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这篇文章将为大家详细讲解有关pytorch如何实现inception_v3，小编觉得挺实用的，因此分享给大家做个参考，希望大家阅读完这篇文章后可以有所收获。
如下所示：
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
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数据集路径，路径下的数据集分为训练集和测试集，也就是train 以及val，train下分为两类数据1，2，val集同理
data_dir = "/home/dell/Desktop/data/切割图像"
# Models to choose from [resnet, alexnet, vgg, squeezenet, densenet, inception]
model_name = "inception"
# Number of classes in the dataset
num_classes = 2#两类数据1，2
# Batch size for training (change depending on how much memory you have)
batch_size = 32#batchsize尽量选取合适，否则训练时会内存溢出
# 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 = True
# 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 inception')
parser.add_argument('--outf', default='/home/dell/Desktop/dj/inception/', help='folder to output images and model checkpoints') #输出结果保存路径
parser.add_argument('--net', default='/home/dell/Desktop/dj/inception/inception.pth', help="path to net (to continue training)") #恢复训练时的模型路径
args = parser.parse_args()
训练函数
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25,is_inception=False):
since = time.time()
val_acc_history = []
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
print("Start Training, InceptionV3!")
with open("acc.txt", "w") as f1:
with open("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':
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'):
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)%50==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, epoch_acc))
f1.write('\n')
f1.flush()
if phase == 'val' and epoch_acc > best_acc:
f3 = open("best_acc.txt", "w")
f3.write("EPOCH=%d,best_acc= %.3f%%" % (epoch + 1,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
#是否更新参数
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 == "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
# 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)
#准备数据
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=0) for x in ['train', 'val']}
# Detect if we have a GPU available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
'''
是否加载之前训练过的模型
we='/home/dell/Desktop/dj/inception_050.pth'
model_ft.load_state_dict(torch.load(we))
'''
# 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=0.001, 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()
# Train and evaluate
model_ft, hist = train_model(model_ft, dataloaders_dict, criterion, optimizer_ft, num_epochs=num_epochs, is_inception=(model_name=="inception"))
'''
#随机初始化时的训练程序
# Initialize the non-pretrained version of the model used for this run
scratch_model,_ = initialize_model(model_name, num_classes, feature_extract=False, use_pretrained=False)
scratch_model = scratch_model.to(device)
scratch_optimizer = optim.SGD(scratch_model.parameters(), lr=0.001, momentum=0.9)
scratch_criterion = nn.CrossEntropyLoss()
_,scratch_hist = train_model(scratch_model, dataloaders_dict, scratch_criterion, scratch_optimizer, num_epochs=num_epochs, is_inception=(model_name=="inception"))
# Plot the training curves of validation accuracy vs. number
# of training epochs for the transfer learning method and
# the model trained from scratch
ohist = []
shist = []
ohist = [h.cpu().numpy() for h in hist]
shist = [h.cpu().numpy() for h in scratch_hist]
plt.title("Validation Accuracy vs. Number of Training Epochs")
plt.xlabel("Training Epochs")
plt.ylabel("Validation Accuracy")
plt.plot(range(1,num_epochs+1),ohist,label="Pretrained")
plt.plot(range(1,num_epochs+1),shist,label="Scratch")
plt.ylim((0,1.))
plt.xticks(np.arange(1, num_epochs+1, 1.0))
plt.legend()
plt.show()
'''1.PyTorch是相当简洁且高效快速的框架；2.设计追求最少的封装；3.设计符合人类思维，它让用户尽可能地专注于实现自己的想法；4.与google的Tensorflow类似，FAIR的支持足以确保PyTorch获得持续的开发更新；5.PyTorch作者亲自维护的论坛 供用户交流和求教问题6.入门简单
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