从零开始自己搭建复杂网络（以DenseNet为例）
DenseNet 是一种具有密集连接的卷积神经网络。在该网络中，任何两层之间都有直接的连接，也就是说，网络每一层的输入都是前面所有层输出的并集，
而该层所学习的特征图也会被直接传给其后面所有层作为输入。
DenseNet 在 ResNet 基础上，提出了更优秀的 shortcut 方式。Dense Connection 不仅能使得 feature 更加 robust ，还能带来更快的收敛速度。
显存和计算量上稍显不足，需要业界进一步的优化才能广泛应用。
我们使用slim框架来构建网络，进行slim官方的densenet代码的讲解。
"""Contains the definition of the DenseNet architecture. As described in https://arxiv.org/abs/1608.06993. Densely Connected Convolutional Networks Gao Huang, Zhuang Liu, Kilian Q. Weinberger, Laurens van der Maaten """
那么，开始构建整体网络框架吧
densenet的基本网络由以下代码构成
def densenet(inputs, num_classes=1000, reduction=None, growth_rate=None, num_filters=None, num_layers=None, dropout_rate=None, data_format='NHWC', is_training=True, reuse=None, scope=None): assert reduction is not None assert growth_rate is not None assert num_filters is not None assert num_layers is not None compression = 1.0 - reduction num_dense_blocks = len(num_layers) if data_format == 'NCHW': inputs = tf.transpose(inputs, [0, 3, 1, 2]) with tf.variable_scope(scope, 'densenetxxx', [inputs, num_classes], reuse=reuse) as sc: end_points_collection = sc.name + '_end_points' with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training), slim.arg_scope([slim.conv2d, _conv, _conv_block, _dense_block, _transition_block], outputs_collections=end_points_collection), slim.arg_scope([_conv], dropout_rate=dropout_rate): net = inputs # initial convolution net = slim.conv2d(net, num_filters, 7, stride=2, scope='conv1') net = slim.batch_norm(net) net = tf.nn.relu(net) net = slim.max_pool2d(net, 3, stride=2, padding='SAME') # blocks for i in range(num_dense_blocks - 1): # dense blocks net, num_filters = _dense_block(net, num_layers[i], num_filters, growth_rate, scope='dense_block' + str(i+1)) # Add transition_block net, num_filters = _transition_block(net, num_filters, compression=compression, scope='transition_block' + str(i+1)) net, num_filters = _dense_block( net, num_layers[-1], num_filters, growth_rate, scope='dense_block' + str(num_dense_blocks)) # final blocks with tf.variable_scope('final_block', [inputs]): net = slim.batch_norm(net) net = tf.nn.relu(net) net = _global_avg_pool2d(net, scope='global_avg_pool') net = slim.conv2d(net, num_classes, 1, biases_initializer=tf.zeros_initializer(), scope='logits') end_points = slim.utils.convert_collection_to_dict( end_points_collection) if num_classes is not None: end_points['predictions'] = slim.softmax(net, scope='predictions') return net, end_points
纵观论文的网络结构，Densenet由4个部分组成：
- initial convolution
- dense blocks
- transition_block
- final blocks
初始卷积层拥有
conv2d
batch_norm
relu
max_pool2d
这四个方法在开始定义了
with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training), slim.arg_scope([slim.conv2d, _conv, _conv_block, _dense_block, _transition_block], outputs_collections=end_points_collection), slim.arg_scope([_conv], dropout_rate=dropout_rate):
然后我们定义_dense_block
@slim.add_arg_scope def _dense_block(inputs, num_layers, num_filters, growth_rate, grow_num_filters=True, scope=None, outputs_collections=None): with tf.variable_scope(scope, 'dense_blockx', [inputs]) as sc: net = inputs for i in range(num_layers): branch = i + 1 net = _conv_block(net, growth_rate, scope='conv_block'+str(branch)) if grow_num_filters: num_filters += growth_rate net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net) return net, num_filters
_dense_block中由不同个数的_conv_block组成。拿densenet121来说，卷积的个数为[6,12,24,16]。
_conv_block由一个1×1的卷积和3×3的卷积组合而成，之后将两个卷积融合起来。
@slim.add_arg_scope def _conv_block(inputs, num_filters, data_format='NHWC', scope=None, outputs_collections=None): with tf.variable_scope(scope, 'conv_blockx', [inputs]) as sc: net = inputs net = _conv(net, num_filters*4, 1, scope='x1') net = _conv(net, num_filters, 3, scope='x2') if data_format == 'NHWC': #在某一个shape的第三个维度上连 net = tf.concat([inputs, net], axis=3) else: # "NCHW" net = tf.concat([inputs, net], axis=1) net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net) return net
接着，我们构建_transition_block：
@slim.add_arg_scope def _transition_block(inputs, num_filters, compression=1.0, scope=None, outputs_collections=None): num_filters = int(num_filters * compression) with tf.variable_scope(scope, 'transition_blockx', [inputs]) as sc: net = inputs net = _conv(net, num_filters, 1, scope='blk') net = slim.avg_pool2d(net, 2) net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net) return net, num_filters
这个模块由一个1×1 的卷积对其维度，然后接平均池化。
最后一层，我们使用1×1的卷积将输出维度与最后的分类数对其。
# final blocks with tf.variable_scope('final_block', [inputs]): net = slim.batch_norm(net) net = tf.nn.relu(net) net = _global_avg_pool2d(net, scope='global_avg_pool') net = slim.conv2d(net, num_classes, 1, biases_initializer=tf.zeros_initializer(), scope='logits') net = tf.contrib.layers.flatten(net)
Densenet的每个模块就介绍完毕了
下面是全部的代码：
# Copyright 2016 pudae. All Rights Reserved. # # Licensed 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. # ============================================================================== """Contains the definition of the DenseNet architecture. As described in https://arxiv.org/abs/1608.06993. Densely Connected Convolutional Networks Gao Huang, Zhuang Liu, Kilian Q. Weinberger, Laurens van der Maaten """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf slim = tf.contrib.slim @slim.add_arg_scope def _global_avg_pool2d(inputs, data_format='NHWC', scope=None, outputs_collections=None): with tf.variable_scope(scope, 'xx', [inputs]) as sc: axis = [1, 2] if data_format == 'NHWC' else [2, 3] net = tf.reduce_mean(inputs, axis=axis, keepdims=True) net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net) return net @slim.add_arg_scope def _conv(inputs, num_filters, kernel_size, stride=1, dropout_rate=None, scope=None, outputs_collections=None): with tf.variable_scope(scope, 'xx', [inputs]) as sc: net = slim.batch_norm(inputs) net = tf.nn.relu(net) net = slim.conv2d(net, num_filters, kernel_size) if dropout_rate: net = tf.nn.dropout(net) net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net) return net @slim.add_arg_scope def _conv_block(inputs, num_filters, data_format='NHWC', scope=None, outputs_collections=None): with tf.variable_scope(scope, 'conv_blockx', [inputs]) as sc: net = inputs net = _conv(net, num_filters*4, 1, scope='x1') net = _conv(net, num_filters, 3, scope='x2') if data_format == 'NHWC': net = tf.concat([inputs, net], axis=3) else: # "NCHW" net = tf.concat([inputs, net], axis=1) net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net) return net @slim.add_arg_scope def _dense_block(inputs, num_layers, num_filters, growth_rate, grow_num_filters=True, scope=None, outputs_collections=None): with tf.variable_scope(scope, 'dense_blockx', [inputs]) as sc: net = inputs for i in range(num_layers): branch = i + 1 net = _conv_block(net, growth_rate, scope='conv_block'+str(branch)) if grow_num_filters: num_filters += growth_rate net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net) return net, num_filters @slim.add_arg_scope def _transition_block(inputs, num_filters, compression=1.0, scope=None, outputs_collections=None): num_filters = int(num_filters * compression) with tf.variable_scope(scope, 'transition_blockx', [inputs]) as sc: net = inputs net = _conv(net, num_filters, 1, scope='blk') net = slim.avg_pool2d(net, 2) net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net) return net, num_filters def densenet(inputs, num_classes=1000, reduction=None, growth_rate=None, num_filters=None, num_layers=None, dropout_rate=None, data_format='NHWC', is_training=True, reuse=None, scope=None): assert reduction is not None assert growth_rate is not None assert num_filters is not None assert num_layers is not None compression = 1.0 - reduction num_dense_blocks = len(num_layers) if data_format == 'NCHW': inputs = tf.transpose(inputs, [0, 3, 1, 2]) with tf.variable_scope(scope, 'densenetxxx', [inputs, num_classes], reuse=reuse) as sc: end_points_collection = sc.name + '_end_points' with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training), slim.arg_scope([slim.conv2d, _conv, _conv_block, _dense_block, _transition_block], outputs_collections=end_points_collection), slim.arg_scope([_conv], dropout_rate=dropout_rate): net = inputs # initial convolution net = slim.conv2d(net, num_filters, 7, stride=2, scope='conv1') net = slim.batch_norm(net) net = tf.nn.relu(net) net = slim.max_pool2d(net, 3, stride=2, padding='SAME') # blocks for i in range(num_dense_blocks - 1): # dense blocks net, num_filters = _dense_block(net, num_layers[i], num_filters, growth_rate, scope='dense_block' + str(i+1)) # Add transition_block net, num_filters = _transition_block(net, num_filters, compression=compression, scope='transition_block' + str(i+1)) net, num_filters = _dense_block( net, num_layers[-1], num_filters, growth_rate, scope='dense_block' + str(num_dense_blocks)) # final blocks with tf.variable_scope('final_block', [inputs]): net = slim.batch_norm(net) net = tf.nn.relu(net) net = _global_avg_pool2d(net, scope='global_avg_pool') net = slim.conv2d(net, num_classes, 1, biases_initializer=tf.zeros_initializer(), scope='logits') net = tf.contrib.layers.flatten(net) # print(net) end_points = slim.utils.convert_collection_to_dict( end_points_collection) if num_classes is not None: end_points['predictions'] = slim.softmax(net, scope='predictions') return net, end_points def densenet121(inputs, num_classes=1000, data_format='NHWC', is_training=True, reuse=None): return densenet(inputs, num_classes=num_classes, reduction=0.5, growth_rate=32, num_filters=64, num_layers=[6,12,24,16], data_format=data_format, is_training=is_training, reuse=reuse, scope='densenet121') densenet121.default_image_size = 224 def densenet161(inputs, num_classes=1000, data_format='NHWC', is_training=True, reuse=None): return densenet(inputs, num_classes=num_classes, reduction=0.5, growth_rate=48, num_filters=96, num_layers=[6,12,36,24], data_format=data_format, is_training=is_training, reuse=reuse, scope='densenet161') densenet161.default_image_size = 224 def densenet169(inputs, num_classes=1000, data_format='NHWC', is_training=True, reuse=None): return densenet(inputs, num_classes=num_classes, reduction=0.5, growth_rate=32, num_filters=64, num_layers=[6,12,32,32], data_format=data_format, is_training=is_training, reuse=reuse, scope='densenet169') densenet169.default_image_size = 224 def densenet_arg_scope(weight_decay=1e-4, batch_norm_decay=0.99, batch_norm_epsilon=1.1e-5, data_format='NHWC'): with slim.arg_scope([slim.conv2d, slim.batch_norm, slim.avg_pool2d, slim.max_pool2d, _conv_block, _global_avg_pool2d], data_format=data_format): with slim.arg_scope([slim.conv2d], weights_regularizer=slim.l2_regularizer(weight_decay), activation_fn=None, biases_initializer=None): with slim.arg_scope([slim.batch_norm], scale=True, decay=batch_norm_decay, epsilon=batch_norm_epsilon) as scope: return scope
