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mobilenet.py
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mobilenet.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.
# -*- coding:utf-8 -*-
'''
mobilenet
Suittable for image with around resolution x resolution, resolution is multiple of 32.
Reference:
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://arxiv.org/abs/1704.04861
'''
__author__ = 'qingzhouzhen'
__date__ = '17/8/5'
__modify__ = 'dwSun'
__modified_date__ = '17/11/30'
import mxnet as mx
alpha_values = [0.25, 0.50, 0.75, 1.0]
def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name='', 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 = mx.sym.Activation(data=bn, act_type='relu', name='%s%s_relu' % (name, suffix))
return act
def Conv_DPW(data, depth=1, stride=(1, 1), name='', idx=0, suffix=''):
conv_dw = Conv(data, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=stride, name="conv_%d_dw" % (idx), suffix=suffix)
conv = Conv(conv_dw, num_filter=depth * stride[0], kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_%d" % (idx), suffix=suffix)
return conv
def get_symbol_compact(num_classes, alpha=1, resolution=224, **kwargs):
assert alpha in alpha_values, 'Invalid alpha={0}, must be one of {1}'.format(alpha, alpha_values)
assert resolution % 32 == 0, 'resolution must be multiple of 32'
base = int(32 * alpha)
data = mx.symbol.Variable(name="data") # 224
conv_1 = Conv(data, num_filter=base, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1") # 32*alpha, 224/112
conv_2_dw = Conv(conv_1, num_group=base, num_filter=base, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_2_dw") # 112/112
conv_2 = Conv(conv_2_dw, num_filter=base * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_2") # 32*alpha, 112/112
conv_3_dpw = Conv_DPW(conv_2, depth=base * 2, stride=(2, 2), idx=3) # 64*alpha, 112/56 => 56/56
conv_4_dpw = Conv_DPW(conv_3_dpw, depth=base * 4, stride=(1, 1), idx=4) # 128*alpha, 56/56 =>56/56
conv_5_dpw = Conv_DPW(conv_4_dpw, depth=base * 4, stride=(2, 2), idx=5) # 128*alpha, 56/28 => 28/28
conv_6_dpw = Conv_DPW(conv_5_dpw, depth=base * 8, stride=(1, 1), idx=6) # 256*alpha, 28/28 => 28/28
conv_7_dpw = Conv_DPW(conv_6_dpw, depth=base * 8, stride=(2, 2), idx=7) # 256*alpha, 28/14 => 14/14
conv_dpw = conv_7_dpw
for idx in range(8, 13):
conv_dpw = Conv_DPW(conv_dpw, depth=base * 16, stride=(1, 1), idx=idx) # 512*alpha, 14/14
conv_12_dpw = conv_dpw
conv_13_dpw = Conv_DPW(conv_12_dpw, depth=base * 16, stride=(2, 2), idx=13) # 512*alpha, 14/7 => 7/7
conv_14_dpw = Conv_DPW(conv_13_dpw, depth=base * 32, stride=(1, 1), idx=14) # 1024*alpha, 7/7 => 7/7
pool_size = int(resolution / 32)
pool = mx.sym.Pooling(data=conv_14_dpw, kernel=(pool_size, pool_size), stride=(1, 1), pool_type="avg", name="global_pool")
flatten = mx.sym.Flatten(data=pool, name="flatten")
fc = mx.symbol.FullyConnected(data=flatten, num_hidden=num_classes, name='fc')
softmax = mx.symbol.SoftmaxOutput(data=fc, name='softmax')
return softmax
def get_symbol(num_classes, alpha=1, resolution=224, **kwargs):
assert alpha in alpha_values, 'Invalid alpha=[{0}], must be one of [{1}]'.format(alpha, alpha_values)
assert resolution % 32 == 0, 'resolution must be multpile of 32'
base = int(32 * alpha)
data = mx.symbol.Variable(name="data") # 224
depth = base # 32*alpha
conv_1 = Conv(data, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1") # 224/112
depth = base # 32*alpha
conv_2_dw = Conv(conv_1, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_2_dw") # 112/112
conv_2 = Conv(conv_2_dw, num_filter=depth * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_2") # 112/112
depth = base * 2 # 64*alpha
conv_3_dw = Conv(conv_2, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_3_dw") # 112/56
conv_3 = Conv(conv_3_dw, num_filter=depth * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_3") # 56/56
depth = base * 4 # 128*alpha
conv_4_dw = Conv(conv_3, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_4_dw") # 56/56
conv_4 = Conv(conv_4_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_4") # 56/56
depth = base * 4 # 128*alpha
conv_5_dw = Conv(conv_4, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_5_dw") # 56/28
conv_5 = Conv(conv_5_dw, num_filter=depth * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_5") # 28/28
depth = base * 8 # 256*alpha
conv_6_dw = Conv(conv_5, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_6_dw") # 28/28
conv_6 = Conv(conv_6_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_6") # 28/28
depth = base * 8 # 256*alpha
conv_7_dw = Conv(conv_6, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_7_dw") # 28/14
conv_7 = Conv(conv_7_dw, num_filter=depth * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_7") # 14/14
depth = base * 16 # 512*alpha
conv_8_dw = Conv(conv_7, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_8_dw") # 14/14
conv_8 = Conv(conv_8_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_8") # 14/14
conv_9_dw = Conv(conv_8, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_9_dw") # 14/14
conv_9 = Conv(conv_9_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_9") # 14/14
conv_10_dw = Conv(conv_9, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_10_dw") # 14/14
conv_10 = Conv(conv_10_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_10") # 14/14
conv_11_dw = Conv(conv_10, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_11_dw") # 14/14
conv_11 = Conv(conv_11_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_11") # 14/14
conv_12_dw = Conv(conv_11, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_12_dw") # 14/14
conv_12 = Conv(conv_12_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_12") # 14/14
depth = base * 16 # 512*alpha
conv_13_dw = Conv(conv_12, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_13_dw") # 14/7
conv_13 = Conv(conv_13_dw, num_filter=depth * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_13") # 7/7
depth = base * 32 # 1024*alpha
conv_14_dw = Conv(conv_13, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_14_dw") # 7/7
conv_14 = Conv(conv_14_dw, num_filter=depth, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_14") # 7/7
pool_size = int(resolution / 32)
pool = mx.sym.Pooling(data=conv_14, kernel=(pool_size, pool_size), stride=(1, 1), pool_type="avg", name="global_pool")
flatten = mx.sym.Flatten(data=pool, name="flatten")
fc = mx.symbol.FullyConnected(data=flatten, num_hidden=num_classes, name='fc')
softmax = mx.symbol.SoftmaxOutput(data=fc, name='softmax')
return softmax