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train_hybrid.py
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train_hybrid.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
import argparse
import logging
logging.basicConfig(level=logging.DEBUG)
import time
from common import find_mxnet, data, fit
from common.util import download_file
import mxnet as mx
thisdir = os.path.dirname(os.path.abspath(__file__))
if __name__ == '__main__':
# parse args
parser = argparse.ArgumentParser(description="train hybrid-1365",
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, 2)
parser.set_defaults(
# network
network = 'mobilenetv2',
multiplier = 1.0,
model_prefix = os.path.join(thisdir,'models/hybrid1365-mnetv2-1_0'),
# data
data_train = '/DATA1/liangfu/hybrid1365/hybrid1365_train.rec',
data_train_idx = '/DATA1/liangfu/hybrid1365/hybrid1365_train.idx',
data_val = '/DATA1/liangfu/hybrid1365/hybrid1365_val.rec',
data_val_idx = '/DATA1/liangfu/hybrid1365/hybrid1365_val.idx',
num_classes = 1365,
num_examples = 1281167+1803460,
image_shape = '3,224,224',
max_random_scale = 1.0,
min_random_scale = 0.533, # if input image has min size k, suggest to use
# 256.0/x, e.g. 0.533 for 480
# train
num_epochs = 480, # default=480 epochs
lr = 0.045, # default=0.045
lr_factor = 0.98, # default=0.98
lr_step_epochs = ','.join([str(i) for i in range(1,480)]),
wd = 0.00004,
dtype = 'float32',
batch_size = 240,
gpus = '0,1,2',
optimizer = 'sgd',
# monitor = 20,
load_epoch = None, # default=None
top_k = 5,
)
args = parser.parse_args()
from pprint import pprint
pprint(vars(args))
# load network
from importlib import import_module
net = import_module('symbols.'+args.network)
sym = net.get_symbol(num_classes=args.num_classes, multiplier=args.multiplier)
# print(sym.get_internals()['mobilenetv20_features_conv0_weight'].attr_dict()['mobilenetv20_features_conv0_weight']['__shape__'])
# exit()
# set up logger
logger = logging.getLogger()
fh = logging.FileHandler(os.path.join('log',time.strftime('%F-%T',time.localtime()).replace(':','-')+'.log'))
fh.setLevel(logging.DEBUG)
# ch = logging.StreamHandler()
# ch.setLevel(logging.INFO)
logger.addHandler(fh)
# logger.addHandler(ch)
# train
fit.fit(args, sym, data.get_rec_iter, logger)