onnx文件转rrt文件并运行trt文件（python版）

tensorrt作为一个优秀的gpu推理引擎，支持的深度学习框架和算子也十分的丰富

这里模型依然使用这个模型文件

转换onnx为trt文件

trt作为tensorrt推理的引擎文件

因此使用其他机器学习的框架都需进行转换

转换如下：

import os
import tensorrt as trt

TRT_LOGGER = trt.Logger()
model_path='FashionMNIST.onnx'
engine_file_path = "FashionMNIST.trt"
EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)#batchsize=1

with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH)\
 as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
    builder.max_workspace_size = 1 << 28
    builder.max_batch_size = 1
    if not os.path.exists(model_path):
        print('ONNX file {} not found.'.format(model_path))
        exit(0)
    print('Loading ONNX file from path {}...'.format(model_path))
    with open(model_path, 'rb') as model:
        print('Beginning ONNX file parsing')
        if not parser.parse(model.read()):
            print ('ERROR: Failed to parse the ONNX file.')
            for error in range(parser.num_errors):
                print (parser.get_error(error))
            
    network.get_input(0).shape = [1, 1, 28, 28]
    print('Completed parsing of ONNX file')
    engine = builder.build_cuda_engine(network)
    with open(engine_file_path, "wb") as f:
        f.write(engine.serialize())

运行结果如下：

接下来我们就使用这个trt文件进行推理

import pycuda.driver as cuda
import pycuda.autoinit
import cv2
import numpy as np
import os
import tensorrt as trt

TRT_LOGGER = trt.Logger()
model_path='FashionMNIST.onnx'
engine_file_path = "FashionMNIST.trt"

class HostDeviceMem(object):
    def __init__(self, host_mem, device_mem):
        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 do_inference_v2(context, bindings, inputs, outputs, stream):
    # Transfer input data to the GPU.
    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
    # Run inference.
    context.execute_async_v2(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]
        
with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime,\
runtime.deserialize_cuda_engine(f.read()) as engine, engine.create_execution_context() as context:
    inputs, outputs, bindings, stream = allocate_buffers(engine)
    image = cv2.imread('123.jpg',cv2.IMREAD_GRAYSCALE)
    image=cv2.resize(image,(28,28))
    print(image.shape)
    image=image[np.newaxis,np.newaxis,:,:].astype(np.float32)
    inputs[0].host = image
    #开始推理
    trt_outputs =do_inference_v2(context, bindings=bindings, \
    	inputs=inputs, outputs=outputs, stream=stream)
    print(trt_outputs)

运行结果：

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和这个文章运行的结果一致，至此python版使用结束

onnx文件转rrt文件并运行trt文件（python版）

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