关于ResNeXt网络的pytorch实现

 """ 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 whic
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