PyTorch模型转TensorRT是怎么实现的?

ubuntu-18.04
PyTorch-1.8.1
onnx-1.9.0
onnxruntime-1.7.2
cuda-11.1
cudnn-8.2.0
TensorRT-7.2.3.4

pip install onnx
pip install onnxruntime

conda install -c conda-forge onnx
conda install -c conda-forge onnxruntime

pip install netron

#--*-- coding:utf-8 --*--
import onnx 
# 注意这里导入onnx时必须在torch导入之前，否则会出现segmentation fault
import torch
import torchvision 

from model import Net

model= Net(args).cuda()#初始化模型
checkpoint = torch.load(checkpoint_path)
net.load_state_dict(checkpoint['state_dict'])#载入训练好的权重文件
print ("Model and weights LOADED successfully")

export_onnx_file = './net.onnx'
x = torch.onnx.export(net,
					torch.randn(1,1,224,224,device='cuda'), #根据输入要求初始化一个dummy input
					export_onnx_file,
					verbose=False, #是否以字符串形式显示计算图
					input_names = ["inputs"]+["params_%d"%i for i in range(120)],#输入节点的名称，这里也可以给一个list，list中名称分别对应每一层可学习的参数，便于后续查询
					output_names = ["outputs"],# 输出节点的名称
					opset_version  = 10,#onnx 支持采用的operator set, 应该和pytorch版本相关
					do_constant_folding = True,
					dynamic_axes = {"inputs":{0:"batch_size"}, 2:"h", 3:"w"}, "outputs":{0: "batch_size"},})

net = onnx.load('./erfnet.onnx') #加载onnx 计算图
onnx.checker.check_model(net) # 检查文件模型是否正确
onnx.helper.printable_graph(net.graph) #输出onnx的计算图

import netron
import onnxruntime
import numpy as np
from PIL import Image
import cv2

netron.start('./net.onnx')
test_image = np.asarray(Image.open(test_image_path).convert('L'),dtype='float32') /255.
test_image = cv2.resize(np.array(test_image),(224,224),interpolation = cv2.INTER_CUBIC)
test_image = test_image[np.newaxis,np.newaxis,:,:]
session = onnxruntime.InferenceSession('./net.onnx')
outputs = session.run(None, {"inputs": test_image})
print(len(outputs))
print(outputs[0].shape)
#根据需要处理一下outputs[0],并可视化一下结果，看看结果是否正常

cd download_path
dpkg -i nv-tensorrt-repo-ubuntu1804-cuda11.1-trt7.2.3.4-ga-20210226_1-1_amd64.deb
sudo apt-get update
sudo apt-get install tensorrt

pip install 'pycuda<2021.1'

sudo apt-get install python3-libnvinfer-dev

sudo apt-get install onnx-graphsurgeon

dpkg -l | grep TensorRT

pip3 install --upgrade setuptools pip
pip install nvidia-pyindex
pip install nvidia-tensorrt

import tensorrt
print(tensorrt.__version__)
#输出8.0.0.3

pip unintall nvidia-tensorrt #先把8.0.0.3版本卸载掉
pip install nvidia-tensorrt==7.2.* --index-url https://pypi.ngc.nvidia.com # 安装7.2.3.4banben 

import pycuda.autoinit 
import pycuda.driver as cuda
import tensorrt as trt
import torch 
import time 
from PIL import Image
import cv2,os
import torchvision 
import numpy as np
from scipy.special import softmax

### get_img_np_nchw h和postprocess_the_output函数根据需要进行修改

TRT_LOGGER = trt.Logger()

def get_img_np_nchw(img_path):
	img = Image.open(img_path).convert('L')
	img = np.asarray(img, dtype='float32')
	img = cv2.resize(np.array(img),(224, 224), interpolation = cv2.INTER_CUBIC)
	img = img / 255.
	img = img[np.newaxis, np.newaxis]
	return image
class HostDeviceMem(object):
    def __init__(self, host_mem, device_mem):
        """host_mom指代cpu内存，device_mem指代GPU内存
        """
        self.host = host_mem
        self.device = device_mem

    def __str__(self):
        return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)

    def __repr__(self):
        return self.__str__()

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))
        # Allocate host and device buffers
        host_mem = cuda.pagelocked_empty(size, dtype)
        device_mem = cuda.mem_alloc(host_mem.nbytes)
        # Append the device buffer to device 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

def get_engine(max_batch_size=1, onnx_file_path="", engine_file_path="",fp16_mode=False, int8_mode=False,save_engine=False):
    """
    params max_batch_size:      预先指定大小好分配显存
    params onnx_file_path:      onnx文件路径
    params engine_file_path:    待保存的序列化的引擎文件路径
    params fp16_mode:           是否采用FP16
    params int8_mode:           是否采用INT8
    params save_engine:         是否保存引擎
    returns:                    ICudaEngine
    """
    # 如果已经存在序列化之后的引擎，则直接反序列化得到cudaEngine
    if os.path.exists(engine_file_path):
        print("Reading engine from file: {}".format(engine_file_path))
        with open(engine_file_path, 'rb') as f, \
            trt.Runtime(TRT_LOGGER) as runtime:
            return runtime.deserialize_cuda_engine(f.read())  # 反序列化
    else:  # 由onnx创建cudaEngine
        
        # 使用logger创建一个builder 
        # builder创建一个计算图 INetworkDefinition
        explicit_batch = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
        # In TensorRT 7.0, the ONNX parser only supports full-dimensions mode, meaning that your network definition must be created with the explicitBatch flag set. For more information, see Working With Dynamic Shapes.

        with trt.Builder(TRT_LOGGER) as builder, \
            builder.create_network(explicit_batch) as network,  \
            trt.OnnxParser(network, TRT_LOGGER) as parser, \
            builder.create_builder_config() as config: # 使用onnx的解析器绑定计算图，后续将通过解析填充计算图
            profile = builder.create_optimization_profile()
            profile.set_shape("inputs", (1, 1, 224, 224),(1,1,224,224),(1,1,224,224))
            config.add_optimization_profile(profile)

            config.max_workspace_size = 1<<30  # 预先分配的工作空间大小,即ICudaEngine执行时GPU最大需要的空间
            builder.max_batch_size = max_batch_size # 执行时最大可以使用的batchsize
            builder.fp16_mode = fp16_mode
            builder.int8_mode = int8_mode

            if int8_mode:
                # To be updated
                raise NotImplementedError

            # 解析onnx文件，填充计算图
            if not os.path.exists(onnx_file_path):
                quit("ONNX file {} not found!".format(onnx_file_path))
            print('loading onnx file from path {} ...'.format(onnx_file_path))
            # with open(onnx_file_path, 'rb') as model: # 二值化的网络结果和参数
            #     print("Begining onnx file parsing")
            #     parser.parse(model.read())  # 解析onnx文件
            parser.parse_from_file(onnx_file_path) # parser还有一个从文件解析onnx的方法

            print("Completed parsing of onnx file")
            # 填充计算图完成后，则使用builder从计算图中创建CudaEngine
            print("Building an engine from file{}' this may take a while...".format(onnx_file_path))

            #################
            # import pdb;pdb.set_trace()
            print(network.get_layer(network.num_layers-1).get_output(0).shape)
            # network.mark_output(network.get_layer(network.num_layers -1).get_output(0))
            engine = builder.build_engine(network,config)  # 注意，这里的network是INetworkDefinition类型，即填充后的计算图
            print("Completed creating Engine")
            if save_engine:  #保存engine供以后直接反序列化使用
                with open(engine_file_path, 'wb') as f:
                    f.write(engine.serialize())  # 序列化
            return engine

def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
    # Transfer data from CPU to the GPU.
    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
    # Run inference.
    context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)
    # Transfer predictions back from the GPU.
    [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
    # Synchronize the stream
    stream.synchronize()
    # Return only the host outputs.
    return [out.host for out in outputs]

def postprocess_the_outputs(outputs, shape_of_output):
    outputs = outputs.reshape(*shape_of_output)
    out = np.argmax(softmax(outputs,axis=1)[0,...],axis=0)
    # import pdb;pdb.set_trace()
    return out
# 验证TensorRT模型是否正确
onnx_model_path = './Net.onnx'
max_batch_size = 1
# These two modes are dependent on hardwares
fp16_mode = False
int8_mode = False
trt_engine_path = './model_fp16_{}_int8_{}.trt'.format(fp16_mode, int8_mode)
# Build an engine
engine = get_engine(max_batch_size, onnx_model_path, trt_engine_path, fp16_mode, int8_mode , save_engine=True)
# Create the context for this engine
context = engine.create_execution_context()
# Allocate buffers for input and output
inputs, outputs, bindings, stream = allocate_buffers(engine)  # input, output: host # bindings

# Do inference
img_np_nchw = get_img_np_nchw(img_path)
inputs[0].host = img_np_nchw.reshape(-1)
shape_of_output = (max_batch_size, 2, 224, 224)

# inputs[1].host = ... for multiple input
t1 = time.time()
trt_outputs = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream) # numpy data
t2 = time.time()
feat = postprocess_the_outputs(trt_outputs[0], shape_of_output)

print('TensorRT ok')
print("Inference time with the TensorRT engine: {}".format(t2-t1))

with trt.Builder(TRT_LOGGER) as builder, \
            builder.create_network(explicit_batch) as network,  \
            trt.OnnxParser(network, TRT_LOGGER) as parser, \
            builder.create_builder_config() as config: # 使用onnx的解析器绑定计算图，后续将通过解析填充计算图
            profile = builder.create_optimization_profile()
            profile.set_shape("inputs", (1, 1, 224, 224),(1,1,224,224),(1,1,224,224))
            config.add_optimization_profile(profile)

            config.max_workspace_size = 1<<30  # 预先分配的工作空间大小,即ICudaEngine执行时GPU最大需要的空间
            engine = builder.build_engine(network,config)