本月1日起，上海正式開始了“史上最嚴“垃圾分類的規(guī)定，扔錯垃圾最高可罰200元。全國其它46個城市也要陸續(xù)步入垃圾分類新時代。各種被垃圾分類逼瘋的段子在社交媒體上層出不窮。

其實從人工智能的角度看垃圾分類就是圖像處理中圖像分類任務的一種應用，而這在2012年以來的ImageNet圖像分類任務的評比中，SENet模型以top-5測試集回歸2.25%錯誤率的成績可謂是技壓群雄，堪稱目前最強的圖像分類器。

筆者剛剛還到SENet的創(chuàng)造者momenta公司的網(wǎng)站上看了一下，他們最新的方向已經(jīng)是3D物體識別和標定了，效果如下：

可以說他們提出的SENet進行垃圾圖像處理是完全沒問題的。

Senet簡介

Senet的是由momenta和牛津大學共同提出的一種基于擠壓(squeeze）和激勵（Excitation）的模型，每個模塊通過“擠壓”操作嵌入來自全局感受野的信息，并且通過“激勵”操作選擇性地誘導響應增強。我們可以看到歷年的ImageNet冠軍基本都是在使用加大模型數(shù)量和連接數(shù)量的方式來提高精度，而Senet在這種”大力出奇跡”的潮流下明顯是一股清流。其論文地址如下：http://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.pdf

其具體原理說明如下：

Sequeeze：對 C×H×W 進行 global average pooling，得到 1×1×C 大小的特征圖，這個特征圖可以理解為具有全局感受野。翻譯論文原文來說：將每個二維的特征通道變成一個實數(shù)，這個實數(shù)某種程度上具有全局的感受野，并且輸出的維度和輸入的特征通道數(shù)相匹配。它表征著在特征通道上響應的全局分布，而且使得靠近輸入的層也可以獲得全局的感受野。

Excitation ：使用一個全連接神經(jīng)網(wǎng)絡，對 Sequeeze 之后的結(jié)果做一個非線性變換。它的機制一個類似于循環(huán)神經(jīng)網(wǎng)絡中的門。通過參數(shù) w 來為每個特征通道生成權重，其中參數(shù) w 被學習用來顯式地建模特征通道間的相關性。

特征重標定：使用 Excitation 得到的結(jié)果作為權重，乘到輸入特征上。將Excitation輸出的權重可以認為是特征通道的重要性反應，逐通道加權到放到先前的特征上，完成對原始特征的重標定。

其模型架構如下：

SENet 構造非常簡單，而且很容易被部署，不需要引入新的函數(shù)或者層。其caffe模型可以通過百度下載（https://pan.baidu.com/s/1o7HdfAE?errno=0&errmsg=Auth%20Login%20Sucess&&bduss=&ssnerror=0&traceid=）

Senet的運用

如果讀者布署有caffe那么直接下載剛剛的模型直接load進來就可以使用了。如果沒有裝caffe而裝了tensorflow也沒關系，我們剛剛說了SENet沒有引入新的函數(shù)和層，很方便用tensorflow實現(xiàn)。

下載圖像集：經(jīng)筆者各方查找發(fā)現(xiàn)了這個數(shù)據(jù)集，雖然不大也沒有發(fā)揮出SENet的優(yōu)勢，不過也方便使用：

https://raw.githubusercontent.com/garythung/trashnet/master/data/dataset-resized.zip

建立SENet模型：使用tensorflow建立的模型在github上也有開源項目了，網(wǎng)址如下：https://github.com/taki0112/SENet-Tensorflow，只是他使用的是Cifar10數(shù)據(jù)集，不過這也沒關系，只需要在gitclone以下將其cifar10.py中的prepare_data函數(shù)做如下修改即可。

1defprepare_data(): 2print("======Loadingdata======") 3download_data() 4data_dir='e:/test/' 5#data_dir='./cifar-10-batches-py'#改為你的文件俠 6image_dim=image_size*image_size*img_channels 7#meta=unpickle(data_dir+'/batches.meta')#本數(shù)據(jù)集不使用meta文件分類，故需要修改 8label_names=['cardboard','glass','metal','trash','paper','plastic'] 9label_count=len(label_names)10#train_files=['data_batch_%d'%dfordinrange(1,6)]11train_files=[data_dir+sforsinlabel_names]#改為12train_data,train_labels=load_data(train_files,data_dir,label_count)13test_data,test_labels=load_data(['test_batch'],data_dir,label_count)1415print("Traindata:",np.shape(train_data),np.shape(train_labels))16print("Testdata:",np.shape(test_data),np.shape(test_labels))17print("======Loadfinished======")1819print("======Shufflingdata======")20indices=np.random.permutation(len(train_data))21train_data=train_data[indices]22train_labels=train_labels[indices]23print("======PrepareFinished======")2425returntrain_data,train_labels,test_data,test_labels

其最主要的建模代碼如下，其主要工作就是將SENet的模型結(jié)構實現(xiàn)一下即可：

1importtensorflowastf 2fromtflearn.layers.convimportglobal_avg_pool 3fromtensorflow.contrib.layersimportbatch_norm,flatten 4fromtensorflow.contrib.frameworkimportarg_scope 5fromcifar10import* 6importnumpyasnp 7 8weight_decay=0.0005 9momentum=0.9 10 11init_learning_rate=0.1 12 13reduction_ratio=4 14 15batch_size=128 16iteration=391 17#128*391~50,000 18 19test_iteration=10 20 21total_epochs=100 22 23defconv_layer(input,filter,kernel,stride=1,padding='SAME',layer_name="conv",activation=True): 24withtf.name_scope(layer_name): 25network=tf.layers.conv2d(inputs=input,use_bias=True,filters=filter,kernel_size=kernel,strides=stride,padding=padding) 26ifactivation: 27network=Relu(network) 28returnnetwork 29 30defFully_connected(x,units=class_num,layer_name='fully_connected'): 31withtf.name_scope(layer_name): 32returntf.layers.dense(inputs=x,use_bias=True,units=units) 33 34defRelu(x): 35returntf.nn.relu(x) 36 37defSigmoid(x): 38returntf.nn.sigmoid(x) 39 40defGlobal_Average_Pooling(x): 41returnglobal_avg_pool(x,name='Global_avg_pooling') 42 43defMax_pooling(x,pool_size=[3,3],stride=2,padding='VALID'): 44returntf.layers.max_pooling2d(inputs=x,pool_size=pool_size,strides=stride,padding=padding) 45 46defBatch_Normalization(x,training,scope): 47witharg_scope([batch_norm], 48scope=scope, 49updates_collections=None, 50decay=0.9, 51center=True, 52scale=True, 53zero_debias_moving_mean=True): 54returntf.cond(training, 55lambda:batch_norm(inputs=x,is_training=training,reuse=None), 56lambda:batch_norm(inputs=x,is_training=training,reuse=True)) 57 58defConcatenation(layers): 59returntf.concat(layers,axis=3) 60 61defDropout(x,rate,training): 62returntf.layers.dropout(inputs=x,rate=rate,training=training) 63 64defEvaluate(sess): 65test_acc=0.0 66test_loss=0.0 67test_pre_index=0 68add=1000 69 70foritinrange(test_iteration): 71test_batch_x=test_x[test_pre_index:test_pre_index+add] 72test_batch_y=test_y[test_pre_index:test_pre_index+add] 73test_pre_index=test_pre_index+add 74 75test_feed_dict={ 76x:test_batch_x, 77label:test_batch_y, 78learning_rate:epoch_learning_rate, 79training_flag:False 80} 81 82loss_,acc_=sess.run([cost,accuracy],feed_dict=test_feed_dict) 83 84test_loss+=loss_ 85test_acc+=acc_ 86 87test_loss/=test_iteration#averageloss 88test_acc/=test_iteration#averageaccuracy 89 90summary=tf.Summary(value=[tf.Summary.Value(tag='test_loss',simple_value=test_loss), 91tf.Summary.Value(tag='test_accuracy',simple_value=test_acc)]) 92 93returntest_acc,test_loss,summary 94 95classSE_Inception_resnet_v2(): 96def__init__(self,x,training): 97self.training=training 98self.model=self.Build_SEnet(x) 99100defStem(self,x,scope):101withtf.name_scope(scope):102x=conv_layer(x,filter=32,kernel=[3,3],stride=2,padding='VALID',layer_name=scope+'_conv1')103x=conv_layer(x,filter=32,kernel=[3,3],padding='VALID',layer_name=scope+'_conv2')104block_1=conv_layer(x,filter=64,kernel=[3,3],layer_name=scope+'_conv3')105106split_max_x=Max_pooling(block_1)107split_conv_x=conv_layer(block_1,filter=96,kernel=[3,3],stride=2,padding='VALID',layer_name=scope+'_split_conv1')108x=Concatenation([split_max_x,split_conv_x])109110split_conv_x1=conv_layer(x,filter=64,kernel=[1,1],layer_name=scope+'_split_conv2')111split_conv_x1=conv_layer(split_conv_x1,filter=96,kernel=[3,3],padding='VALID',layer_name=scope+'_split_conv3')112113split_conv_x2=conv_layer(x,filter=64,kernel=[1,1],layer_name=scope+'_split_conv4')114split_conv_x2=conv_layer(split_conv_x2,filter=64,kernel=[7,1],layer_name=scope+'_split_conv5')115split_conv_x2=conv_layer(split_conv_x2,filter=64,kernel=[1,7],layer_name=scope+'_split_conv6')116split_conv_x2=conv_layer(split_conv_x2,filter=96,kernel=[3,3],padding='VALID',layer_name=scope+'_split_conv7')117118x=Concatenation([split_conv_x1,split_conv_x2])119120split_conv_x=conv_layer(x,filter=192,kernel=[3,3],stride=2,padding='VALID',layer_name=scope+'_split_conv8')121split_max_x=Max_pooling(x)122123x=Concatenation([split_conv_x,split_max_x])124125x=Batch_Normalization(x,training=self.training,scope=scope+'_batch1')126x=Relu(x)127128returnx129130defInception_resnet_A(self,x,scope):131withtf.name_scope(scope):132init=x133134split_conv_x1=conv_layer(x,filter=32,kernel=[1,1],layer_name=scope+'_split_conv1')135136split_conv_x2=conv_layer(x,filter=32,kernel=[1,1],layer_name=scope+'_split_conv2')137split_conv_x2=conv_layer(split_conv_x2,filter=32,kernel=[3,3],layer_name=scope+'_split_conv3')138139split_conv_x3=conv_layer(x,filter=32,kernel=[1,1],layer_name=scope+'_split_conv4')140split_conv_x3=conv_layer(split_conv_x3,filter=48,kernel=[3,3],layer_name=scope+'_split_conv5')141split_conv_x3=conv_layer(split_conv_x3,filter=64,kernel=[3,3],layer_name=scope+'_split_conv6')142143x=Concatenation([split_conv_x1,split_conv_x2,split_conv_x3])144x=conv_layer(x,filter=384,kernel=[1,1],layer_name=scope+'_final_conv1',activation=False)145146x=x*0.1147x=init+x148149x=Batch_Normalization(x,training=self.training,scope=scope+'_batch1')150x=Relu(x)151152returnx153154defInception_resnet_B(self,x,scope):155withtf.name_scope(scope):156init=x157158split_conv_x1=conv_layer(x,filter=192,kernel=[1,1],layer_name=scope+'_split_conv1')159160split_conv_x2=conv_layer(x,filter=128,kernel=[1,1],layer_name=scope+'_split_conv2')161split_conv_x2=conv_layer(split_conv_x2,filter=160,kernel=[1,7],layer_name=scope+'_split_conv3')162split_conv_x2=conv_layer(split_conv_x2,filter=192,kernel=[7,1],layer_name=scope+'_split_conv4')163164x=Concatenation([split_conv_x1,split_conv_x2])165x=conv_layer(x,filter=1152,kernel=[1,1],layer_name=scope+'_final_conv1',activation=False)166#1154167x=x*0.1168x=init+x169170x=Batch_Normalization(x,training=self.training,scope=scope+'_batch1')171x=Relu(x)172173returnx174175defInception_resnet_C(self,x,scope):176withtf.name_scope(scope):177init=x178179split_conv_x1=conv_layer(x,filter=192,kernel=[1,1],layer_name=scope+'_split_conv1')180181split_conv_x2=conv_layer(x,filter=192,kernel=[1,1],layer_name=scope+'_split_conv2')182split_conv_x2=conv_layer(split_conv_x2,filter=224,kernel=[1,3],layer_name=scope+'_split_conv3')183split_conv_x2=conv_layer(split_conv_x2,filter=256,kernel=[3,1],layer_name=scope+'_split_conv4')184185x=Concatenation([split_conv_x1,split_conv_x2])186x=conv_layer(x,filter=2144,kernel=[1,1],layer_name=scope+'_final_conv2',activation=False)187#2048188x=x*0.1189x=init+x190191x=Batch_Normalization(x,training=self.training,scope=scope+'_batch1')192x=Relu(x)193194returnx195196defReduction_A(self,x,scope):197withtf.name_scope(scope):198k=256199l=256200m=384201n=384202203split_max_x=Max_pooling(x)204205split_conv_x1=conv_layer(x,filter=n,kernel=[3,3],stride=2,padding='VALID',layer_name=scope+'_split_conv1')206207split_conv_x2=conv_layer(x,filter=k,kernel=[1,1],layer_name=scope+'_split_conv2')208split_conv_x2=conv_layer(split_conv_x2,filter=l,kernel=[3,3],layer_name=scope+'_split_conv3')209split_conv_x2=conv_layer(split_conv_x2,filter=m,kernel=[3,3],stride=2,padding='VALID',layer_name=scope+'_split_conv4')210211x=Concatenation([split_max_x,split_conv_x1,split_conv_x2])212213x=Batch_Normalization(x,training=self.training,scope=scope+'_batch1')214x=Relu(x)215216returnx217218defReduction_B(self,x,scope):219withtf.name_scope(scope):220split_max_x=Max_pooling(x)221222split_conv_x1=conv_layer(x,filter=256,kernel=[1,1],layer_name=scope+'_split_conv1')223split_conv_x1=conv_layer(split_conv_x1,filter=384,kernel=[3,3],stride=2,padding='VALID',layer_name=scope+'_split_conv2')224225split_conv_x2=conv_layer(x,filter=256,kernel=[1,1],layer_name=scope+'_split_conv3')226split_conv_x2=conv_layer(split_conv_x2,filter=288,kernel=[3,3],stride=2,padding='VALID',layer_name=scope+'_split_conv4')227228split_conv_x3=conv_layer(x,filter=256,kernel=[1,1],layer_name=scope+'_split_conv5')229split_conv_x3=conv_layer(split_conv_x3,filter=288,kernel=[3,3],layer_name=scope+'_split_conv6')230split_conv_x3=conv_layer(split_conv_x3,filter=320,kernel=[3,3],stride=2,padding='VALID',layer_name=scope+'_split_conv7')231232x=Concatenation([split_max_x,split_conv_x1,split_conv_x2,split_conv_x3])233234x=Batch_Normalization(x,training=self.training,scope=scope+'_batch1')235x=Relu(x)236237returnx238239defSqueeze_excitation_layer(self,input_x,out_dim,ratio,layer_name):240withtf.name_scope(layer_name):241242243squeeze=Global_Average_Pooling(input_x)244245excitation=Fully_connected(squeeze,units=out_dim/ratio,layer_name=layer_name+'_fully_connected1')246excitation=Relu(excitation)247excitation=Fully_connected(excitation,units=out_dim,layer_name=layer_name+'_fully_connected2')248excitation=Sigmoid(excitation)249250excitation=tf.reshape(excitation,[-1,1,1,out_dim])251scale=input_x*excitation252253returnscale254255defBuild_SEnet(self,input_x):256input_x=tf.pad(input_x,[[0,0],[32,32],[32,32],[0,0]])257#size32->96258print(np.shape(input_x))259#onlycifar10architecture260261x=self.Stem(input_x,scope='stem')262263foriinrange(5):264x=self.Inception_resnet_A(x,scope='Inception_A'+str(i))265channel=int(np.shape(x)[-1])266x=self.Squeeze_excitation_layer(x,out_dim=channel,ratio=reduction_ratio,layer_name='SE_A'+str(i))267268x=self.Reduction_A(x,scope='Reduction_A')269270channel=int(np.shape(x)[-1])271x=self.Squeeze_excitation_layer(x,out_dim=channel,ratio=reduction_ratio,layer_name='SE_A')272273foriinrange(10):274x=self.Inception_resnet_B(x,scope='Inception_B'+str(i))275channel=int(np.shape(x)[-1])276x=self.Squeeze_excitation_layer(x,out_dim=channel,ratio=reduction_ratio,layer_name='SE_B'+str(i))277278x=self.Reduction_B(x,scope='Reduction_B')279280channel=int(np.shape(x)[-1])281x=self.Squeeze_excitation_layer(x,out_dim=channel,ratio=reduction_ratio,layer_name='SE_B')282283foriinrange(5):284x=self.Inception_resnet_C(x,scope='Inception_C'+str(i))285channel=int(np.shape(x)[-1])286x=self.Squeeze_excitation_layer(x,out_dim=channel,ratio=reduction_ratio,layer_name='SE_C'+str(i))287288289#channel=int(np.shape(x)[-1])290#x=self.Squeeze_excitation_layer(x,out_dim=channel,ratio=reduction_ratio,layer_name='SE_C')291292x=Global_Average_Pooling(x)293x=Dropout(x,rate=0.2,training=self.training)294x=flatten(x)295296x=Fully_connected(x,layer_name='final_fully_connected')297returnx298299300train_x,train_y,test_x,test_y=prepare_data()301train_x,test_x=color_preprocessing(train_x,test_x)302303304#image_size=32,img_channels=3,class_num=10incifar10305x=tf.placeholder(tf.float32,shape=[None,image_size,image_size,img_channels])306label=tf.placeholder(tf.float32,shape=[None,class_num])307308training_flag=tf.placeholder(tf.bool)309310311learning_rate=tf.placeholder(tf.float32,name='learning_rate')312313logits=SE_Inception_resnet_v2(x,training=training_flag).model314cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=label,logits=logits))315316l2_loss=tf.add_n([tf.nn.l2_loss(var)forvarintf.trainable_variables()])317optimizer=tf.train.MomentumOptimizer(learning_rate=learning_rate,momentum=momentum,use_nesterov=True)318train=optimizer.minimize(cost+l2_loss*weight_decay)319320correct_prediction=tf.equal(tf.argmax(logits,1),tf.argmax(label,1))321accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))322323saver=tf.train.Saver(tf.global_variables())324325withtf.Session()assess:326ckpt=tf.train.get_checkpoint_state('./model')327ifckptandtf.train.checkpoint_exists(ckpt.model_checkpoint_path):328saver.restore(sess,ckpt.model_checkpoint_path)329else:330sess.run(tf.global_variables_initializer())331332summary_writer=tf.summary.FileWriter('./logs',sess.graph)333334epoch_learning_rate=init_learning_rate335forepochinrange(1,total_epochs+1):336ifepoch%30==0:337epoch_learning_rate=epoch_learning_rate/10338339pre_index=0340train_acc=0.0341train_loss=0.0342343forstepinrange(1,iteration+1):344ifpre_index+batch_size

其實使用SENet做垃圾分類真是大才小用了，不過大家也可以感受一下他的實力強大。