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

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()

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()

\'\'\'