关于ResNeXt网络的pytorch实现
更新时间:2020年01月14日 09:04:00 作者:朴素.无恙
今天小编就为大家分享一篇关于ResNeXt网络的pytorch实现,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧
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脚本之家。
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