关于ResNeXt网络的pytorch实现


Posted in Python onJanuary 14, 2020

此处需要pip install pretrainedmodels

"""
Finetuning Torchvision Models

"""

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
import argparse
import pretrainedmodels.models.resnext as resnext

print("PyTorch Version: ",torch.__version__)
print("Torchvision Version: ",torchvision.__version__)


# Top level data directory. Here we assume the format of the directory conforms 
#  to the ImageFolder structure
#data_dir = "./data/hymenoptera_data"
data_dir = "/media/dell/dell/data/13/"
# Models to choose from [resnet, alexnet, vgg, squeezenet, densenet, inception]
model_name = "resnext"

# Number of classes in the dataset
num_classes = 171

# Batch size for training (change depending on how much memory you have)
batch_size = 16

# Number of epochs to train for 
num_epochs = 1000

# Flag for feature extracting. When False, we finetune the whole model, 
#  when True we only update the reshaped layer params
feature_extract = False

# 参数设置,使得我们能够手动输入命令行参数,就是让风格变得和Linux命令行差不多
parser = argparse.ArgumentParser(description='PyTorch seresnet')
parser.add_argument('--outf', default='/home/dell/Desktop/zhou/train7', help='folder to output images and model checkpoints') #输出结果保存路径
parser.add_argument('--net', default='/home/dell/Desktop/zhou/train7/resnext.pth', help="path to net (to continue training)") #恢复训练时的模型路径
args = parser.parse_args()


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

  val_acc_history = []
  
  best_model_wts = copy.deepcopy(model.state_dict())
  best_acc = 0.0
  print("Start Training, resnext!") # 定义遍历数据集的次数
  with open("/home/dell/Desktop/zhou/train7/acc.txt", "w") as f1:
    with open("/home/dell/Desktop/zhou/train7/log.txt", "w")as f2:
      for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch+1, num_epochs))
        print('*' * 10)
        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
          if phase == 'train':
            #scheduler.step()
            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))
          f2.write('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
          f2.write('\n')
          f2.flush()           
          # deep copy the model
          if phase == 'val':
            if (epoch+1)%5==0:
              #print('Saving model......')
              torch.save(model.state_dict(), '%s/inception_%03d.pth' % (args.outf, epoch + 1))
            f1.write("EPOCH=%03d,Accuracy= %.3f%%" % (epoch + 1, 100*epoch_acc))
            f1.write('\n')
            f1.flush()
          if phase == 'val' and epoch_acc > best_acc:
            f3 = open("/home/dell/Desktop/zhou/train7/best_acc.txt", "w")
            f3.write("EPOCH=%d,best_acc= %.3f%%" % (epoch + 1,100*epoch_acc))
            f3.close()
            best_acc = epoch_acc
            best_model_wts = copy.deepcopy(model.state_dict())
          if phase == 'val':
            val_acc_history.append(epoch_acc)

  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, val_acc_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":
    """ Resnet18
    """
    model_ft = models.resnet18(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 = 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 == "resnext":
    """ resnext
    Be careful, expects (3,224,224) sized images 
    """
    model_ft = resnext.resnext101_64x4d(num_classes=1000, pretrained='imagenet')
    set_parameter_requires_grad(model_ft, feature_extract)
    model_ft.last_linear = nn.Linear(2048, num_classes)   
    #pre='/home/dell/Desktop/zhou/train6/inception_009.pth'
    #model_ft.load_state_dict(torch.load(pre))
    input_size = 224

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

# 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:1" if torch.cuda.is_available() else "cpu")

#we='/home/dell/Desktop/dj/inception_050.pth'
#model_ft.load_state_dict(torch.load(we))#diaoyong
# 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=0.01, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
#exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=30, gamma=0.95)

# Setup the loss fxn
criterion = nn.CrossEntropyLoss()
print(model_ft)
# Train and evaluate
model_ft, hist = train_model(model_ft, dataloaders_dict, criterion, optimizer_ft, num_epochs=num_epochs, is_inception=False)

以上这篇关于ResNeXt网络的pytorch实现就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持三水点靠木。

Python 相关文章推荐
详解Python中expandtabs()方法的使用
May 18 Python
用Python写一个无界面的2048小游戏
May 24 Python
Python绑定方法与非绑定方法详解
Aug 18 Python
Python实现的视频播放器功能完整示例
Feb 01 Python
Django中url的反向查询的方法
Mar 14 Python
PyQt5每天必学之进度条效果
Apr 19 Python
python 对key为时间的dict排序方法
Oct 17 Python
python获取交互式ssh shell的方法
Feb 14 Python
浅谈python编译pyc工程--导包问题解决
Mar 20 Python
django restframework serializer 增加自定义字段操作
Jul 15 Python
PyTorch安装与基本使用详解
Aug 31 Python
深度学习tensorflow基础mnist
Apr 14 Python
Python属性和内建属性实例解析
Jan 14 #Python
Python程序控制语句用法实例分析
Jan 14 #Python
dpn网络的pytorch实现方式
Jan 14 #Python
Django之form组件自动校验数据实现
Jan 14 #Python
简单了解python filter、map、reduce的区别
Jan 14 #Python
Python vtk读取并显示dicom文件示例
Jan 13 #Python
Python解析多帧dicom数据详解
Jan 13 #Python
You might like
PHP获取网站域名和地址的代码
2008/08/17 PHP
PHP数据库操作之基于Mysqli的数据库操作类库
2014/04/19 PHP
深入理解PHP类的自动载入机制
2016/09/16 PHP
详细对比php中类继承和接口继承
2018/10/11 PHP
JavaScript入门学习书籍推荐
2008/06/12 Javascript
DOM下的节点属性和操作小结
2009/05/14 Javascript
JavaScript 事件冒泡简介及应用
2010/01/11 Javascript
jquery插件orbit.js实现图片折叠轮换特效
2015/04/14 Javascript
详细解读JavaScript的跨浏览器事件处理
2015/08/12 Javascript
require.js的用法详解
2015/10/20 Javascript
深入理解ECMAScript的几个关键语句
2016/06/01 Javascript
使用jquery的jsonp如何发起跨域请求及其原理详解
2017/08/17 jQuery
Vue header组件开发详解
2018/01/26 Javascript
浅谈在vue中用webpack打包之后运行文件的问题以及相关配置方法
2018/02/21 Javascript
elementUI多选框反选的实现代码
2019/04/03 Javascript
浅析Vue下的components模板使用及应用
2019/11/27 Javascript
JS控制下拉列表左右选择实例代码
2020/05/08 Javascript
Python与shell的3种交互方式介绍
2015/04/11 Python
python定时关机小脚本
2018/06/20 Python
python实现爬取图书封面
2018/07/05 Python
Python连接HDFS实现文件上传下载及Pandas转换文本文件到CSV操作
2020/06/06 Python
Python 连接 MySQL 的几种方法
2020/09/09 Python
有关pycharm登录github时有的时候会报错connection reset的问题
2020/09/15 Python
python实现磁盘日志清理的示例
2020/11/05 Python
NEW LOOK官网:英国时装零售巨头之一,快时尚品牌
2017/01/11 全球购物
Troy-Bilt官网:草坪割草机、吹雪机、分蘖机等
2019/02/19 全球购物
澳大利亚玩具剧场:Toy Playhouse
2019/03/03 全球购物
Shopping happy life西班牙:以最优惠的价格提供最好的时尚配饰
2020/03/13 全球购物
学期自我鉴定
2013/11/04 职场文书
教师优秀党员事迹材料
2014/08/14 职场文书
加强干部作风建设整改方案
2014/10/24 职场文书
欠款证明
2015/06/24 职场文书
团拜会主持词
2015/07/04 职场文书
导游词之秦皇岛燕塞湖
2020/01/03 职场文书
用python删除文件夹中的重复图片(图片去重)
2021/05/12 Python
教你nginx跳转配置的四种方式
2022/07/07 Servers