')
parser.add_argument('--frequent', type=int, default=50,
help='frequency of logging')
parser.add_argument('--memonger', action='store_true', default=False,
help='true means using memonger to save momory, https://github.com/dmlc/mxnet-memonger')
parser.add_argument('--retrain', action='store_true', default=False,
help='true means continue training')
args = parser.parse_args()
logging.info(args)
main()