| # 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 |
