1. 数据增强、transform 模块

数据预处理部分：

• 数据增强：torchvision中transforms模块自带功能，比较实用
• 数据预处理：torchvision中transforms也帮我们实现好了，直接调用即可
• DataLoader模块直接读取batch数据

网络模块设置：

• 加载预训练模型，torchvision中有很多经典网络架构，调用起来十分方便，并且可以用人家训练好的权重参数来继续训练，也就是所谓的迁移学习
• 需要注意的是别人训练好的任务跟咱们的可不是完全一样，需要把最后的head层改一改，一般也就是最后的全连接层，改成咱们自己的任务
• 训练时可以全部重头训练，也可以只训练最后咱们任务的层，因为前几层都是做特征提取的，本质任务目标是一致的

网络模型保存与测试

• 模型保存的时候可以带有选择性，例如在验证集中如果当前效果好则保存
• 读取模型进行实际测试

torchvision import imagefolder dataloader

数据增强：-45——+45度之间随机旋转
随机裁剪：原始大小10241024->resize 512512 例如vgg网络，中心随机裁剪成224*224
数据增i强：亮度饱和度随机变化
数据增强：灰度随机变换
数据增强：标准化，均值与标准差

import os
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import torch
from torch import nn
import torch.optim as optim
import torchvision
#pip install torchvision
from torchvision import transforms, models, datasets
#https://pytorch.org/docs/stable/torchvision/index.html
import imageio
import time
import warnings
import random
import sys
import copy
import json
from PIL import Image//数据读取操作
data_dir = './flower_data/'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
### 制作好数据源：
- data_transforms中指定了所有图像预处理操作
- ImageFolder假设所有的文件按文件夹保存好，每个文件夹下面存贮同一类别的图片，文件夹的名字为分类的名字data_transforms = {'train': transforms.Compose([transforms.RandomRotation(45),#随机旋转，-45到45度之间随机选transforms.CenterCrop(224),#从中心开始裁剪transforms.RandomHorizontalFlip(p=0.5),#随机水平翻转 选择一个概率概率transforms.RandomVerticalFlip(p=0.5),#随机垂直翻转transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1),#参数1为亮度，参数2为对比度，参数3为饱和度，参数4为色相transforms.RandomGrayscale(p=0.025),#概率转换成灰度率，3通道就是R=G=Btransforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])#均值，标准差]),'valid': transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),
}batch_size = 8image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'valid']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True) for x in ['train', 'valid']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'valid']}
class_names = image_datasets['train'].classes### 读取标签对应的实际名字
with open('cat_to_name.json', 'r') as f:cat_to_name = json.load(f)### 展示下数据
- 注意tensor的数据需要转换成numpy的格式，而且还需要还原回标准化的结果def im_convert(tensor):""" 展示数据"""image = tensor.to("cpu").clone().detach()image = image.numpy().squeeze()image = image.transpose(1,2,0)image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))image = image.clip(0, 1)return imagefig=plt.figure(figsize=(20, 12))
columns = 4
rows = 2dataiter = iter(dataloaders['valid'])
inputs, classes = dataiter.next()for idx in range (columns*rows):ax = fig.add_subplot(rows, columns, idx+1, xticks=[], yticks=[])ax.set_title(cat_to_name[str(int(class_names[classes[idx]]))])plt.imshow(im_convert(inputs[idx]))
plt.show()### 加载models中提供的模型，并且直接用训练的好权重当做初始化参数
- 第一次执行需要下载，可能会比较慢，我会提供给大家一份下载好的，可以直接放到相应路径
model_name = 'resnet'  #可选的比较多 ['resnet', 'alexnet', 'vgg', 'squeezenet', 'densenet', 'inception']
#是否用人家训练好的特征来做
feature_extract = True # 是否用GPU训练
train_on_gpu = torch.cuda.is_available()if not train_on_gpu:print('CUDA is not available.  Training on CPU ...')
else:print('CUDA is available!  Training on GPU ...')device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")def set_parameter_requires_grad(model, feature_extracting):if feature_extracting:for param in model.parameters():param.requires_grad = Falsemodel_ft = models.resnet152()
model_ft

2. 迁移学习

卷积网络本质就是特征提取，因此类似模型的训练结果也即权重，可以复用或者作为自己训练的权重的初始化的值。

3. 参考pytorch官网例子

### 实现
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):# 选择合适的模型，不同模型的初始化方法稍微有点区别model_ft = Noneinput_size = 0if model_name == "resnet":""" Resnet152"""model_ft = models.resnet152(pretrained=use_pretrained)set_parameter_requires_grad(model_ft, feature_extract)num_ftrs = model_ft.fc.in_featuresmodel_ft.fc = nn.Sequential(nn.Linear(num_ftrs, 102),nn.LogSoftmax(dim=1))input_size = 224elif 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_featuresmodel_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)input_size = 224elif model_name == "vgg":""" VGG11_bn"""model_ft = models.vgg16(pretrained=use_pretrained)set_parameter_requires_grad(model_ft, feature_extract)num_ftrs = model_ft.classifier[6].in_featuresmodel_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)input_size = 224elif 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_classesinput_size = 224elif 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_featuresmodel_ft.classifier = nn.Linear(num_ftrs, num_classes)input_size = 224elif model_name == "inception":""" Inception v3Be 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 netnum_ftrs = model_ft.AuxLogits.fc.in_featuresmodel_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)# Handle the primary netnum_ftrs = model_ft.fc.in_featuresmodel_ft.fc = nn.Linear(num_ftrs,num_classes)input_size = 299else:print("Invalid model name, exiting...")exit()return model_ft, input_size

### 设置哪些层需要训练
model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)#GPU计算
model_ft = model_ft.to(device)# 模型保存
filename='checkpoint.pth'# 是否训练所有层
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)### 优化器设置
# 优化器设置
optimizer_ft = optim.Adam(params_to_update, lr=1e-2)
scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)#学习率每7个epoch衰减成原来的1/10
#最后一层已经LogSoftmax()了，所以不能nn.CrossEntropyLoss()来计算了，nn.CrossEntropyLoss()相当于logSoftmax()和nn.NLLLoss()整合
criterion = nn.NLLLoss()

### 训练模块
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False,filename=filename):since = time.time()best_acc = 0"""checkpoint = torch.load(filename)best_acc = checkpoint['best_acc']model.load_state_dict(checkpoint['state_dict'])optimizer.load_state_dict(checkpoint['optimizer'])model.class_to_idx = checkpoint['mapping']"""model.to(device)val_acc_history = []train_acc_history = []train_losses = []valid_losses = []LRs = [optimizer.param_groups[0]['lr']]best_model_wts = copy.deepcopy(model.state_dict())for epoch in range(num_epochs):print('Epoch {}/{}'.format(epoch, num_epochs - 1))print('-' * 10)# 训练和验证for phase in ['train', 'valid']:if phase == 'train':model.train()  # 训练else:model.eval()   # 验证running_loss = 0.0running_corrects = 0# 把数据都取个遍for inputs, labels in dataloaders[phase]:inputs = inputs.to(device)labels = labels.to(device)# 清零optimizer.zero_grad()# 只有训练的时候计算和更新梯度with torch.set_grad_enabled(phase == 'train'):if is_inception and phase == 'train':outputs, aux_outputs = model(inputs)loss1 = criterion(outputs, labels)loss2 = criterion(aux_outputs, labels)loss = loss1 + 0.4*loss2else:#resnet执行的是这里outputs = model(inputs)loss = criterion(outputs, labels)_, preds = torch.max(outputs, 1)# 训练阶段更新权重if phase == 'train':loss.backward()optimizer.step()# 计算损失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)time_elapsed = time.time() - sinceprint('Time elapsed {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))# 得到最好那次的模型if phase == 'valid' and epoch_acc > best_acc:best_acc = epoch_accbest_model_wts = copy.deepcopy(model.state_dict())state = {'state_dict': model.state_dict(),'best_acc': best_acc,'optimizer' : optimizer.state_dict(),}torch.save(state, filename)if phase == 'valid':val_acc_history.append(epoch_acc)valid_losses.append(epoch_loss)scheduler.step(epoch_loss)if phase == 'train':train_acc_history.append(epoch_acc)train_losses.append(epoch_loss)print('Optimizer learning rate : {:.7f}'.format(optimizer.param_groups[0]['lr']))LRs.append(optimizer.param_groups[0]['lr'])print()time_elapsed = time.time() - sinceprint('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))print('Best val Acc: {:4f}'.format(best_acc))# 训练完后用最好的一次当做模型最终的结果model.load_state_dict(best_model_wts)return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs ### 开始训练！
model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs  = train_model(model_ft, dataloaders, criterion, optimizer_ft, num_epochs=20, is_inception=(model_name=="inception"))

### 再继续训练所有层
for param in model_ft.parameters():param.requires_grad = True# 再继续训练所有的参数，学习率调小一点
optimizer = optim.Adam(params_to_update, lr=1e-4)
scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)# 损失函数
criterion = nn.NLLLoss()# Load the checkpointcheckpoint = torch.load(filename)
best_acc = checkpoint['best_acc']
model_ft.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])#model_ft.class_to_idx = checkpoint['mapping']
model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs  = train_model(model_ft, dataloaders, criterion, optimizer, num_epochs=10, is_inception=(model_name=="inception"))

### 测试网络效果输入一张测试图像，看看网络的返回结果：probs, classes = predict(image_path, model)
print(probs)
print(classes)
> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]
> ['70', '3', '45', '62', '55']注意预处理方法需相同### 加载训练好的模型
model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)# GPU模式
model_ft = model_ft.to(device)# 保存文件的名字
filename='seriouscheckpoint.pth'# 加载模型
checkpoint = torch.load(filename)
best_acc = checkpoint['best_acc']
model_ft.load_state_dict(checkpoint['state_dict'])### 测试数据预处理- 测试数据处理方法需要跟训练时一直才可以 
- crop操作的目的是保证输入的大小是一致的
- 标准化操作也是必须的，用跟训练数据相同的mean和std,但是需要注意一点训练数据是在0-1上进行标准化，所以测试数据也需要先归一化
- 最后一点，PyTorch中颜色通道是第一个维度，跟很多工具包都不一样，需要转换def process_image(image_path):# 读取测试数据img = Image.open(image_path)# Resize,thumbnail方法只能进行缩小，所以进行了判断if img.size[0] > img.size[1]:img.thumbnail((10000, 256))else:img.thumbnail((256, 10000))# Crop操作left_margin = (img.width-224)/2bottom_margin = (img.height-224)/2right_margin = left_margin + 224top_margin = bottom_margin + 224img = img.crop((left_margin, bottom_margin, right_margin,   top_margin))# 相同的预处理方法img = np.array(img)/255mean = np.array([0.485, 0.456, 0.406]) #provided meanstd = np.array([0.229, 0.224, 0.225]) #provided stdimg = (img - mean)/std# 注意颜色通道应该放在第一个位置img = img.transpose((2, 0, 1))return imgdef imshow(image, ax=None, title=None):"""展示数据"""if ax is None:fig, ax = plt.subplots()# 颜色通道还原image = np.array(image).transpose((1, 2, 0))# 预处理还原mean = np.array([0.485, 0.456, 0.406])std = np.array([0.229, 0.224, 0.225])image = std * image + meanimage = np.clip(image, 0, 1)ax.imshow(image)ax.set_title(title)return aximage_path = 'image_06621.jpg'
img = process_image(image_path)
imshow(img)img.shape# 得到一个batch的测试数据
dataiter = iter(dataloaders['valid'])
images, labels = dataiter.next()model_ft.eval()if train_on_gpu:output = model_ft(images.cuda())
else:output = model_ft(images)output表示对一个batch中每一个数据得到其属于各个类别的可能性
output.shape### 得到概率最大的那个
_, preds_tensor = torch.max(output, 1)preds = np.squeeze(preds_tensor.numpy()) if not train_on_gpu else np.squeeze(preds_tensor.cpu().numpy())
preds### 展示预测结果
fig=plt.figure(figsize=(20, 20))
columns =4
rows = 2for idx in range (columns*rows):ax = fig.add_subplot(rows, columns, idx+1, xticks=[], yticks=[])plt.imshow(im_convert(images[idx]))ax.set_title("{} ({})".format(cat_to_name[str(preds[idx])], cat_to_name[str(labels[idx].item())]),color=("green" if cat_to_name[str(preds[idx])]==cat_to_name[str(labels[idx].item())] else "red"))
plt.show()