【Tensorflow slim 实战】写MobileNet

# -*- coding:utf-8 -*-
import tensorflow as tf
import tensorflow.contrib.slim as slim

def 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