迁移学习_pytorch实战
想学习一下迁移学习，则将使用预先训练的网络，来构建用于疟疾检测的图像分类器，这个分类器只需要将得到的数据，分为“感染”“未感染”两类。
我们将要用到的图像数据集可以在这里下载👇
https://drive.google.com/open?id=16DbIOMCtCuRuMdYF64MPv3iLqpSG6tfv
经过预先训练的网络在ImageNet上进行了训练，其中包含120万张1000个类别的图像，
用到的模型是torchvision.models，它有6种不同的架构我们可以使用。
torchvision.models具有模型性能的细分以及可以使用的层数（由模型附带的数字表示）。
加载所有必需的包和库：
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
from matplotlib import pyplot as plt
import torch
from torch import nn
import torch.nn.functional as F
from torch import optim
from torch.autograd import Variable
from torchvision import datasets, transforms, models
from PIL import Image
import numpy as np
import os
from torch.utils.data.sampler import SubsetRandomSampler
import pandas as pd
image_dir = 'D:/Notebook/cell_images'
def imshow(image):
plt.figure(figsize=(6, 6))
plt.imshow(image)
plt.axis('off')
plt.show()
#可视化一张感染的图
x = Image.open(image_dir + '/Parasitized/C39P4thinF_original_IMG_20150622_111206_cell_84.png')
np.array(x).shape
imshow(x)
定义转换并加载进数据
转换是将一个图形、表达式或函数转换为另一个图形、表达式或函数的过程。
我们需要为训练、测试以及验证数据定义一些转换。值得注意的，可能有的类别图像太少，不够进行转换，为了增加网络识别的图像数量，我们执行所谓的数据增强。
在训练期间，我们随机裁剪、缩放和旋转图像，以便在每个时期，网络会看到同一图像的不同变化，提高实验的准确性。
# Define transforms for the training, validation, and testing sets
train_transforms = transforms.Compose([transforms.RandomResizedCrop(size=256, scale=(0.8, 1.0)),
transforms.RandomRotation(degrees=15),
transforms.ColorJitter(),
transforms.RandomHorizontalFlip(),
transforms.CenterCrop(size=224), # Image net standards
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
])
test_transforms = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
validation_transforms = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
接下来加载数据集。最简单的方法是用torchvision的dataset.ImageFolder。
加载imageFolder后，我们将数据拆分为20％验证集和10％测试集; 然后将它传递给DataLoader。
它接收一个类似从ImageFolder获得的数据集，并返回批量图像及其相应的标签（可以将改组设置为true以在时期内引入变化）。
# Loading in the dataset
train_data = datasets.ImageFolder(image_dir, transform=train_transforms)
# number of subprocesses to use for data loading
num_workers = 0
# percentage of training set to use as validation
valid_size = 0.2
test_size = 0.1
# obtain training indices that will be used for validation
num_train = len(train_data)
indices = list(range(num_train))
np.random.shuffle(indices)
valid_split = int(np.floor((valid_size) * num_train))
test_split = int(np.floor((valid_size + test_size) * num_train))
valid_idx, test_idx, train_idx = indices[:valid_split], indices[valid_split:test_split], indices[test_split:]
print(len(valid_idx), len(test_idx), len(train_idx))
# define samplers for obtaining training and validation batches
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(valid_idx)
test_sampler = SubsetRandomSampler(test_idx)
# prepare data loaders (combine dataset and sampler)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, sampler=train_sampler, num_workers=num_workers)
valid_loader = torch.utils.data.DataLoader(train_data, batch_size=32, sampler=valid_sampler, num_workers=num_workers)
test_loader = torch.utils.data.DataLoader(train_data, batch_size=32, sampler=test_sampler, num_workers=num_workers)
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模型训练流程
- 加载预先训练的模型
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# pretrained=True will download a pretrained network for us
model = models.densenet121(pretrained=True)
#model
PyTorch以及几乎所有其他深度学习框架，都使用CUDA来有效地计算GPU上的前向和后向传递。
在PyTorch中，我们使用model.cuda（）将模型参数和其他张量移动到GPU内存，或者从GPU移回，
import tensorwatch as tw
import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin'
tw.draw_model(model, [1, 3, 224, 224])
Pytorch网络结构可视化
所需库文件：
- graphviz
- tensorwatch
draw_model函数需要传入三个参数，第一个为model，第二个参数为input_shape，第三个参数为orientation，可以选择'LR'或者'TB'，分别代表左右布局与上下布局。
统计网络参数
可以通过model_stats方法统计各层的参数情况。
tw.draw_model(model, [1, 3, 224, 224], orientation='LR')
tw.model_stats(model, [1, 3, 224, 224])
- 冻结卷积层并使用自定义分类器替换全连接层
#Freezing model parameters and defining the fully connected network to be attached to the model, loss function and the optimizer.
#We there after put the model on the GPUs
for param in model.parameters():
param.require_grad = False
fc = nn.Sequential(
nn.Linear(1024, 460),
nn.ReLU(),
nn.Dropout(0.4),
nn.Linear(460,2),
nn.LogSoftmax(dim=1)
)
model.classifier = fc
criterion = nn.NLLLoss()
#Over here we want to only update the parameters of the classifier so
optimizer = torch.optim.Adam(model.classifier.parameters(), lr=0.003)
model.cuda()
冻结模型参数允许我们为早期卷积层保留预训练模型的权重，其目的是用于特征提取。
然后我们定义我们的全连接网络，示例代码中是1024。
我们还定义了要使用的激活函数，和有助于通过随机关闭层中的神经元，以强制在剩余节点之间共享信息，来避免过度拟合。
在我们定义了自定义全连接网络之后，我们将其连接到预先训练好的模型的完全连接网络。
接下来我们定义损失函数，优化器，并通过将模型移动到GPU来准备训练模型。
- 为特定任务训练自定义分类器
在训练期间，我们遍历每个时期的DataLoader。 对于每个batch，使用标准函数计算损失。
使用loss.backward（）方法计算相对于模型参数的损失梯度。
optimizer.zero_grad（）负责清除任何累积的梯度，因为我们会一遍又一遍地计算梯度。
optimizer.step（）使用具有动量的随机梯度下降（Adam）更新模型参数。
为了防止过度拟合，我们使用一种称为早期停止的强大技术。背后的想法很简单，当验证数据集上的性能开始降低时停止训练。
epochs = 10
valid_loss_min = 0.0
torch.device('cuda')
torch.backends.cudnn.benchmark = True
import time
for epoch in range(epochs):
start = time.time()
# scheduler.step()
model.to(device)
model.train()
train_loss = 0.0
valid_loss = 0.0
for index, (inputs, labels) in enumerate(train_loader):
# Move input and label tensors to the default device
inputs, labels = inputs.cuda(), labels.cuda()
logps = model(inputs)
loss = criterion(logps, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
model.eval()
with torch.no_grad():
accuracy = 0
for inputs, labels in valid_loader:
inputs, labels = inputs.to(device), labels.to(device)
logps = model.forward(inputs)
batch_loss = criterion(logps, labels)
valid_loss += batch_loss.item()
# Calculate accuracy
ps = torch.exp(logps)
top_p, top_class = ps.topk(1, dim=1)
equals = top_class == labels.view(*top_class.shape)
accuracy += torch.mean(equals.type(torch.FloatTensor)).item()
# calculate average losses
train_loss = train_loss / len(train_loader)
valid_loss = valid_loss / len(valid_loader)
valid_accuracy = accuracy / len(valid_loader)
# print training/validation statistics
print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f} \tValidation Accuracy: {:.6f}'.format(
epoch + 1, train_loss, valid_loss, valid_accuracy))
if valid_loss <= valid_loss_min:
print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(
valid_loss_min,
valid_loss))
model_save_name = "Malaria.pt"
path = F"/content/drive/My Drive/{model_save_name}"
torch.save(model.state_dict(), path)
valid_loss_min = valid_loss
print(f"Time per epoch: {(time.time() - start):.3f} seconds")
# 从磁盘加载已保存的模型 进行测试
model.load_state_dict(torch.load('Malaria.pt'))
def test(model, criterion):
# monitor test loss and accuracy
test_loss = 0.
correct = 0.
total = 0.
for batch_idx, (data, target) in enumerate(test_loader):
# move to GPU
if torch.cuda.is_available():
data, target = data.cuda(), target.cuda()
# forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# calculate the loss
loss = criterion(output, target)
# update average test loss
test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))
# convert output probabilities to predicted class
pred = output.data.max(1, keepdim=True)[1]
# compare predictions to true label
correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
total += data.size(0)
print('Test Loss: {:.6f}\n'.format(test_loss))
print('\nTest Accuracy: %2d%% (%2d/%2d)' % (
100. * correct / total, correct, total))
test(model, criterion)
结果可视化
def load_input_image(img_path):
image = Image.open(img_path)
prediction_transform = transforms.Compose([transforms.Resize(size=(224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
# discard the transparent, alpha channel (that's the :3) and add the batch dimension
image = prediction_transform(image)[:3,:,:].unsqueeze(0)
return image
def predict_malaria(model, class_names, img_path):
# load the image and return the predicted breed
img = load_input_image(img_path)
model = model.cpu()
model.eval()
idx = torch.argmax(model(img))
return class_names[idx]
from glob import glob
from PIL import Image
from termcolor import colored
class_names=['Parasitized','Uninfected']
inf = np.array(glob(img_dir + "/Parasitized/*"))
uninf = np.array(glob(img_dir + "/Uninfected/*"))
for i in range(3):
img_path=inf[i]
img = Image.open(img_path)
if predict_malaria(model, class_names, img_path) == 'Parasitized':
print(colored('Parasitized', 'green'))
else:
print(colored('Uninfected', 'red'))
plt.imshow(img)
plt.show()
for i in range(3):
img_path=uninf[i]
img = Image.open(img_path)
if predict_malaria(model, class_names, img_path) == 'Uninfected':
print(colored('Uninfected', 'green'))
else:
print(colored('Parasitized', 'red'))
plt.imshow(img)
plt.show()
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