本文是基于TensorRT 5.0.2基础上，关于其内部的network_api_pytorch_mnist例子的分析和介绍。
本例子直接基于pytorch进行训练，然后直接导出权重值为字典，此时并未dump该权重；接着基于tensorrt的network进行手动设计网络结构并填充权重。本文核心在于介绍network api的使用
1 引言
假设当前路径为：
TensorRT-5.0.2.6/samples其对应当前例子文件目录树为：
# tree python
python
├── common.py
├── network_api_pytorch_mnist
│ ├── model.py
│ ├── README.md
│ ├── requirements.txt
│ └── sample.py2 基于pytorch
其中只有2个文件：
- model：该文件包含用于训练Pytorch MNIST 模型的函数
- sample：该文件使用Pytorch生成的mnist模型去创建一个TensorRT inference engine
首先介绍下model.py
首先下载对应的mnist数据，并放到对应缓存路径下：
'''
i) 去http://yann.lecun.com/exdb/mnist/index.html 下载四个
ii) 放到/tmp/mnist/data/MNIST/raw/
'''
/tmp/mnist/data/MNIST/raw
├── t10k-images-idx3-ubyte.gz
├── t10k-labels-idx1-ubyte.gz
├── train-images-idx3-ubyte.gz
└── train-labels-idx1-ubyte.gz这样加快model.py读取mnist数据的速度
# 该文件包含用于训练Pytorch MNIST模型的函数
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import numpy as np
import os
from random import randint
# Network结构，2层卷积+dropout+一层全连接+一层softmax
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 20, kernel_size=5)
self.conv2 = nn.Conv2d(20, 50, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(800, 500)
self.fc2 = nn.Linear(500, 10)
def forward(self, x):
x = F.max_pool2d(self.conv1(x), kernel_size=2, stride=2)
x = F.max_pool2d(self.conv2(x), kernel_size=2, stride=2)
x = x.view(-1, 800)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
class MnistModel(object):
''' 初始化'''
def __init__(self):
self.batch_size = 64
self.test_batch_size = 100
self.learning_rate = 0.01
self.sgd_momentum = 0.9
self.log_interval = 100
# Fetch MNIST data set.
# 训练时候的数据读取
self.train_loader = torch.utils.data.DataLoader(
datasets.MNIST('/tmp/mnist/data', train=True, download=True, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=self.batch_size,
shuffle=True)
# 测试时候的数据读取
self.test_loader = torch.utils.data.DataLoader(
datasets.MNIST('/tmp/mnist/data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=self.test_batch_size,
shuffle=True)
# 网络结构实例化
self.network = Net()
''' 训练该网络，然后每个epoch之后进行验证.'''
def learn(self, num_epochs=5):
# 每个epoch的训练过程
def train(epoch):
self.network.train() # 开启训练flag
optimizer = optim.SGD(self.network.parameters(), lr=self.learning_rate, momentum=self.sgd_momentum)
for batch, (data, target) in enumerate(self.train_loader):
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = self.network(data) # 一次前向
loss = F.nll_loss(output, target) # 计算loss
loss.backward() # 反向计算梯度
optimizer.step()
if batch % self.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch,
batch * len(data),
len(self.train_loader.dataset),
100. * batch / len(self.train_loader),
loss.data.item()))
# 测试该网络
def test(epoch):
self.network.eval() # 开启验证flag
test_loss = 0
correct = 0
for data, target in self.test_loader:
with torch.no_grad():
data, target = Variable(data), Variable(target)
output = self.network(data) # 前向
test_loss += F.nll_loss(output, target).data.item() # 累加loss值
pred = output.data.max(1)[1] # 计算当次预测值
correct += pred.eq(target.data).cpu().sum() # 累加预测正确的
test_loss /= len(self.test_loader)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss,
correct,
len(self.test_loader.dataset),
100. * correct / len(self.test_loader.dataset)))
# 调用上面定义好的训练函数和测试函数
for e in range(num_epochs):
train(e + 1)
test(e + 1)
''' 可视化权重'''
def get_weights(self):
return self.network.state_dict()
''' 随机获取 测试样本队列中 样本 '''
def get_random_testcase(self):
data, target = next(iter(self.test_loader))
case_num = randint(0, len(data) - 1)
test_case = data.numpy()[case_num].ravel().astype(np.float32)
test_name = target.numpy()[case_num]
return test_case, test_name
可以看出，上面的代码就是定义了网络结构，和训练网络的函数方法。下面介绍下sample.py
# 该例子用pytorch编写的MNIST模型去生成一个TensorRT Inference Engine
from PIL import Image
import numpy as np
import pycuda.driver as cuda
import pycuda.autoinit
import tensorrt as trt
import sys, os
sys.path.insert(1, os.path.join(sys.path[0], ".."))
import model
# import common
# 这里将common中的GiB和find_sample_data，do_inference等函数移动到该py文件中，保证自包含。
def GiB(val):
'''以GB为单位，计算所需要的存储值，向左位移10bit表示KB，20bit表示MB '''
return val * 1 << 30
def find_sample_data(description="Runs a TensorRT Python sample", subfolder="", find_files=[]):
'''该函数就是一个参数解析函数。
Parses sample arguments.
Args:
description (str): Description of the sample.
subfolder (str): The subfolder containing data relevant to this sample
find_files (str): A list of filenames to find. Each filename will be replaced with an absolute path.
Returns:
str: Path of data directory.
Raises:
FileNotFoundError
'''
# 为了简洁，这里直接将路径硬编码到代码中。
data_root = kDEFAULT_DATA_ROOT = os.path.abspath("/TensorRT-5.0.2.6/python/data/")
subfolder_path = os.path.join(data_root, subfolder)
if not os.path.exists(subfolder_path):
print("WARNING: " + subfolder_path + " does not exist. Using " + data_root + " instead.")
data_path = subfolder_path if os.path.exists(subfolder_path) else data_root
if not (os.path.exists(data_path)):
raise FileNotFoundError(data_path + " does not exist.")
for index, f in enumerate(find_files):
find_files[index] = os.path.abspath(os.path.join(data_path, f))
if not os.path.exists(find_files[index]):
raise FileNotFoundError(find_files[index] + " does not exist. ")
if find_files:
return data_path, find_files
else:
return data_path
#-----------------
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
class ModelData(object):
INPUT_NAME = "data"
INPUT_SHAPE = (1, 28, 28)
OUTPUT_NAME = "prob"
OUTPUT_SIZE = 10
DTYPE = trt.float32
'''main中第三步：构建engine'''
# 该函数构建的网络结构和上面model.py中一致，只是这里通过训练后的网络模型读取对应的权重值，并填充到network中
# network是TensorRT提供的，weights是Pytorch训练后的模型提供的
def populate_network(network, weights):
'''network支持的方法来自https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/python_api/infer/Graph/Network.html '''
# 基于提供的权重配置网络层
input_tensor = network.add_input(name=ModelData.INPUT_NAME, dtype=ModelData.DTYPE, shape=ModelData.INPUT_SHAPE)
conv1_w = weights['conv1.weight'].numpy()
conv1_b = weights['conv1.bias'].numpy()
conv1 = network.add_convolution(input=input_tensor, num_output_maps=20, kernel_shape=(5, 5), kernel=conv1_w, bias=conv1_b)
conv1.stride = (1, 1)
pool1 = network.add_pooling(input=conv1.get_output(0), type=trt.PoolingType.MAX, window_size=(2, 2))
pool1.stride = (2, 2)
conv2_w = weights['conv2.weight'].numpy()
conv2_b = weights['conv2.bias'].numpy()
conv2 = network.add_convolution(pool1.get_output(0), 50, (5, 5), conv2_w, conv2_b)
conv2.stride = (1, 1)
pool2 = network.add_pooling(conv2.get_output(0), trt.PoolingType.MAX, (2, 2))
pool2.stride = (2, 2)
fc1_w = weights['fc1.weight'].numpy()
fc1_b = weights['fc1.bias'].numpy()
fc1 = network.add_fully_connected(input=pool2.get_output(0), num_outputs=500, kernel=fc1_w, bias=fc1_b)
relu1 = network.add_activation(input=fc1.get_output(0), type=trt.ActivationType.RELU)
fc2_w = weights['fc2.weight'].numpy()
fc2_b = weights['fc2.bias'].numpy()
fc2 = network.add_fully_connected(relu1.get_output(0), ModelData.OUTPUT_SIZE, fc2_w, fc2_b)
fc2.get_output(0).name = ModelData.OUTPUT_NAME
network.mark_output(tensor=fc2.get_output(0))
'''main中第三步：构建engine'''
def build_engine(weights):
'''下面的create_network会返回一个tensorrt.tensorrt.INetworkDefinition对象
https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/python_api/infer/Core/Builder.html?highlight=create_network#tensorrt.Builder.create_network
'''
with trt.Builder(TRT_LOGGER) as builder, \
builder.create_network() as network:
builder.max_workspace_size = GiB(1)
populate_network(network, weights) # 用之前的pytorch模型中的权重来填充network
# 构建并返回一个engine.
return builder.build_cuda_engine(network)
'''main中第四步 '''
def allocate_buffers(engine):
inputs = []
outputs = []
bindings = []
stream = cuda.Stream()
for binding in engine:
size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
dtype = trt.nptype(engine.get_binding_dtype(binding))
# 分配host和device端的buffer
host_mem = cuda.pagelocked_empty(size, dtype)
device_mem = cuda.mem_alloc(host_mem.nbytes)
# 将device端的buffer追加到device的bindings.
bindings.append(int(device_mem))
# Append to the appropriate list.
if engine.binding_is_input(binding):
inputs.append(HostDeviceMem(host_mem, device_mem))
else:
outputs.append(HostDeviceMem(host_mem, device_mem))
return inputs, outputs, bindings, stream
'''main中第五步：执行inference '''
# 用pytorch的DataLoader随机选择一个测试样本
def load_random_test_case(model, pagelocked_buffer):
img, expected_output = model.get_random_testcase()
# 将图片copy到host端的pagelocked buffer
np.copyto(pagelocked_buffer, img)
return expected_output
'''main中第六步：执行inference '''
# 该函数可以适应多个输入/输出；输入和输出格式为HostDeviceMem对象组成的列表
def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
# 将数据移动到GPU
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
# 执行inference.
context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)
# 将结果从 GPU写回到host端
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
# 同步stream
stream.synchronize()
# 返回host端的输出结果
return [out.host for out in outputs]
def main():
''' 1 - 寻找模型文件'''
data_path = find_sample_data(description="Runs an MNIST network using a PyTorch model", subfolder="mnist")
''' 2 - 训练该模型'''
mnist_model = model.MnistModel()
mnist_model.learn()
# 获取训练好的权重
weights = mnist_model.get_weights()
''' 3 - 基于build_engine构建engine；用tensorrt来进行inference '''
with build_engine(weights) as engine:
''' 4 - 构建engine, 分配buffers， 创建一个流 '''
inputs, outputs, bindings, stream = allocate_buffers(engine)
with engine.create_execution_context() as context:
''' 5 - 读取测试样本，并归一化'''
case_num = load_random_test_case(mnist_model, pagelocked_buffer=inputs[0].host)
''' 6 -执行inference，do_inference函数会返回一个list类型，此处只有一个元素 '''
[output] = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
pred = np.argmax(output)
print("Test Case: " + str(case_num))
print("Prediction: " + str(pred))
if __name__ == '__main__':
main()
运行结果如下：
