Pytorch resnet 分类网络训练方法

文章目录

Pytorch resnet 分类网络训练方法

conda环境安装与配置

新建conda环境并激活

conda create --name pytorch170_py3_7 python=3.7
conda activate pytorch170_py3_7

配置

官网上面找与本机cuda对应的版本安装

选择合适的版本安装

# CUDA 10.1
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.1 -c pytorch

# CPU Only
conda install pytorch==1.7.0 torchvision==0.8.0 cpuonly -c pytorch

检测环境是否成功

python  
import torch  
print(torch.__version__)            //查看pytorch版本
print(torch.version.cuda)           //查看cuda版本
print(torch.cuda.is_available())    //验证cuda是否可用

准备数据集

在训练数据目录下新建 train 和 val 两个文件夹
在train文件夹下新建各个分类文件夹。如：class1,class2,class3,class4
在val文件夹下同样如此
将不同分类的图片放到个子文件夹里面

#最终文件结构为
-数据集根目录:
----------------train:
----------------------class1
----------------------class2
----------------------class3
----------------------class4
----------------val:
----------------------class1
----------------------class2
----------------------class3
----------------------class4

训练

修改训练脚本

from __future__ import print_function 
from __future__ import division
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

#训练数据目录
data_dir = "./DataSets"

# 选择训练网络 [resnet, alexnet, vgg, squeezenet, densenet, inception]
model_name = "resnet"

#权重文件输出目录及名称
output_path="./resnet152.pth"

#学习率
lr_rate=0.001

# 种类数目
num_classes = 4

# Batch Size取决你电脑内存大小
batch_size = 8

# 训练次数 
num_epochs = 500

# False时候更新所有参数，True时候只更新最后一层的参数
feature_extract = False


def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False):
    since = time.time()

    val_acc_history = []
    val_loss_history=[]
    
    train_acc_history = []
    train_loss_history=[]
    
    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)
                
                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    # Get model outputs and calculate loss
                    # Special case for inception because in training it has an auxiliary output. In train
                    #   mode we calculate the loss by summing the final output and the auxiliary output
                    #   but in testing we only consider the final output.
                    if is_inception and phase == 'train':
                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958
                        outputs, aux_outputs = model(inputs)
                        loss1 = criterion(outputs, labels)
                        loss2 = criterion(aux_outputs, labels)
                        loss = loss1 + 0.4*loss2
                    else:
                        outputs = model(inputs)
                        loss = criterion(outputs, labels)

                    _, preds = torch.max(outputs, 1)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / len(dataloaders[phase].dataset)
            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
                
            if phase == 'train':
                train_acc_history.append(epoch_acc)
                train_loss_history.append(epoch_loss) 
            else:
                val_acc_history.append(epoch_acc)
                val_loss_history.append(epoch_loss)          

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model, train_acc_history,train_loss_history,val_acc_history,val_loss_history



def set_parameter_requires_grad(model, feature_extracting):
    if feature_extracting:
        for param in model.parameters():
            param.requires_grad = False



def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
    # Initialize these variables which will be set in this if statement. Each of these
    #   variables is model specific.
    model_ft = None
    input_size = 0

    if model_name == "resnet":
        """ Resnet152
        """
        model_ft = models.resnet152(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs, num_classes)
        input_size = 480 #224

    elif model_name == "alexnet":
        """ Alexnet
        """
        model_ft = models.alexnet(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier[6].in_features
        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224

    elif model_name == "vgg":
        """ VGG11_bn
        """
        model_ft = models.vgg11_bn(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier[6].in_features
        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224

    elif model_name == "squeezenet":
        """ Squeezenet
        """
        model_ft = models.squeezenet1_0(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
        model_ft.num_classes = num_classes
        input_size = 224

    elif model_name == "densenet":
        """ Densenet
        """
        model_ft = models.densenet121(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier.in_features
        model_ft.classifier = nn.Linear(num_ftrs, num_classes) 
        input_size = 224

    elif model_name == "inception":
        """ Inception v3 
        Be careful, expects (299,299) sized images and has auxiliary output
        """
        model_ft = models.inception_v3(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        # Handle the auxilary net
        num_ftrs = model_ft.AuxLogits.fc.in_features
        model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
        # Handle the primary net
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs,num_classes)
        input_size = 299

    else:
        print("Invalid model name, exiting...")
        exit()
    
    return model_ft, input_size



if __name__ == '__main__':

    print("PyTorch Version: ",torch.__version__)
    print("Torchvision Version: ",torchvision.__version__)
    # Initialize the model for this run
    model_ft, input_size = initialize_model(model_name, num_classes, feature_extract, use_pretrained=True)
    
    # Print the model we just instantiated
    print(model_ft) 
    
    data_transforms = {
    
    
        'train': transforms.Compose([
            transforms.RandomResizedCrop(input_size),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]),
        'val': transforms.Compose([
            transforms.Resize(input_size),
            transforms.CenterCrop(input_size),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]),
    }
    
    print("Initializing Datasets and Dataloaders...")
    
    # Create training and validation datasets
    image_datasets = {
    
    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}

    # Create training and validation dataloaders
    dataloaders_dict = {
    
    x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True, num_workers=4) for x in ['train', 'val']}
    
    # Detect if we have a GPU available
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    
    
    # Send the model to GPU
    model_ft = model_ft.to(device)
    params_to_update = model_ft.parameters()
    print("Params to learn:")
    if feature_extract:
        params_to_update = []
        for name,param in model_ft.named_parameters():
            if param.requires_grad == True:
                params_to_update.append(param)
                print("\t",name)
    else:
        for name,param in model_ft.named_parameters():
            if param.requires_grad == True:
                print("\t",name)
    
    # Observe that all parameters are being optimized
    optimizer_ft = optim.SGD(params_to_update, lr=lr_rate, momentum=0.9)
    
    # Setup the loss fxn
    criterion = nn.CrossEntropyLoss()
    
    # Train and evaluate
    model_ft, trainacc,trainloss,valacc,valloss = train_model(model_ft, dataloaders_dict, criterion, optimizer_ft, num_epochs=num_epochs, is_inception=(model_name=="inception"))
    torch.save(model_ft.state_dict(), output_path)

    plt.title("Acc&Loss")
    plt.xlabel("Training Epochs")
    plt.ylabel("Value")
    plt.plot(range(1,num_epochs+1),trainacc,label="TrainAcc")
    plt.plot(range(1,num_epochs+1),trainloss,label="TrainLoss")
    plt.plot(range(1,num_epochs+1),valacc,label="ValAcc")
    plt.plot(range(1,num_epochs+1),valloss,label="ValLoss")
    
    plt.ylim((0,1.))
    plt.xticks(np.arange(1, num_epochs+1, 1.0))
    plt.legend()
    plt.show()

执行脚本训练

python finetuning_torchvision_models_tutorial.py

测试效果

# -*- coding: utf-8 -*-
#!/usr/bin/python
# -*- coding: UTF-8 -*-

import torchvision as tv
import torchvision.transforms as transforms
import torch
from PIL import Image
import torch.nn as nn

input_size = 480
names = ['class1', 'class2','class3','class4']
def pridict():

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model=tv.models.resnet152()
    num_ftrs = model.fc.in_features
    model.fc = nn.Linear(num_ftrs, 4)  # 分类数量
    model.load_state_dict(torch.load("resnet152.pth"))
    model = model.to(device)
    model.eval()  # 预测模式

    # 获取测试图片，并行相应的处理
    img = Image.open('4.jpg')
    transform = transforms.Compose([
            transforms.Resize(input_size),
            transforms.CenterCrop(input_size),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])
    

    img = transform(img)
    img = img.unsqueeze(0)
    img = img.to(device)


    with torch.no_grad():
        py = model(img)
    _, predicted = torch.max(py, 1)  # 获取分类结果
    classIndex_ = predicted[0]

    print('预测结果', names[classIndex_])


if __name__ == '__main__':
    pridict()

转换模型

将模型转换为c++可用的模型

import torch
import torchvision
import torch.nn as nn
#model = torchvision.models.resnet50(pretrained=True)
model=torchvision.models.resnet152()
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 4)  # make the change
model.load_state_dict(torch.load("resnet152.pth"))

model.eval()
example = torch.rand(1, 3, 480, 480)
traced_script_module = torch.jit.trace(model, example)
traced_script_module.save("resnet152.pt")

Pytorch 分类网络训练方法（Resnet152为例）

Pytorch resnet 分类网络训练方法

文章目录

conda环境安装与配置

新建conda环境并激活

配置

准备数据集

训练

修改训练脚本

执行脚本训练

测试效果

转换模型

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