| """ |
| Contains the definition of the Inception Resnet V2 architecture. |
| As described in http://arxiv.org/abs/1602.07261. |
| Inception-v4, Inception-ResNet and the Impact of Residual Connections |
| on Learning |
| Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi |
| """ |
| import mxnet as mx |
| |
| |
| def ConvFactory(data, num_filter, kernel, stride=(1, 1), pad=(0, 0), act_type="relu", mirror_attr={}, with_act=True): |
| conv = mx.symbol.Convolution( |
| data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad) |
| bn = mx.symbol.BatchNorm(data=conv) |
| if with_act: |
| act = mx.symbol.Activation( |
| data=bn, act_type=act_type, attr=mirror_attr) |
| return act |
| else: |
| return bn |
| |
| |
| def block35(net, input_num_channels, scale=1.0, with_act=True, act_type='relu', mirror_attr={}): |
| tower_conv = ConvFactory(net, 32, (1, 1)) |
| tower_conv1_0 = ConvFactory(net, 32, (1, 1)) |
| tower_conv1_1 = ConvFactory(tower_conv1_0, 32, (3, 3), pad=(1, 1)) |
| tower_conv2_0 = ConvFactory(net, 32, (1, 1)) |
| tower_conv2_1 = ConvFactory(tower_conv2_0, 48, (3, 3), pad=(1, 1)) |
| tower_conv2_2 = ConvFactory(tower_conv2_1, 64, (3, 3), pad=(1, 1)) |
| tower_mixed = mx.symbol.Concat(*[tower_conv, tower_conv1_1, tower_conv2_2]) |
| tower_out = ConvFactory( |
| tower_mixed, input_num_channels, (1, 1), with_act=False) |
| |
| net += scale * tower_out |
| if with_act: |
| act = mx.symbol.Activation( |
| data=net, act_type=act_type, attr=mirror_attr) |
| return act |
| else: |
| return net |
| |
| |
| def block17(net, input_num_channels, scale=1.0, with_act=True, act_type='relu', mirror_attr={}): |
| tower_conv = ConvFactory(net, 192, (1, 1)) |
| tower_conv1_0 = ConvFactory(net, 129, (1, 1)) |
| tower_conv1_1 = ConvFactory(tower_conv1_0, 160, (1, 7), pad=(1, 2)) |
| tower_conv1_2 = ConvFactory(tower_conv1_1, 192, (7, 1), pad=(2, 1)) |
| tower_mixed = mx.symbol.Concat(*[tower_conv, tower_conv1_2]) |
| tower_out = ConvFactory( |
| tower_mixed, input_num_channels, (1, 1), with_act=False) |
| net += scale * tower_out |
| if with_act: |
| act = mx.symbol.Activation( |
| data=net, act_type=act_type, attr=mirror_attr) |
| return act |
| else: |
| return net |
| |
| |
| def block8(net, input_num_channels, scale=1.0, with_act=True, act_type='relu', mirror_attr={}): |
| tower_conv = ConvFactory(net, 192, (1, 1)) |
| tower_conv1_0 = ConvFactory(net, 192, (1, 1)) |
| tower_conv1_1 = ConvFactory(tower_conv1_0, 224, (1, 3), pad=(0, 1)) |
| tower_conv1_2 = ConvFactory(tower_conv1_1, 256, (3, 1), pad=(1, 0)) |
| tower_mixed = mx.symbol.Concat(*[tower_conv, tower_conv1_2]) |
| tower_out = ConvFactory( |
| tower_mixed, input_num_channels, (1, 1), with_act=False) |
| net += scale * tower_out |
| if with_act: |
| act = mx.symbol.Activation( |
| data=net, act_type=act_type, attr=mirror_attr) |
| return act |
| else: |
| return net |
| |
| |
| def repeat(inputs, repetitions, layer, *args, **kwargs): |
| outputs = inputs |
| for i in range(repetitions): |
| outputs = layer(outputs, *args, **kwargs) |
| return outputs |
| |
| |
| def get_symbol(num_classes=1000, **kwargs): |
| data = mx.symbol.Variable(name='data') |
| conv1a_3_3 = ConvFactory(data=data, num_filter=32, |
| kernel=(3, 3), stride=(2, 2)) |
| conv2a_3_3 = ConvFactory(conv1a_3_3, 32, (3, 3)) |
| conv2b_3_3 = ConvFactory(conv2a_3_3, 64, (3, 3), pad=(1, 1)) |
| maxpool3a_3_3 = mx.symbol.Pooling( |
| data=conv2b_3_3, kernel=(3, 3), stride=(2, 2), pool_type='max') |
| conv3b_1_1 = ConvFactory(maxpool3a_3_3, 80, (1, 1)) |
| conv4a_3_3 = ConvFactory(conv3b_1_1, 192, (3, 3)) |
| maxpool5a_3_3 = mx.symbol.Pooling( |
| data=conv4a_3_3, kernel=(3, 3), stride=(2, 2), pool_type='max') |
| |
| tower_conv = ConvFactory(maxpool5a_3_3, 96, (1, 1)) |
| tower_conv1_0 = ConvFactory(maxpool5a_3_3, 48, (1, 1)) |
| tower_conv1_1 = ConvFactory(tower_conv1_0, 64, (5, 5), pad=(2, 2)) |
| |
| tower_conv2_0 = ConvFactory(maxpool5a_3_3, 64, (1, 1)) |
| tower_conv2_1 = ConvFactory(tower_conv2_0, 96, (3, 3), pad=(1, 1)) |
| tower_conv2_2 = ConvFactory(tower_conv2_1, 96, (3, 3), pad=(1, 1)) |
| |
| tower_pool3_0 = mx.symbol.Pooling(data=maxpool5a_3_3, kernel=( |
| 3, 3), stride=(1, 1), pad=(1, 1), pool_type='avg') |
| tower_conv3_1 = ConvFactory(tower_pool3_0, 64, (1, 1)) |
| tower_5b_out = mx.symbol.Concat( |
| *[tower_conv, tower_conv1_1, tower_conv2_2, tower_conv3_1]) |
| net = repeat(tower_5b_out, 10, block35, scale=0.17, input_num_channels=320) |
| tower_conv = ConvFactory(net, 384, (3, 3), stride=(2, 2)) |
| tower_conv1_0 = ConvFactory(net, 256, (1, 1)) |
| tower_conv1_1 = ConvFactory(tower_conv1_0, 256, (3, 3), pad=(1, 1)) |
| tower_conv1_2 = ConvFactory(tower_conv1_1, 384, (3, 3), stride=(2, 2)) |
| tower_pool = mx.symbol.Pooling(net, kernel=( |
| 3, 3), stride=(2, 2), pool_type='max') |
| net = mx.symbol.Concat(*[tower_conv, tower_conv1_2, tower_pool]) |
| net = repeat(net, 20, block17, scale=0.1, input_num_channels=1088) |
| tower_conv = ConvFactory(net, 256, (1, 1)) |
| tower_conv0_1 = ConvFactory(tower_conv, 384, (3, 3), stride=(2, 2)) |
| tower_conv1 = ConvFactory(net, 256, (1, 1)) |
| tower_conv1_1 = ConvFactory(tower_conv1, 288, (3, 3), stride=(2, 2)) |
| tower_conv2 = ConvFactory(net, 256, (1, 1)) |
| tower_conv2_1 = ConvFactory(tower_conv2, 288, (3, 3), pad=(1, 1)) |
| tower_conv2_2 = ConvFactory(tower_conv2_1, 320, (3, 3), stride=(2, 2)) |
| tower_pool = mx.symbol.Pooling(net, kernel=( |
| 3, 3), stride=(2, 2), pool_type='max') |
| net = mx.symbol.Concat( |
| *[tower_conv0_1, tower_conv1_1, tower_conv2_2, tower_pool]) |
| |
| net = repeat(net, 9, block8, scale=0.2, input_num_channels=2080) |
| net = block8(net, with_act=False, input_num_channels=2080) |
| |
| net = ConvFactory(net, 1536, (1, 1)) |
| net = mx.symbol.Pooling(net, kernel=( |
| 1, 1), global_pool=True, stride=(2, 2), pool_type='avg') |
| net = mx.symbol.Flatten(net) |
| net = mx.symbol.Dropout(data=net, p=0.2) |
| net = mx.symbol.FullyConnected(data=net, num_hidden=num_classes) |
| softmax = mx.symbol.SoftmaxOutput(data=net, name='softmax') |
| return softmax |
