MxNet 迁移学习实现深度学习分类

利用MxNet实现图像分类任务

这篇文章将利用MxNet以及其前端gluon 实现一个完整的图像分类任务，其中主要包括以下几个方面：

• 图像I/O
• 搭建网络
• 进行训练
• 验证算法
• 输出结果

---

1. 训练数据I/O

将处理好的训练数据读入，进行训练。

训练数据的格式基本按照一个子类一个子文件夹的形式保持，具体可以参考MXNet的数据I/O

1.1 程序的第一步，首先导入相关的包

#import some packages
import sys
import collections
import datetime			#用于计时
import gluonbook as gb		#用于导入一些功能函数
import math
import numpy as np
import mxnet as mx    		#mxnet
from mxnet import autograd, gluon, init, nd, image    #导入自动梯度，gluon前端，图像等模块
from mxnet.gluon import data as gdata, loss as gloss, model_zoo, nn   #导入模型相关模块
import os
import shutil     		#用于预处理复制文件
import zipfile
import matplotlib.pyplot as plt	#绘图工具导入

1.2 随后定义精度计算函数、图像增广函数等辅助函数

# 图像增广和辅助函数
# 计算 Average Precision
def calculate_ap(labels, outputs):
    cnt = 0
    ap = 0.
    for label, output in zip(labels, outputs):
        for lb, op in zip(label.asnumpy().astype(np.int),
                          output.asnumpy()):
            op_argsort = np.argsort(op)[::-1]    #输出排序后的index，最大概率的值对应的index
            lb_int = int(lb)    #标签对应的整数
            ap += 1.0 / (1+list(op_argsort).index(lb_int))    #精度计算 正确的个数
            cnt += 1
    return ((ap, cnt))

# 训练集图片增广
def transform_train(data, label):
    im = data.astype('float32') / 255		#归并到0~1之间
    #图像增强的函数组定义，并利用ImageNet的预训练均值、方差归一化输入图像
    auglist = image.CreateAugmenter(data_shape=(3, 224, 224), resize=256,
                                    rand_crop=True, rand_mirror=True,
                                    mean = np.array([0.485, 0.456, 0.406]),
                                    std = np.array([0.229, 0.224, 0.225]))			
    for aug in auglist:
        im = aug(im)
    im = nd.transpose(im, (2,0,1))    #改变
    return (im, nd.array([label]).asscalar())

# 验证集图片增广，没有随机裁剪和翻转
def transform_val(data, label):
    im = data.astype('float32') / 255
    auglist = image.CreateAugmenter(data_shape=(3, 224, 224), resize=256,
                                    mean = np.array([0.485, 0.456, 0.406]),
                                    std = np.array([0.229, 0.224, 0.225]))
    for aug in auglist:
        im = aug(im)
    im = nd.transpose(im, (2,0,1))    #改变格式为 channel width height
    return (im, nd.array([label]).asscalar())

# 在验证集上预测并评估
def validate(net, val_data, ctx):
    metric = mx.metric.Accuracy()
    L = gluon.loss.SoftmaxCrossEntropyLoss()
    AP = 0.
    AP_cnt = 0
    val_loss = 0
    for i, batch in enumerate(val_data):
        data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, 
                                          batch_axis=0, even_split=False)
        label = gluon.utils.split_and_load(batch[1], ctx_list=ctx, 
                                           batch_axis=0, even_split=False)
        outputs = [net(X) for X in data]
        metric.update(label, outputs)
        loss = [L(yhat, y) for yhat, y in zip(outputs, label)]
        val_loss += sum([l.mean().asscalar() for l in loss]) / len(loss)    #平均损失
        ap, cnt = calculate_ap(label, outputs)
        AP += ap
        AP_cnt += cnt    #精度也要求平均
    _, val_acc = metric.get()
    return ((val_acc, AP / AP_cnt, val_loss / len(val_data)))

1.3 读取训练和验证数据

这时候可以利用gluon的内置函数来对数据进行读取了，只需要输入对应数据的文件夹即可，参考MXNet I/O

#读取数据文件
train_set = gdata.vision.ImageFolderDataset('./train_dis/',flag=1)
valid_set = gdata.vision.ImageFolderDataset('./valid_dis/',flag=1)
#check data classes
print(train_set)  #check数据的长度是否正确，应为训练图像总数量
print(train_set.synsets)  #also has items attributes，现实分类别是否正确，应为类别数目
print(valid_set)
print(valid_set.synsets)  #also has items attributes

<mxnet.gluon.data.vision.datasets.ImageFolderDataset object at 0x7fb3d6e06710>
['0', '1', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '2', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '3', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '4', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '5', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '6', '60', '7', '8', '9']
<mxnet.gluon.data.vision.datasets.ImageFolderDataset object at 0x7fb3d6e06668>
['0', '1', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '2', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '3', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '4', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '5', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '6', '60', '7', '8', '9']

得到输入序列后，将图像读入迭代器中，根据显存设置批量的大小。

#data into iter and realized argumentation
batch_size = 64  #32--2821M  could be 64

train_iter = gdata.DataLoader(train_set.transform(transform_train),
                              batch_size, shuffle=True, last_batch='keep', num_workers=4)
valid_iter = gdata.DataLoader(valid_set.transform(transform_val),
                              batch_size, shuffle=True, last_batch='keep', num_workers=4)

读入后check迭代器的数据，并显示目测

# check the data set in iter
print("trainiter lenght is: %d"%len(train_iter))
import matplotlib.pyplot as plt
for imgs, labels in train_iter:
    print(labels)    #打印label 对应类别label
    print(imgs.shape)    #查看batch图像的维度
    break    #读入一个batch
#show images
nor_parms = [[0.485, 0.456, 0.406],[0.229, 0.224, 0.225]]
#_,figs = plt.subplots(8,4,figsize=(8,4))
for i in range(8):
    for j in range(4):
        x = nd.transpose(imgs[i*4+j,:,:,:],(1,2,0)).asnumpy()
        print(x.shape,type(x))    #查看batch中图像的维度和类型
        #x[:,:,0]*nor_parms[0][0]+nor_parms[1][0]
        #x[:,:,1]*nor_parms[0][1]+nor_parms[1][1]
        #x[:,:,2]*nor_parms[0][2]+nor_parms[1][2]
        plt.imshow(x)
        plt.show()
        break

trainiter lenght is: 512    #总共有512个batch，每个batch有64个训练数据

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

[35.  0. 31. 19. 38. 33. 35. 33. 19. 25. 16. 26. 36. 52. 18. 16. 27. 23.
 19.  4. 19. 38. 38. 11. 41. 36. 22. 36. 29. 57. 26. 55. 18. 55. 55. 16.
 27. 26. 55. 10. 19. 21. 23. 19. 50. 56. 31. 14. 20. 19.  8. 54. 57.  8.
 52. 19. 56. 57. 17. 42. 18.  0. 23. 55.]
<NDArray 64 @cpu_shared(0)>
(64, 3, 224, 224)
(224, 224, 3) <class 'numpy.ndarray'>

2.定义模型

这里主要使用迁移学习的方式，利用预训练模型抽取图像的基本特征，而后只需要训练最后的输出层来进行分类。

#define the net work by pre-train
def get_net(ctx):
    resnet = model_zoo.vision.resnet50_v2(pretrained=True)  #ctx  使用resnet_50作为基本网络抽取特征
    resnet.output_new = nn.HybridSequential(prefix='')     #output is the origin  得到特征，新定义一个输出
    #add two fcn for finetune
    resnet.output_new.add(nn.Dense(256,activation = 'relu'))   #在模型基础上，定义最后两个全连接层
    resnet.output_new.add(nn.Dense(61))
    #initialize
    resnet.output_new.initialize(init.Xavier(),ctx=ctx)  #for fintune
    resnet.collect_params().reset_ctx(ctx)           #for whole net
    return resnet    

定义损失函数，这里主要使用分类的softmax交叉熵来作为损失。

#for loss
loss = gloss.SoftmaxCrossEntropyLoss()    #分类损失交叉熵
def get_loss(data,net,ctx):
    l=0.0  #loss
    for X,y in data:
        y = y.as_in_context(ctx)
        #计算预训练模型输出的特征
        out_features = net.features(X.as_in_context(ctx))
        outputs = net.output_new(out_features)    #final output
        l += loss(outputs,y).mean().asscalar()    #loss for the process
    return l/len(data)

2.1定义训练过程

完成了以上的准备工作，读入了数据、定义好了网络和损失，我们可以开始进行训练了，训练函数定义如下，输入为网络模型、数据、训练epochs、学习率、衰减等：

#def trainning  process, trainer, epochscircles, lossback,  valide
def train(net,train_iter,valid_iter,num_epochs, lr, wd, ctx, lr_period, lr_decay):
    trainer = gluon.Trainer(net.output_new.collect_params(), 'sgd', 
                           {'learning_rate':lr, 'momentum':0.9, 'wd': wd})
    plot_loss = []  #plot loss
    tic = datetime.datetime.now()
    print('Traing is begining, please waiting......')
    for epoch in range(num_epochs):
        train_l = 0.0    #存储训练loss
        counter = 0    #训练batch周期计数器
        #if epoch >0 and epoch %lr_period==0:    #every period step update lr
        trainer.set_learning_rate(trainer.learning_rate*lr_decay)    #every steps updata lr
        #print("There are %d data could train network"%len(train_iter))
        for X,y in train_iter:      #X~32(batch)*1024(iter)= 32768
            #output for process reminding
            counter +=1
            if counter % 256 ==0:
                print('processd %d images'%(counter*batch_size))    #一定批量就显示处理过程
            #output finished
            
            y = y.astype('float32').as_in_context(ctx)
            #feature
            out_features = net.features(X.as_in_context(ctx))    #预训练直接前传得到特征，未来这一步可以一次性做
            #partly training fineturning
            with autograd.record():
                #features to output, just use features as input
                outputs = net.output_new(out_features)    #这里只bp最后两层，只训练最后新定义的部分
                l = loss(outputs, y)
            l.backward()
            
            #for next batch
            trainer.step(batch_size)
            train_l += l.mean().asscalar()
        #log time into
        toc = datetime.datetime.now()
        h, remainder = divmod((toc - tic).seconds, 3600)
        m, s = divmod(remainder, 60)
        time_s = "time %02d:%02d:%02d" % (h, m, s)
        #validata
        if valid_iter is not None:   #验证数据，验证训练效果
            valid_loss = get_loss(valid_iter, net, ctx)
            epoch_s = ("epoch %d, train loss is %f, valid loss is %f :D "
                       %(epoch+1, train_l/len(train_iter),valid_loss))
        else:
            epoch_s = ("epoch %d, train loss is %f  :D"
                       %(epoch+1, train_l/len(train_iter)))
        tic = toc
        print(epoch_s + time_s + ', lr ' + str(trainer.learning_rate))
        #plot loss
        plot_loss.append(train_l/len(train_iter))
        plt.plot(plot_loss)    #将损失优化结果保存到图里
        plt.savefig("./training_loss.png")

2.2 开始训练

ctx = gb.try_gpu();num_epochs = 1000;lr = 0.01;wd = 1e-4;lr_period = 10;lr_decay = 0.99;
net = get_net(ctx)    #将网络和数据定义到gpu上
train(net,train_iter,valid_iter,num_epochs, lr, wd, ctx, lr_period, lr_decay)    #训练
net.output_new.collect_params().save('./output_new_2_1000.params')       #训练结束后保存参数
#net.output_new.save_params('./output_new_50.params')

Traing is begining, please waiting......
processd xxxxx images
processd xxxxx images
epoch 1, train loss is 1.234988, valid loss is 0.776764 :Dtime 00:04:10, lr 0.0099

3.测试

在训练完成得到模型后，我们需要对数据进行测试。同样需要读入数据，并利用网络进行分类。

#prepaer data
test_set = gdata.vision.ImageFolderDataset('./test_dis/',flag=1)
print("There are %d test imgs"%len(test_set))

There are xxxx test imgs

定义图像读入函数

def plot_image(img_path):
    with open(img_path, 'rb') as f:
        img = image.imdecode(f.read())    #读入输入
    #plt.imshow(img.asnumpy())
    return img

接下来就是测试过程了：

#predict process
preds = []
count_p=0
for img_path,label in test_set.items:    #将加载列表中每一张测试图进行分类
    img = plot_image(img_path)
    data, _ = transform_val(img, 0)
    data = data.expand_dims(axis=0)
    
    #plt.imshow(img.asnumpy())
    #plt.show()
    #print(img_path)
    #break
    
    # 计算预训练模型输出层的输入，即特征。
    output_features = net.features(data.as_in_context(mx.gpu()))
    # 将特征作为我们定义的输出网络的输入，计算输出。
    output = nd.softmax(net.output_new(output_features))

    preds.extend(output.asnumpy())
    count_p +=1
    #print(count_p)
    if count_p%100==0:
        print("processed %d imgs"%count_p)

processed 100 imgs

---

可以根据需要将生成的预测结果preds保存为json文件：

# use the tese_set name and predict results
with open('submission.json', 'w') as f:
    f.write("[")
    for i in range(len(preds)):
        if i==len(preds)-1:
            f.write("{"+""image_id": "+"""+test_set.items[i][0].split('/')[-1]+"""+','+""xxxx_class":"+str(preds[i].argmax())+'}')
        else:
            f.write("{"+""image_id": "+"""+test_set.items[i][0].split('/')[-1]+"""+','+""xxxx_class":"+str(preds[i].argmax())+'}'+',')
    f.write("]")

最后检查生成的数据长度，是否和测试集数据长度相同，然后就大功告成啦~~~~

#check format
import json
user_result_list = json.load(open('./submission.json', encoding='utf-8'))
len(user_result_list)

代码：
1.gluon
2.论坛
3.代码1，代码2
4. Logo from zcool.com