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1-2,图片数据建模流程范例#

import os
import datetime

#打印时间
def printbar():
    nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    print("\n"+"=========="*8 + "%s"%nowtime)

#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" 

一,准备数据#

cifar2数据集为cifar10数据集的子集,只包括前两种类别airplane和automobile。

训练集有airplane和automobile图片各5000张,测试集有airplane和automobile图片各1000张。

cifar2任务的目标是训练一个模型来对飞机airplane和机动车automobile两种图片进行分类。

我们准备的Cifar2数据集的文件结构如下所示。


在Pytorch中构建图片数据管道通常有三种方法。

第一种是使用 torchvision中的datasets.ImageFolder来读取图片然后用 DataLoader来并行加载。

第二种是通过继承 torch.utils.data.Dataset 实现用户自定义读取逻辑然后用 DataLoader来并行加载。

第三种方法是读取用户自定义数据集的通用方法,既可以读取图片数据集,也可以读取文本数据集。

本篇我们介绍第一种方法。

import torch 
from torch import nn
from torch.utils.data import Dataset,DataLoader
from torchvision import transforms,datasets 
transform_train = transforms.Compose(
    [transforms.ToTensor()])
transform_valid = transforms.Compose(
    [transforms.ToTensor()])
ds_train = datasets.ImageFolder("../data/cifar2/train/",
            transform = transform_train,target_transform= lambda t:torch.tensor([t]).float())
ds_valid = datasets.ImageFolder("../data/cifar2/test/",
            transform = transform_valid,target_transform= lambda t:torch.tensor([t]).float())

print(ds_train.class_to_idx)
{'0_airplane': 0, '1_automobile': 1}
dl_train = DataLoader(ds_train,batch_size = 50,shuffle = True,num_workers=3)
dl_valid = DataLoader(ds_valid,batch_size = 50,shuffle = True,num_workers=3)
%matplotlib inline
%config InlineBackend.figure_format = 'svg'

#查看部分样本
from matplotlib import pyplot as plt 

plt.figure(figsize=(8,8)) 
for i in range(9):
    img,label = ds_train[i]
    img = img.permute(1,2,0)
    ax=plt.subplot(3,3,i+1)
    ax.imshow(img.numpy())
    ax.set_title("label = %d"%label.item())
    ax.set_xticks([])
    ax.set_yticks([]) 
plt.show()

# Pytorch的图片默认顺序是 Batch,Channel,Width,Height
for x,y in dl_train:
    print(x.shape,y.shape) 
    break
torch.Size([50, 3, 32, 32]) torch.Size([50, 1])

二,定义模型#

使用Pytorch通常有三种方式构建模型:使用nn.Sequential按层顺序构建模型,继承nn.Module基类构建自定义模型,继承nn.Module基类构建模型并辅助应用模型容器(nn.Sequential,nn.ModuleList,nn.ModuleDict)进行封装。

此处选择通过继承nn.Module基类构建自定义模型。

#测试AdaptiveMaxPool2d的效果
pool = nn.AdaptiveMaxPool2d((1,1))
t = torch.randn(10,8,32,32)
pool(t).shape 
torch.Size([10, 8, 1, 1])
class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3)
        self.pool = nn.MaxPool2d(kernel_size = 2,stride = 2)
        self.conv2 = nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5)
        self.dropout = nn.Dropout2d(p = 0.1)
        self.adaptive_pool = nn.AdaptiveMaxPool2d((1,1))
        self.flatten = nn.Flatten()
        self.linear1 = nn.Linear(64,32)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(32,1)
        self.sigmoid = nn.Sigmoid()

    def forward(self,x):
        x = self.conv1(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = self.pool(x)
        x = self.dropout(x)
        x = self.adaptive_pool(x)
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.relu(x)
        x = self.linear2(x)
        y = self.sigmoid(x)
        return y

net = Net()
print(net)
Net(
  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
  (dropout): Dropout2d(p=0.1, inplace=False)
  (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))
  (flatten): Flatten()
  (linear1): Linear(in_features=64, out_features=32, bias=True)
  (relu): ReLU()
  (linear2): Linear(in_features=32, out_features=1, bias=True)
  (sigmoid): Sigmoid()
)
import torchkeras
torchkeras.summary(net,input_shape= (3,32,32))
----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1           [-1, 32, 30, 30]             896
         MaxPool2d-2           [-1, 32, 15, 15]               0
            Conv2d-3           [-1, 64, 11, 11]          51,264
         MaxPool2d-4             [-1, 64, 5, 5]               0
         Dropout2d-5             [-1, 64, 5, 5]               0
 AdaptiveMaxPool2d-6             [-1, 64, 1, 1]               0
           Flatten-7                   [-1, 64]               0
            Linear-8                   [-1, 32]           2,080
              ReLU-9                   [-1, 32]               0
           Linear-10                    [-1, 1]              33
          Sigmoid-11                    [-1, 1]               0
================================================================
Total params: 54,273
Trainable params: 54,273
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.011719
Forward/backward pass size (MB): 0.359634
Params size (MB): 0.207035
Estimated Total Size (MB): 0.578388
----------------------------------------------------------------

三,训练模型#

Pytorch通常需要用户编写自定义训练循环,训练循环的代码风格因人而异。

有3类典型的训练循环代码风格:脚本形式训练循环,函数形式训练循环,类形式训练循环。

此处介绍一种较通用的函数形式训练循环。

import pandas as pd 
from sklearn.metrics import roc_auc_score

model = net
model.optimizer = torch.optim.SGD(model.parameters(),lr = 0.01)
model.loss_func = torch.nn.BCELoss()
model.metric_func = lambda y_pred,y_true: roc_auc_score(y_true.data.numpy(),y_pred.data.numpy())
model.metric_name = "auc"
def train_step(model,features,labels):

    # 训练模式,dropout层发生作用
    model.train()

    # 梯度清零
    model.optimizer.zero_grad()

    # 正向传播求损失
    predictions = model(features)
    loss = model.loss_func(predictions,labels)
    metric = model.metric_func(predictions,labels)

    # 反向传播求梯度
    loss.backward()
    model.optimizer.step()

    return loss.item(),metric.item()

def valid_step(model,features,labels):

    # 预测模式,dropout层不发生作用
    model.eval()
    # 关闭梯度计算
    with torch.no_grad():
        predictions = model(features)
        loss = model.loss_func(predictions,labels)
        metric = model.metric_func(predictions,labels)

    return loss.item(), metric.item()


# 测试train_step效果
features,labels = next(iter(dl_train))
train_step(model,features,labels)
(0.6922046542167664, 0.5088566827697262)
def train_model(model,epochs,dl_train,dl_valid,log_step_freq):

    metric_name = model.metric_name
    dfhistory = pd.DataFrame(columns = ["epoch","loss",metric_name,"val_loss","val_"+metric_name]) 
    print("Start Training...")
    nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    print("=========="*8 + "%s"%nowtime)

    for epoch in range(1,epochs+1):  

        # 1,训练循环-------------------------------------------------
        loss_sum = 0.0
        metric_sum = 0.0
        step = 1

        for step, (features,labels) in enumerate(dl_train, 1):

            loss,metric = train_step(model,features,labels)

            # 打印batch级别日志
            loss_sum += loss
            metric_sum += metric
            if step%log_step_freq == 0:   
                print(("[step = %d] loss: %.3f, "+metric_name+": %.3f") %
                      (step, loss_sum/step, metric_sum/step))

        # 2,验证循环-------------------------------------------------
        val_loss_sum = 0.0
        val_metric_sum = 0.0
        val_step = 1

        for val_step, (features,labels) in enumerate(dl_valid, 1):

            val_loss,val_metric = valid_step(model,features,labels)

            val_loss_sum += val_loss
            val_metric_sum += val_metric

        # 3,记录日志-------------------------------------------------
        info = (epoch, loss_sum/step, metric_sum/step, 
                val_loss_sum/val_step, val_metric_sum/val_step)
        dfhistory.loc[epoch-1] = info

        # 打印epoch级别日志
        print(("\nEPOCH = %d, loss = %.3f,"+ metric_name + \
              "  = %.3f, val_loss = %.3f, "+"val_"+ metric_name+" = %.3f") 
              %info)
        nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
        print("\n"+"=========="*8 + "%s"%nowtime)

    print('Finished Training...')

    return dfhistory
epochs = 20

dfhistory = train_model(model,epochs,dl_train,dl_valid,log_step_freq = 50)
Start Training...
================================================================================2020-06-28 20:47:56
[step = 50] loss: 0.691, auc: 0.627
[step = 100] loss: 0.690, auc: 0.673
[step = 150] loss: 0.688, auc: 0.699
[step = 200] loss: 0.686, auc: 0.716

EPOCH = 1, loss = 0.686,auc  = 0.716, val_loss = 0.678, val_auc = 0.806

================================================================================2020-06-28 20:48:18
[step = 50] loss: 0.677, auc: 0.780
[step = 100] loss: 0.675, auc: 0.775
[step = 150] loss: 0.672, auc: 0.782
[step = 200] loss: 0.669, auc: 0.779

EPOCH = 2, loss = 0.669,auc  = 0.779, val_loss = 0.651, val_auc = 0.815

......

================================================================================2020-06-28 20:54:24
[step = 50] loss: 0.386, auc: 0.914
[step = 100] loss: 0.392, auc: 0.913
[step = 150] loss: 0.395, auc: 0.911
[step = 200] loss: 0.398, auc: 0.911

EPOCH = 19, loss = 0.398,auc  = 0.911, val_loss = 0.449, val_auc = 0.924

================================================================================2020-06-28 20:54:43
[step = 50] loss: 0.416, auc: 0.917
[step = 100] loss: 0.417, auc: 0.916
[step = 150] loss: 0.404, auc: 0.918
[step = 200] loss: 0.402, auc: 0.918

EPOCH = 20, loss = 0.402,auc  = 0.918, val_loss = 0.535, val_auc = 0.925

================================================================================2020-06-28 20:55:03
Finished Training...

四,评估模型#

dfhistory 

%matplotlib inline
%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt

def plot_metric(dfhistory, metric):
    train_metrics = dfhistory[metric]
    val_metrics = dfhistory['val_'+metric]
    epochs = range(1, len(train_metrics) + 1)
    plt.plot(epochs, train_metrics, 'bo--')
    plt.plot(epochs, val_metrics, 'ro-')
    plt.title('Training and validation '+ metric)
    plt.xlabel("Epochs")
    plt.ylabel(metric)
    plt.legend(["train_"+metric, 'val_'+metric])
    plt.show()
plot_metric(dfhistory,"loss")

plot_metric(dfhistory,"auc")


五,使用模型#

def predict(model,dl):
    model.eval()
    with torch.no_grad():
        result = torch.cat([model.forward(t[0]) for t in dl])
    return(result.data)
#预测概率
y_pred_probs = predict(model,dl_valid)
y_pred_probs
tensor([[8.4032e-01],
        [1.0407e-02],
        [5.4146e-04],
        ...,
        [1.4471e-02],
        [1.7673e-02],
        [4.5081e-01]])
#预测类别
y_pred = torch.where(y_pred_probs>0.5,
        torch.ones_like(y_pred_probs),torch.zeros_like(y_pred_probs))
y_pred
tensor([[1.],
        [0.],
        [0.],
        ...,
        [0.],
        [0.],
        [0.]])

六,保存模型#

推荐使用保存参数方式保存Pytorch模型。

print(model.state_dict().keys())
odict_keys(['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias', 'linear1.weight', 'linear1.bias', 'linear2.weight', 'linear2.bias'])
# 保存模型参数

torch.save(model.state_dict(), "../data/model_parameter.pkl")

net_clone = Net()
net_clone.load_state_dict(torch.load("../data/model_parameter.pkl"))

predict(net_clone,dl_valid)
tensor([[0.0204],
        [0.7692],
        [0.4967],
        ...,
        [0.6078],
        [0.7182],
        [0.8251]])

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