【Tensorflow slim 实战】写MobileNet

按https://arxiv.org/pdf/1704.04861.pdf

通过和https://github.com/Zehaos/MobileNet/blob/master/nets/mobilenet.py 的对比

复写了一遍MobileNet

# -*- coding:utf-8 -*-import tensorflow as tfimport tensorflow.contrib.slim as slimdef mobile_net(inputs,               num_classes=1000,               is_training=True,               width_multiplier=1,               scope='MobileNet'):    '''    net structs    --------------------------------------------------    conv / s2         | 3x3x3x32         | 224x224x3      conv dw / s1      | 3x3x1x32 dw      | 112x112x32    conv / s1         | 1x1x32x64        | 112x112x32      conv dw / s2      | 3x3x1x64 dw      | 112x112x64    conv / s1         | 1x1x64x128       | 56x56x64      conv dw / s1      | 3x3x1x128 dw     | 56x56x128    conv / s1         | 1x1x128x128      | 56x56x128     conv dw / s2      | 3x3x1x128 dw     | 56x56x128    conv / s1         | 1x1x128x256      | 28x28x128      conv dw / s1      | 3x3x1x256 dw     | 28x28x256    conv / s1         | 1x1x256x256      | 28x28x256      conv dw / s2      | 3x3x1x256 dw     | 28x28x256    conv / s1         | 1x1x256x512      | 14x14x256    --------------------------------------------------    5x         conv dw / s1   | 3x3x1x512 dw     | 14x14x512       conv / s1      | 1x1x512x512      | 14x14x512      --------------------------------------------------    conv dw / s2      | 3x3x1x512 dw     | 14x14x512    conv / s1         | 1x1x512x1024     | 7x7x512      conv dw / s2      | 3x3x1x1024 dw    | 7x7x1024    conv / s1         | 1x1x1024x1024    | 7x7x1024    Avg Pool / s1     | Pool 7x7         | 7x7x1024      FC / s1           | 1204x1000        | 1x1x1024    Softmax / s1      | Classifier       | 1x1x1000    --------------------------------------------------    '''    def _depthwise_separable_conv(inputs,                                  num_pwc_filters,                                  width_multiplier,                                  downsimple=False,                                  scope_name='Depthwise_Conv'):        '''        net structs  == conv dw / sx  + conv /s1   x=downsimple取值        -------------------------        3x3 Depthwise Conv        BN        ReLU        1X1 Conv        BN        ReLU        -------------------------        inputs        '''        ds = 2 if downsimple else 1        dwc = slim.separable_conv2d(inputs,                                    num_outputs=None,                                    kernel_size=[3, 3],                                    stride = ds,                                    depth_multiplier=1,                                    activation_fn=None,                                    scope=scope_name+'/depthwise_conv')        bn = slim.batch_norm(dwc, scope=scope_name+'/dw_bn')        relu = tf.nn.relu(bn, name=scope_name+'/dw_relu')        conv = slim.conv2d(relu,                       num_pwc_filters,                       kernel_size=[1, 1],                       activation_fn=None,                       scope=scope_name+'/conv2d')        bn = slim.batch_norm(conv, activation_fn=tf.nn.relu, scope=scope_name+'/conv_bn')        #relu = tf.nn.relu(bn, name=scope_name+'/conv_relu')        return bn    def _standard_conv(inputs,                       num_outputs,                       scope_name="std_conv"):        '''        net structs        -------------------------        3x3 conv        BN        ReLU        -------------------------        '''        conv = slim.conv2d(inputs,                       num_outputs=num_outputs,                       kernel_size=[3,3],                       activation_fn=None,                       scope=scope_name+'/conv')        bn = slim.batch_norm(conv, activation_fn=tf.nn.relu, scope=scope_name+"/bn")        return bn    with tf.variable_scope(scope) as sc:        end_points_collection = sc+'_end_points'        with slim.arg_scope([slim.conv2d, slim.separable_conv2d],                            activation_fn=None,                            outputs_collections=[end_points_collection]):            with slim.arg_scope([slim.batch_norm],                                is_training=is_training,                                activation_fn=tf.nn.relu,                                fused=True)                net = slim.conv2d(inputs, round(32 * width_multiplier), [3, 3], stride=2, padding='SAME', scope='conv_1')                net = slim.batch_norm(net, scope='conv_1/bn')                net = _depthwise_separable_conv(net, 64, width_multiplier, scope_name='conv_ds_2')                net = _depthwise_separable_conv(net, 128, width_multiplier, downsimple=True, scope_name='conv_ds_3')                net = _depthwise_separable_conv(net, 128, width_multiplier, scope_name='conv_ds_4')                net = _depthwise_separable_conv(net, 256, width_multiplier, downsimple=True, scope_name='conv_ds_5')                net = _depthwise_separable_conv(net, 256, width_multiplier, scope_name='conv_ds_6')                net = _depthwise_separable_conv(net, 512, width_multiplier, downsimple=True, scope_name='conv_ds_7')                net = _depthwise_separable_conv(net, 512, width_multiplier, scope_name='conv_ds_8')                net = _depthwise_separable_conv(net, 512, width_multiplier, scope_name='conv_ds_9')                net = _depthwise_separable_conv(net, 512, width_multiplier, scope_name='conv_ds_10')                net = _depthwise_separable_conv(net, 512, width_multiplier, scope_name='conv_ds_11')                net = _depthwise_separable_conv(net, 512, width_multiplier, scope_name='conv_ds_12')                net = _depthwise_separable_conv(net, 1024, width_multiplier, downsimple=True, scope_name='conv_ds_13')                net = _depthwise_separable_conv(net, 1024, width_multiplier, downsimple=True, scope_name='conv_ds_14')                net = slim.avg_pool2d(net, [7, 7], scope='avg_pool_15')            end_points = slim.utils.convert_collection_to_dict(end_points_collection)            net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')            end_points['squeeze'] = net            net = slim.fully_connected(net, num_classes, activation_fn=None, scope='fc_16')            predictions = slim.softmax(logits, scope='Predictions')            end_points['Logits'] = logits            end_points['Predictions'] = predictions        return logits, end_points