一、写在开头
1、基于PyTorch训练出cifar10模型
2、以ONNX(Open Neural Network Exchange)格式导出模型cifar10.onnx
3、下载cifar10二进制版本数据集
4、创建TensorRT(vs c++)项目,解析模型,进行推理
二、基于PyTorch的cifar10神经网络模型
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import datetime
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Assuming that we are on a CUDA machine, this should print a CUDA device:
print(device)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# functions to show an image
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
# load dataset
transform = transforms.Compose(
[transforms.ToTensor()])
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=0)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=0)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()
# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
net = Net()
net = Net().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
# inputs, labels = data
inputs, labels = data[0].to(device), data[1].to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
correct = 0
total = 0
start = datetime.datetime.now()
with torch.no_grad():
for data in trainloader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 50000 test images: %d %%' % (
100 * correct / total))
end = datetime.datetime.now()
print((end - start).seconds, 's')
# 导出onnx模型
dummy_input = torch.randn(1, 3, 32, 32, device='cuda')
input_names = ['input']
output_names = ['output']
torch.onnx.export(net, dummy_input, "cifar10.onnx", verbose='True', input_names=input_names, output_names=output_names)
三、下载cifar10二进制数据集
https://tianchi.aliyun.com/dataset/dataDetail?dataId=43780
四、构建cifar10的TensorRT推理引擎C++
要看懂下面代码,必须参考TensorRT给的samples
std::vector<uint8_t> cifarbinary;//存储每个batch文件的标签、RGB数据
inline void readBinaryFile(const std::string& filename, vector<uint8_t>& tempbinary) {//用来读取每个batch文件
std::ifstream infile(filename, std::ifstream::binary);
assert(infile.is_open() && "Attempting to read from a file that is not open.");
infile.seekg(0, ios::end);
const int length = 10000 * (32 * 32 * 3 + 1);
gLogInfo << filename << " : " << length << " bytes" << std::endl;
tempbinary.resize(length);
infile.seekg(0, ios::beg);
infile.read(reinterpret_cast<char*>(tempbinary.data()), length);
infile.close();
}
//直接完成数据读取并进行推理bool SampleOnnxCIFAR10::processInput(samplesCommon::BufferManager& buffers, SampleUniquePtr<nvinfer1::IExecutionContext>& context)
{
const int inputC = mInputDims.d[1];
const int inputH = mInputDims.d[2];
const int inputW = mInputDims.d[3];
const int batchSize = mParams.batchSize;
const int volImg = inputC * inputH * inputW;
const int imageSize = volImg + 1;
const int outputSize = mOutputDims.d[1];
float* hostDataBuffer = static_cast<float*>(buffers.getHostBuffer(mParams.inputTensorNames[0]));
int temp[10];
int maxposition{0};
int count{ 0 };
// 5 batchbinary files
auto starttime = clock();
for (int index = 0; index < 5; ++index) {
// Read cifar10 original binary file
readBinaryFile(locateFile("data_batch_" + std::to_string(index + 1) + ".bin", mParams.dataDirs), cifarbinary);
for (int i = 0; i < 10000; ++i) {
for (int j = 0; j < 32 * 32 * 3; ++j) {
//RGB format
hostDataBuffer[j] = float(cifarbinary[i * imageSize + j])/255.0;
}
// Memcpy from host input buffers to device input buffers
buffers.copyInputToDevice();
//execute inference on every image
bool status = context->executeV2(buffers.getDeviceBindings().data());
assert(status == true);
// Memcpy from device output buffers to host output buffers
buffers.copyOutputToHost();
//verifyoutput
float* output = static_cast<float*>(buffers.getHostBuffer(mParams.outputTensorNames[0]));
maxposition = max_element(output, output + 10) - output;
//predict correctly
if (maxposition == int(cifarbinary[i * imageSize])) {
++count;
}
}
}
auto endtime = clock();
gLogInfo << "The accuracy of the TRT Engine on 50000 data is :" << float(count) / 50000.0 << endl;
gLogInfo << "TotalUse time is :" << double(endtime - starttime) / CLOCKS_PER_SEC << "s" << std::endl;
return true;
}
五、TensorRT与PyTorch的推理速度对比
在50000个image上进行了测试,TRT(c++)引擎的速度要比PyTorch快一倍多,效率提升非常明显。
模型重构以后,准确率略有下降。
原文链接: https://www.cnblogs.com/buctyk/p/13061903.html
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