tensorrt踩坑日记（python pytorch 转 onnx 推理加速）_测试onnx模型使用tensorrt推理前后对比

配置：
Ubuntu 16.04
python 3.6
onnx 1.6
pytorch 1.5
pycuda 2019.1.2
torchvision 0.1.8
建议详读,先安装好环境
官网指导手册

步骤：
1.将pytorch模型转换成onnx模型
这边用的是Darknet生成的pytoch模型

import torch
from torch.autograd import Variable
import onnx


input_name = ['input']
output_name = ['output']
input = Variable(torch.randn(1, 3, 544, 544)).cuda()
model = x.model.cuda()#x.model为我生成的模型

# model = torch.load('', map_location="cuda:0")
torch.onnx.export(model, input, 'model.onnx', input_names=input_name, output_names=output_name, verbose=True)
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其中

#model = x.model.cuda()
#若是不添加cuda()
model = x.model
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出现报错

RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same
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2.检查模型

model = onnx.load("model.onnx")
onnx.checker.check_model(model)
print("==> Passed")
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3.测试onnx模型使用tensorrt推理前后对比

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

filename = '000000.jpg'
max_batch_size = 1
onnx_model_path = 'yolo.onnx'

TRT_LOGGER = trt.Logger()  # This logger is required to build an engine


def get_img_np_nchw(filename):
    image = cv2.imread(filename)
    image_cv = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image_cv = cv2.resize(image_cv, (1920, 1080))
    miu = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    img_np = np.array(image_cv, dtype=float) / 255.
    r = (img_np[:, :, 0] - miu[0]) / std[0]
    g = (img_np[:, :, 1] - miu[1]) / std[1]
    b = (img_np[:, :, 2] - miu[2]) / std[2]
    img_np_t = np.array([r, g, b])
    img_np_nchw = np.expand_dims(img_np_t, axis=0)
    return img_np_nchw

class HostDeviceMem(object):
    def __init__(self, host_mem, device_mem):
        """Within this context, host_mom means the cpu memory and device means the GPU memory
        """
        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,
               ):
    """Attempts to load a serialized engine if available, otherwise builds a new TensorRT engine and saves it."""

    def build_engine(max_batch_size, save_engine):
        """Takes an ONNX file and creates a TensorRT engine to run inference with"""
        EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
        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 << 30  # Your workspace size
            builder.max_batch_size = max_batch_size
            # pdb.set_trace()
            builder.fp16_mode = fp16_mode  # Default: False
            builder.int8_mode = int8_mode  # Default: False
            if int8_mode:
                # To be updated
                raise NotImplementedError

            # Parse model file
            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:
                print('Beginning ONNX file parsing')
                parser.parse(model.read())

                if not parser.parse(model.read()):
                    for error in range(parser.num_errors):
                        print(parser.get_error(error))
                    print("===========Parsing fail!!!!=================")
                else :
                    print('Completed parsing of ONNX file')

            print('Building an engine from file {}; this may take a while...'.format(onnx_file_path))

            engine = builder.build_cuda_engine(network)
            print("Completed creating Engine")

            if save_engine:
                with open(engine_file_path, "wb") as f:
                    f.write(engine.serialize())
            return engine

    if os.path.exists(engine_file_path):
        # If a serialized engine exists, load it instead of building a new one.
        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())
    else:
        return build_engine(max_batch_size, save_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(h_outputs, shape_of_output):
    h_outputs = h_outputs.reshape(*shape_of_output)
    return h_outputs



img_np_nchw = get_img_np_nchw(filename)
img_np_nchw = img_np_nchw.astype(dtype=np.float32)

# 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)
# 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
shape_of_output = (max_batch_size, 1000)
# Load data to the buffer
inputs[0].host = img_np_nchw.reshape(-1)

# 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')
#将model改为自己的模型,此处为pytoch的resnet50,需联网下载
model = torchvision.models.resnet50(pretrained=True).cuda()
resnet_model = model.eval()

input_for_torch = torch.from_numpy(img_np_nchw).cuda()
t3 = time.time()
feat_2= resnet_model(input_for_torch)
t4 = time.time()
feat_2 = feat_2.cpu().data.numpy()
print('Pytorch ok!')


mse = np.mean((feat - feat_2)**2)
print("Inference time with the TensorRT engine: {}".format(t2-t1))
print("Inference time with the PyTorch model: {}".format(t4-t3))
print('MSE Error = {}'.format(mse))

print('All completed!')
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报错：

In node -1 (importModel): INVALID_VALUE: Assertion failed: !_importer_ctx.network()->hasImplicitBatchDimension() && "This version of the ONNX parser only supports TensorRT INetworkDefinitions with an explicit batch dimension. Please ensure the network was created using the EXPLICIT_BATCH NetworkDefinitionCreationFlag."

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解决：

    def build_engine(max_batch_size, save_engine):
 
        EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
        with trt.Builder(TRT_LOGGER) as builder, \
                builder.create_network(EXPLICIT_BATCH) as network, \
                trt.OnnxParser(network, TRT_LOGGER) as parser:
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报错:

Traceback (most recent call last):
  line 126, in <listcomp>
    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
pycuda._driver.LogicError: cuMemcpyHtoDAsync failed: invalid argument
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解决:

def get_img_np_nchw(filename):
    image = cv2.imread(filename)
    image_cv = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image_cv = cv2.resize(image_cv, (1920, 1080))
 
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输入的检测图像尺寸需要resize成model的input的size
改为

def get_img_np_nchw(filename):
    image = cv2.imread(filename)
    image_cv = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image_cv = cv2.resize(image_cv, (544,544))
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报错

line 139, in postprocess_the_outputs
    h_outputs = h_outputs.reshape(*shape_of_output)
ValueError: cannot reshape array of size 5780 into shape (1,1000)
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解决:

#shape_of_output = (max_batch_size, 1000)
#修改成自己模型ouput的大小
shape_of_output = (1,20,17,17)
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