| # -*- coding:utf-8 -*- |
| __author__ = 'zhangshuai' |
| modified_date = '16/7/5' |
| __modify__ = 'anchengwu' |
| modified_date = '17/2/22' |
| |
| ''' |
| Inception v4 , suittable for image with around 299 x 299 |
| |
| Reference: |
| Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning |
| Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke |
| arXiv.1602.07261 |
| ''' |
| import find_mxnet |
| import mxnet as mx |
| |
| def Conv(data, num_filter, kernel=(1, 1), stride=(1, 1), pad=(0, 0), name=None, suffix=''): |
| conv = mx.symbol.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix)) |
| bn = mx.symbol.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True) |
| act = mx.symbol.Activation(data=bn, act_type='relu', name='%s%s_relu' %(name, suffix)) |
| |
| return act |
| |
| |
| def Inception_stem(data, name= None): |
| c = Conv(data, 32, kernel=(3, 3), stride=(2, 2), name='%s_conv1_3*3' %name) |
| c = Conv(c, 32, kernel=(3, 3), name='%s_conv2_3*3' %name) |
| c = Conv(c, 64, kernel=(3, 3), pad=(1, 1), name='%s_conv3_3*3' %name) |
| |
| p1 = mx.symbol.Pooling(c, kernel=(3, 3), stride=(2, 2), pool_type='max', name='%s_maxpool_1' %name) |
| c2 = Conv(c, 96, kernel=(3, 3), stride=(2, 2), name='%s_conv4_3*3' %name) |
| concat = mx.symbol.Concat(*[p1, c2], name='%s_concat_1' %name) |
| |
| c1 = Conv(concat, 64, kernel=(1, 1), pad=(0, 0), name='%s_conv5_1*1' %name) |
| c1 = Conv(c1, 96, kernel=(3, 3), name='%s_conv6_3*3' %name) |
| |
| c2 = Conv(concat, 64, kernel=(1, 1), pad=(0, 0), name='%s_conv7_1*1' %name) |
| c2 = Conv(c2, 64, kernel=(7, 1), pad=(3, 0), name='%s_conv8_7*1' %name) |
| c2 = Conv(c2, 64, kernel=(1, 7), pad=(0, 3), name='%s_conv9_1*7' %name) |
| c2 = Conv(c2, 96, kernel=(3, 3), pad=(0, 0), name='%s_conv10_3*3' %name) |
| |
| concat = mx.symbol.Concat(*[c1, c2], name='%s_concat_2' %name) |
| |
| c1 = Conv(concat, 192, kernel=(3, 3), stride=(2, 2), name='%s_conv11_3*3' %name) |
| p1 = mx.symbol.Pooling(concat, kernel=(3, 3), stride=(2, 2), pool_type='max', name='%s_maxpool_2' %name) |
| |
| concat = mx.symbol.Concat(*[c1, p1], name='%s_concat_3' %name) |
| |
| return concat |
| |
| |
| def InceptionA(input, name=None): |
| p1 = mx.symbol.Pooling(input, kernel=(3, 3), pad=(1, 1), pool_type='avg', name='%s_avgpool_1' %name) |
| c1 = Conv(p1, 96, kernel=(1, 1), pad=(0, 0), name='%s_conv1_1*1' %name) |
| |
| c2 = Conv(input, 96, kernel=(1, 1), pad=(0, 0), name='%s_conv2_1*1' %name) |
| |
| c3 = Conv(input, 64, kernel=(1, 1), pad=(0, 0), name='%s_conv3_1*1' %name) |
| c3 = Conv(c3, 96, kernel=(3, 3), pad=(1, 1), name='%s_conv4_3*3' %name) |
| |
| c4 = Conv(input, 64, kernel=(1, 1), pad=(0, 0), name='%s_conv5_1*1' % name) |
| c4 = Conv(c4, 96, kernel=(3, 3), pad=(1, 1), name='%s_conv6_3*3' % name) |
| c4 = Conv(c4, 96, kernel=(3, 3), pad=(1, 1), name='%s_conv7_3*3' %name) |
| |
| concat = mx.symbol.Concat(*[c1, c2, c3, c4], name='%s_concat_1' %name) |
| |
| return concat |
| |
| |
| def ReductionA(input, name=None): |
| p1 = mx.symbol.Pooling(input, kernel=(3, 3), stride=(2, 2), pool_type='max', name='%s_maxpool_1' %name) |
| |
| c2 = Conv(input, 384, kernel=(3, 3), stride=(2, 2), name='%s_conv1_3*3' %name) |
| |
| c3 = Conv(input, 192, kernel=(1, 1), pad=(0, 0), name='%s_conv2_1*1' %name) |
| c3 = Conv(c3, 224, kernel=(3, 3), pad=(1, 1), name='%s_conv3_3*3' %name) |
| c3 = Conv(c3, 256, kernel=(3, 3), stride=(2, 2), pad=(0, 0), name='%s_conv4_3*3' %name) |
| |
| concat = mx.symbol.Concat(*[p1, c2, c3], name='%s_concat_1' %name) |
| |
| return concat |
| |
| def InceptionB(input, name=None): |
| p1 = mx.symbol.Pooling(input, kernel=(3, 3), pad=(1, 1), pool_type='avg', name='%s_avgpool_1' %name) |
| c1 = Conv(p1, 128, kernel=(1, 1), pad=(0, 0), name='%s_conv1_1*1' %name) |
| |
| c2 = Conv(input, 384, kernel=(1, 1), pad=(0, 0), name='%s_conv2_1*1' %name) |
| |
| c3 = Conv(input, 192, kernel=(1, 1), pad=(0, 0), name='%s_conv3_1*1' %name) |
| c3 = Conv(c3, 224, kernel=(1, 7), pad=(0, 3), name='%s_conv4_1*7' %name) |
| #paper wrong |
| c3 = Conv(c3, 256, kernel=(7, 1), pad=(3, 0), name='%s_conv5_1*7' %name) |
| |
| c4 = Conv(input, 192, kernel=(1, 1), pad=(0, 0), name='%s_conv6_1*1' %name) |
| c4 = Conv(c4, 192, kernel=(1, 7), pad=(0, 3), name='%s_conv7_1*7' %name) |
| c4 = Conv(c4, 224, kernel=(7, 1), pad=(3, 0), name='%s_conv8_7*1' %name) |
| c4 = Conv(c4, 224, kernel=(1, 7), pad=(0, 3), name='%s_conv9_1*7' %name) |
| c4 = Conv(c4, 256, kernel=(7, 1), pad=(3, 0), name='%s_conv10_7*1' %name) |
| |
| concat = mx.sym.Concat(*[c1, c2, c3, c4], name='%s_concat_1' %name) |
| |
| return concat |
| |
| def ReductionB(input,name=None): |
| p1 = mx.symbol.Pooling(input, kernel=(3, 3), stride=(2, 2), pool_type='max', name='%s_maxpool_1' %name) |
| |
| c2 = Conv(input, 192, kernel=(1, 1), pad=(0, 0), name='%s_conv1_1*1' %name) |
| c2 = Conv(c2, 192, kernel=(3, 3), stride=(2, 2), name='%s_conv2_3*3' %name) |
| |
| c3 = Conv(input, 256, kernel=(1, 1), pad=(0, 0), name='%s_conv3_1*1' %name) |
| c3 = Conv(c3, 256, kernel=(1, 7), pad=(0, 3), name='%s_conv4_1*7' %name) |
| c3 = Conv(c3, 320, kernel=(7, 1), pad=(3, 0), name='%s_conv5_7*1' %name) |
| c3 = Conv(c3, 320, kernel=(3, 3), stride=(2, 2), name='%s_conv6_3*3' %name) |
| |
| concat = mx.symbol.Concat(*[p1, c2, c3], name='%s_concat_1' %name) |
| |
| return concat |
| |
| |
| def InceptionC(input, name=None): |
| p1 = mx.symbol.Pooling(input, kernel=(3, 3), pad=(1, 1), pool_type='avg', name='%s_avgpool_1' %name) |
| c1 = Conv(p1, 256, kernel=(1, 1), pad=(0, 0), name='%s_conv1_1*1' %name) |
| |
| c2 = Conv(input, 256, kernel=(1, 1), pad=(0, 0), name='%s_conv2_1*1' %name) |
| |
| c3 = Conv(input, 384, kernel=(1, 1), pad=(0, 0), name='%s_conv3_1*1' %name) |
| c3_1 = Conv(c3, 256, kernel=(1, 3), pad=(0, 1), name='%s_conv4_3*1' %name) |
| c3_2 = Conv(c3, 256, kernel=(3, 1), pad=(1, 0), name='%s_conv5_1*3' %name) |
| |
| c4 = Conv(input, 384, kernel=(1, 1), pad=(0, 0), name='%s_conv6_1*1' %name) |
| c4 = Conv(c4, 448, kernel=(1, 3), pad=(0, 1), name='%s_conv7_1*3' %name) |
| c4 = Conv(c4, 512, kernel=(3, 1), pad=(1, 0), name='%s_conv8_3*1' %name) |
| c4_1 = Conv(c4, 256, kernel=(3, 1), pad=(1, 0), name='%s_conv9_1*3' %name) |
| c4_2 = Conv(c4, 256, kernel=(1, 3), pad=(0, 1), name='%s_conv10_3*1' %name) |
| |
| concat = mx.symbol.Concat(*[c1, c2, c3_1, c3_2, c4_1, c4_2], name='%s_concat' %name) |
| |
| return concat |
| |
| |
| def get_symbol(num_classes=1000, **kwargs): |
| data = mx.symbol.Variable(name="data") |
| x = Inception_stem(data, name='in_stem') |
| |
| #4 * InceptionA |
| # x = InceptionA(x, name='in1A') |
| # x = InceptionA(x, name='in2A') |
| # x = InceptionA(x, name='in3A') |
| # x = InceptionA(x, name='in4A') |
| |
| for i in range(4): |
| x = InceptionA(x, name='in%dA' %(i+1)) |
| |
| #Reduction A |
| x = ReductionA(x, name='re1A') |
| |
| #7 * InceptionB |
| # x = InceptionB(x, name='in1B') |
| # x = InceptionB(x, name='in2B') |
| # x = InceptionB(x, name='in3B') |
| # x = InceptionB(x, name='in4B') |
| # x = InceptionB(x, name='in5B') |
| # x = InceptionB(x, name='in6B') |
| # x = InceptionB(x, name='in7B') |
| |
| for i in range(7): |
| x = InceptionB(x, name='in%dB' %(i+1)) |
| |
| #ReductionB |
| x = ReductionB(x, name='re1B') |
| |
| #3 * InceptionC |
| # x = InceptionC(x, name='in1C') |
| # x = InceptionC(x, name='in2C') |
| # x = InceptionC(x, name='in3C') |
| |
| for i in range(3): |
| x = InceptionC(x, name='in%dC' %(i+1)) |
| |
| #Average Pooling |
| x = mx.symbol.Pooling(x, kernel=(8, 8), pad=(1, 1), pool_type='avg', name='global_avgpool') |
| |
| #Dropout |
| x = mx.symbol.Dropout(x, p=0.2) |
| |
| flatten = mx.symbol.Flatten(x, name='flatten') |
| fc1 = mx.symbol.FullyConnected(flatten, num_hidden=num_classes, name='fc1') |
| softmax = mx.symbol.SoftmaxOutput(fc1, name='softmax') |
| |
| return softmax |
