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add mobilenet

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Jia Guo
2017-12-06 19:46:33 +08:00
parent f297af8911
commit 81efbd1940
2 changed files with 116 additions and 5 deletions
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109
src/symbols/fmobilenet.py Normal file
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@@ -0,0 +1,109 @@
# 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 mxnet as mx
def Act(data, act_type, name):
#ignore param act_type, set it in this function
body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name)
#act = mx.sym.Activation(data=bn, act_type='relu', name='%s%s_relu' %(name, suffix))
return body
def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):
conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))
bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)
act = Act(data=bn, act_type='relu', name='%s%s_relu' %(name, suffix))
return act
def get_symbol(num_classes, **kwargs):
data = mx.symbol.Variable(name="data") # 224
data = data-127.5
data = data*0.0078125
version_input = kwargs.get('version_input', 0)
assert version_input>=0
version_output = kwargs.get('version_output', 'A')
fc_type = version_output
version_unit = kwargs.get('version_unit', 1)
print(version_input, version_output, version_unit)
if version_input==0:
conv_1 = Conv(data, num_filter=32, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1") # 224/112
else:
conv_1 = Conv(data, num_filter=32, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_1") # 224/112
conv_2_dw = Conv(conv_1, num_group=32, num_filter=32, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_2_dw") # 112/112
conv_2 = Conv(conv_2_dw, num_filter=64, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_2") # 112/112
conv_3_dw = Conv(conv_2, num_group=64, num_filter=64, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_3_dw") # 112/56
conv_3 = Conv(conv_3_dw, num_filter=128, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_3") # 56/56
conv_4_dw = Conv(conv_3, num_group=128, num_filter=128, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_4_dw") # 56/56
conv_4 = Conv(conv_4_dw, num_filter=128, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_4") # 56/56
conv_5_dw = Conv(conv_4, num_group=128, num_filter=128, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_5_dw") # 56/28
conv_5 = Conv(conv_5_dw, num_filter=256, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_5") # 28/28
conv_6_dw = Conv(conv_5, num_group=256, num_filter=256, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_6_dw") # 28/28
conv_6 = Conv(conv_6_dw, num_filter=256, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_6") # 28/28
conv_7_dw = Conv(conv_6, num_group=256, num_filter=256, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_7_dw") # 28/14
conv_7 = Conv(conv_7_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_7") # 14/14
conv_8_dw = Conv(conv_7, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_8_dw") # 14/14
conv_8 = Conv(conv_8_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_8") # 14/14
conv_9_dw = Conv(conv_8, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_9_dw") # 14/14
conv_9 = Conv(conv_9_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_9") # 14/14
conv_10_dw = Conv(conv_9, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_10_dw") # 14/14
conv_10 = Conv(conv_10_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_10") # 14/14
conv_11_dw = Conv(conv_10, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_11_dw") # 14/14
conv_11 = Conv(conv_11_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_11") # 14/14
conv_12_dw = Conv(conv_11, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_12_dw") # 14/14
conv_12 = Conv(conv_12_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_12") # 14/14
conv_13_dw = Conv(conv_12, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_13_dw") # 14/7
conv_13 = Conv(conv_13_dw, num_filter=1024, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_13") # 7/7
conv_14_dw = Conv(conv_13, num_group=1024, num_filter=1024, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_14_dw") # 7/7
conv_14 = Conv(conv_14_dw, num_filter=1024, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_14") # 7/7
body = conv_14
if fc_type=='E':
body = mx.symbol.Dropout(data=body, p=0.4)
fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, name='fc1')
elif fc_type=='F':
body = mx.symbol.Dropout(data=body, p=0.4)
fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, name='fc1')
fc1 = Act(data=fc1, act_type='relu', name='fc1_relu')
else:
pool = mx.sym.Pooling(data=conv_14, global_pool=True, kernel=(7, 7), stride=(1, 1), pool_type="avg", name="global_pool")
flat = mx.sym.Flatten(data=pool, name="flatten")
if fc_type=='A':
fc1 = flat
else:
if fc_type=='G' or fc_type=='H':
fc1 = mx.symbol.Dropout(data=flat, p=0.2)
fc1 = mx.sym.FullyConnected(data=fc1, num_hidden=num_classes, name='pre_fc1')
if fc_type=='G':
return fc1
else:
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, name='fc1')
return fc1
else:
#B-D
#B
fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='pre_fc1')
if fc_type=='C':
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, name='fc1')
elif fc_type=='D':
fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, name='fc1')
fc1 = Act(data=fc1, act_type='relu', name='fc1_relu')
return fc1

12
src/train_softmax.py
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@@ -24,7 +24,7 @@ sys.path.append(os.path.join(os.path.dirname(__file__), 'symbols'))
import fresnet
import finception_resnet_v2
import spherenet
import marginalnet
import fmobilenet
#import inceptions
#import xception
#import lfw
@@ -143,8 +143,10 @@ def get_symbol(args, arg_params, aux_params):
if args.network[0]=='s':
embedding = spherenet.get_symbol(512, args.num_layers)
elif args.network[0]=='m':
print('init marginal', args.num_layers)
embedding = marginalnet.get_symbol(512, args.num_layers)
print('init mobilenet', args.num_layers)
embedding = fmobilenet.get_symbol(512,
use_se=args.use_se, version_input=args.version_input,
version_output=args.version_output, version_unit=args.version_unit)
elif args.network[0]=='i':
print('init inception-resnet-v2', args.num_layers)
embedding = finception_resnet_v2.get_symbol(512)
@@ -355,8 +357,8 @@ def train_net(args):
data_shape_dict = {'data': (args.batch_size,)+data_shape, 'softmax_label': (args.batch_size,)}
if args.network[0]=='s':
arg_params, aux_params = spherenet.init_weights(sym, data_shape_dict, args.num_layers)
elif args.network[0]=='m':
arg_params, aux_params = marginalnet.init_weights(sym, data_shape_dict, args.num_layers)
#elif args.network[0]=='m':
# arg_params, aux_params = marginalnet.init_weights(sym, data_shape_dict, args.num_layers)
#resnet_dcn.init_weights(sym, data_shape_dict, arg_params, aux_params)
else:
#sym, arg_params, aux_params = mx.model.load_checkpoint(pretrained, load_epoch)