基于PyTorch与TensorRT的cifar10推理加速引擎(C++)
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。
