Jetson Nano 使用yolov3-tiny及TensorRT加速，达到接近实时目标检测与识别

前言

Jetson nano运行yolov3-tiny模型，在没有使用tensorRT优化加速的情况下，达不到实时检测识别的效果，比较卡顿。英伟达官方给出，使用了tensorRT优化加速之后，帧率能达到25fps。

本文详细介绍了在nano上怎么用tensorRT优化模型。

环境配置

基本环境的配置这里就不说了,Jetpack4.3+上已经自带了好多重要环境，包括tensorRT、CUDA、cuDNN等。这里说几个比较重要的环境的配置：

安装onnx

这个在我的另一篇文章上有介绍。传送门：Jetson Nano 安装onnx
注意onnx版本选择1.4.1的

安装pillow

sudo pip3 install Pillow

安装pycuda

export PATH=/usr/local/cuda/bin:\${PATH}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:\${LD_LIBRARY_PATH}
sudo pip3 install pycuda

安装numpy

sudo pip3 install numpy
或者
sudo apt-get install python3-numpy

模型转换

yolov3-tiny—>onnx

新建yolov3_tiny_to_onnx.py文件。
添加以下代码：

from __future__ import print_function

import sys
import hashlib
import argparse
from collections import OrderedDict

import onnx
from onnx import helper
from onnx import TensorProto
import numpy as np


class DarkNetParser(object):
    """Definition of a parser for DarkNet-based YOLOv3."""

    def __init__(self, supported_layers):
        """Initializes a DarkNetParser object.

        Keyword argument:
        supported_layers -- a string list of supported layers in DarkNet naming convention,
        parameters are only added to the class dictionary if a parsed layer is included.
        """

        # A list of YOLOv3 layers containing dictionaries with all layer
        # parameters:
        self.layer_configs = OrderedDict()
        self.supported_layers = supported_layers
        self.layer_counter = 0

    def parse_cfg_file(self, cfg_file_path):
        """Takes the yolov3.cfg file and parses it layer by layer,
        appending each layer's parameters as a dictionary to layer_configs.

        Keyword argument:
        cfg_file_path -- path to the yolov3.cfg file as string
        """
        with open(cfg_file_path, 'r') as cfg_file:
            remainder = cfg_file.read()
            while remainder is not None:
                layer_dict, layer_name, remainder = self._next_layer(remainder)
                if layer_dict is not None:
                    self.layer_configs[layer_name] = layer_dict
        return self.layer_configs

    def _next_layer(self, remainder):
        """Takes in a string and segments it by looking for DarkNet delimiters.
        Returns the layer parameters and the remaining string after the last delimiter.
        Example for the first Conv layer in yolo.cfg ...

        [convolutional]
        batch_normalize=1
        filters=32
        size=3
        stride=1
        pad=1
        activation=leaky

        ... becomes the following layer_dict return value:
        {'activation': 'leaky', 'stride': 1, 'pad': 1, 'filters': 32,
        'batch_normalize': 1, 'type': 'convolutional', 'size': 3}.

        '001_convolutional' is returned as layer_name, and all lines that follow in yolo.cfg
        are returned as the next remainder.

        Keyword argument:
        remainder -- a string with all raw text after the previously parsed layer
        """
        remainder = remainder.split('[', 1)
        if len(remainder) == 2:
            remainder = remainder[1]
        else:
            return None, None, None
        remainder = remainder.split(']', 1)
        if len(remainder) == 2:
            layer_type, remainder = remainder
        else:
            return None, None, None
        if remainder.replace(' ', '')[0] == '#':
            remainder = remainder.split('\n', 1)[1]

        layer_param_block, remainder = remainder.split('\n\n', 1)
        layer_param_lines = layer_param_block.split('\n')[1:]
        layer_name = str(self.layer_counter).zfill(3) + '_' + layer_type
        layer_dict = dict(type=layer_type)
        if layer_type in self.supported_layers:
            for param_line in layer_param_lines:
                if param_line[0] == '#':
                    continue
                param_type, param_value = self._parse_params(param_line)
                layer_dict[param_type] = param_value
        self.layer_counter += 1
        return layer_dict, layer_name, remainder

    def _parse_params(self, param_line):
        """Identifies the parameters contained in one of the cfg file and returns
        them in the required format for each parameter type, e.g. as a list, an int or a float.

        Keyword argument:
        param_line -- one parsed line within a layer block
        """
        param_line = param_line.replace(' ', '')
        param_type, param_value_raw = param_line.split('=')
        param_value = None
        if param_type == 'layers':
            layer_indexes = list()
            for index in param_value_raw.split(','):
                layer_indexes.append(int(index))
            param_value = layer_indexes
        elif isinstance(param_value_raw, str) and not param_value_raw.isalpha():
            condition_param_value_positive = param_value_raw.isdigit()
            condition_param_value_negative = param_value_raw[0] == '-' and \
                param_value_raw[1:].isdigit()
            if condition_param_value_positive or condition_param_value_negative:
                param_value = int(param_value_raw)
            else:
                param_value = float(param_value_raw)
        else:
            param_value = str(param_value_raw)
        return param_type, param_value


class MajorNodeSpecs(object):
    """Helper class used to store the names of ONNX output names,
    corresponding to the output of a DarkNet layer and its output channels.
    Some DarkNet layers are not created and there is no corresponding ONNX node,
    but we still need to track them in order to set up skip connections.
    """

    def __init__(self, name, channels):
        """ Initialize a MajorNodeSpecs object.

        Keyword arguments:
        name -- name of the ONNX node
        channels -- number of output channels of this node
        """
        self.name = name
        self.channels = channels
        self.created_onnx_node = False
        if name is not None and isinstance(channels, int) and channels > 0:
            self.created_onnx_node = True


class ConvParams(object):
    """Helper class to store the hyper parameters of a Conv layer,
    including its prefix name in the ONNX graph and the expected dimensions
    of weights for convolution, bias, and batch normalization.

    Additionally acts as a wrapper for generating safe names for all
    weights, checking on feasible combinations.
    """

    def __init__(self, node_name, batch_normalize, conv_weight_dims):
        """Constructor based on the base node name (e.g. 101_convolutional), the batch
        normalization setting, and the convolutional weights shape.

        Keyword arguments:
        node_name -- base name of this YOLO convolutional layer
        batch_normalize -- bool value if batch normalization is used
        conv_weight_dims -- the dimensions of this layer's convolutional weights
        """
        self.node_name = node_name
        self.batch_normalize = batch_normalize
        assert len(conv_weight_dims) == 4
        self.conv_weight_dims = conv_weight_dims

    def generate_param_name(self, param_category, suffix):
        """Generates a name based on two string inputs,
        and checks if the combination is valid."""
        assert suffix
        assert param_category in ['bn', 'conv']
        assert(suffix in ['scale', 'mean', 'var', 'weights', 'bias'])
        if param_category == 'bn':
            assert self.batch_normalize
            assert suffix in ['scale', 'bias', 'mean', 'var']
        elif param_category == 'conv':
            assert suffix in ['weights', 'bias']
            if suffix == 'bias':
                assert not self.batch_normalize
        param_name = self.node_name + '_' + param_category + '_' + suffix
        return param_name

class UpsampleParams(object):
    #Helper class to store the scale parameter for an Upsample node.

    def __init__(self, node_name, value):
        """Constructor based on the base node name (e.g. 86_Upsample),
        and the value of the scale input tensor.

        Keyword arguments:
        node_name -- base name of this YOLO Upsample layer
        value -- the value of the scale input to the Upsample layer as a numpy array
        """
        self.node_name = node_name
        self.value = value

    def generate_param_name(self):
        """Generates the scale parameter name for the Upsample node."""
        param_name = self.node_name + '_' + 'scale'
        return param_name

class WeightLoader(object):
    """Helper class used for loading the serialized weights of a binary file stream
    and returning the initializers and the input tensors required for populating
    the ONNX graph with weights.
    """

    def __init__(self, weights_file_path):
        """Initialized with a path to the YOLOv3 .weights file.

        Keyword argument:
        weights_file_path -- path to the weights file.
        """
        self.weights_file = self._open_weights_file(weights_file_path)

    def load_upsample_scales(self, upsample_params):
        """Returns the initializers with the value of the scale input
        tensor given by upsample_params.

        Keyword argument:
        upsample_params -- a UpsampleParams object
        """
        initializer = list()
        inputs = list()
        name = upsample_params.generate_param_name()
        shape = upsample_params.value.shape
        data = upsample_params.value
        scale_init = helper.make_tensor(
            name, TensorProto.FLOAT, shape, data)
        scale_input = helper.make_tensor_value_info(
            name, TensorProto.FLOAT, shape)
        initializer.append(scale_init)
        inputs.append(scale_input)
        return initializer, inputs


    def load_conv_weights(self, conv_params):
        """Returns the initializers with weights from the weights file and
        the input tensors of a convolutional layer for all corresponding ONNX nodes.

        Keyword argument:
        conv_params -- a ConvParams object
        """
        initializer = list()
        inputs = list()
        if conv_params.batch_normalize:
            bias_init, bias_input = self._create_param_tensors(
                conv_params, 'bn', 'bias')
            bn_scale_init, bn_scale_input = self._create_param_tensors(
                conv_params, 'bn', 'scale')
            bn_mean_init, bn_mean_input = self._create_param_tensors(
                conv_params, 'bn', 'mean')
            bn_var_init, bn_var_input = self._create_param_tensors(
                conv_params, 'bn', 'var')
            initializer.extend(
                [bn_scale_init, bias_init, bn_mean_init, bn_var_init])
            inputs.extend([bn_scale_input, bias_input,
                           bn_mean_input, bn_var_input])
        else:
            bias_init, bias_input = self._create_param_tensors(
                conv_params, 'conv', 'bias')
            initializer.append(bias_init)
            inputs.append(bias_input)
        conv_init, conv_input = self._create_param_tensors(
            conv_params, 'conv', 'weights')
        initializer.append(conv_init)
        inputs.append(conv_input)
        return initializer, inputs

    def _open_weights_file(self, weights_file_path):
        """Opens a YOLOv3 DarkNet file stream and skips the header.

        Keyword argument:
        weights_file_path -- path to the weights file.
        """
        weights_file = open(weights_file_path, 'rb')
        length_header = 5
        np.ndarray(
            shape=(length_header, ), dtype='int32', buffer=weights_file.read(
                length_header * 4))
        return weights_file

    def _create_param_tensors(self, conv_params, param_category, suffix):
        """Creates the initializers with weights from the weights file together with
        the input tensors.

        Keyword arguments:
        conv_params -- a ConvParams object
        param_category -- the category of parameters to be created ('bn' or 'conv')
        suffix -- a string determining the sub-type of above param_category (e.g.,
        'weights' or 'bias')
        """
        param_name, param_data, param_data_shape = self._load_one_param_type(
            conv_params, param_category, suffix)

        initializer_tensor = helper.make_tensor(
            param_name, TensorProto.FLOAT, param_data_shape, param_data)
        input_tensor = helper.make_tensor_value_info(
            param_name, TensorProto.FLOAT, param_data_shape)
        return initializer_tensor, input_tensor

    def _load_one_param_type(self, conv_params, param_category, suffix):
        """Deserializes the weights from a file stream in the DarkNet order.

        Keyword arguments:
        conv_params -- a ConvParams object
        param_category -- the category of parameters to be created ('bn' or 'conv')
        suffix -- a string determining the sub-type of above param_category (e.g.,
        'weights' or 'bias')
        """
        param_name = conv_params.generate_param_name(param_category, suffix)
        channels_out, channels_in, filter_h, filter_w = conv_params.conv_weight_dims
        if param_category == 'bn':
            param_shape = [channels_out]
        elif param_category == 'conv':
            if suffix == 'weights':
                param_shape = [channels_out, channels_in, filter_h, filter_w]
            elif suffix == 'bias':
                param_shape = [channels_out]
        param_size = np.product(np.array(param_shape))
        param_data = np.ndarray(
            shape=param_shape,
            dtype='float32',
            buffer=self.weights_file.read(param_size * 4))
        param_data = param_data.flatten().astype(float)
        return param_name, param_data, param_shape


class GraphBuilderONNX(object):
    """Class for creating an ONNX graph from a previously generated list of layer dictionaries."""

    def __init__(self, model_name, output_tensors):
        """Initialize with all DarkNet default parameters used creating YOLOv3,
        and specify the output tensors as an OrderedDict for their output dimensions
        with their names as keys.

        Keyword argument:
        output_tensors -- the output tensors as an OrderedDict containing the keys'
        output dimensions
        """
        self.model_name = model_name
        self.output_tensors = output_tensors
        self._nodes = list()
        self.graph_def = None
        self.input_tensor = None
        self.epsilon_bn = 1e-5
        self.momentum_bn = 0.99
        self.alpha_lrelu = 0.1
        self.param_dict = OrderedDict()
        self.major_node_specs = list()
        self.batch_size = 1

    def build_onnx_graph(
            self,
            layer_configs,
            weights_file_path,
            verbose=True):
        """Iterate over all layer configs (parsed from the DarkNet representation
        of YOLOv3-608), create an ONNX graph, populate it with weights from the weights
        file and return the graph definition.

        Keyword arguments:
        layer_configs -- an OrderedDict object with all parsed layers' configurations
        weights_file_path -- location of the weights file
        verbose -- toggles if the graph is printed after creation (default: True)
        """
        for layer_name in layer_configs.keys():
            layer_dict = layer_configs[layer_name]
            major_node_specs = self._make_onnx_node(layer_name, layer_dict)
            if major_node_specs.name is not None:
                self.major_node_specs.append(major_node_specs)
        outputs = list()
        for tensor_name in self.output_tensors.keys():
            output_dims = [self.batch_size, ] + \
                self.output_tensors[tensor_name]
            output_tensor = helper.make_tensor_value_info(
                tensor_name, TensorProto.FLOAT, output_dims)
            outputs.append(output_tensor)
        inputs = [self.input_tensor]
        weight_loader = WeightLoader(weights_file_path)
        initializer = list()
        # If a layer has parameters, add them to the initializer and input lists.
        for layer_name in self.param_dict.keys():
            _, layer_type = layer_name.split('_', 1)
            params = self.param_dict[layer_name]
            if layer_type == 'convolutional':
                initializer_layer, inputs_layer = weight_loader.load_conv_weights(
                    params)
                initializer.extend(initializer_layer)
                inputs.extend(inputs_layer)
            elif layer_type == 'upsample':
                initializer_layer, inputs_layer = weight_loader.load_upsample_scales(
                    params)
                initializer.extend(initializer_layer)
                inputs.extend(inputs_layer)
        del weight_loader
        self.graph_def = helper.make_graph(
            nodes=self._nodes,
            name=self.model_name,
            inputs=inputs,
            outputs=outputs,
            initializer=initializer
        )
        if verbose:
            print(helper.printable_graph(self.graph_def))
        model_def = helper.make_model(self.graph_def,
                                      producer_name='NVIDIA TensorRT sample')
        return model_def

    def _make_onnx_node(self, layer_name, layer_dict):
        """Take in a layer parameter dictionary, choose the correct function for
        creating an ONNX node and store the information important to graph creation
        as a MajorNodeSpec object.

        Keyword arguments:
        layer_name -- the layer's name (also the corresponding key in layer_configs)
        layer_dict -- a layer parameter dictionary (one element of layer_configs)
        """
        layer_type = layer_dict['type']
        if self.input_tensor is None:
            if layer_type == 'net':
                major_node_output_name, major_node_output_channels = self._make_input_tensor(
                    layer_name, layer_dict)
                major_node_specs = MajorNodeSpecs(major_node_output_name,
                                                  major_node_output_channels)
            else:
                raise ValueError('The first node has to be of type "net".')
        else:
            node_creators = dict()
            node_creators['convolutional'] = self._make_conv_node
            node_creators['maxpool'] = self._make_maxpool_node
            node_creators['shortcut'] = self._make_shortcut_node
            node_creators['route'] = self._make_route_node
            node_creators['upsample'] = self._make_upsample_node

            if layer_type in node_creators.keys():
                major_node_output_name, major_node_output_channels = \
                    node_creators[layer_type](layer_name, layer_dict)
                major_node_specs = MajorNodeSpecs(major_node_output_name,
                                                  major_node_output_channels)
            else:
                print(
                    'Layer of type %s not supported, skipping ONNX node generation.' %
                    layer_type)
                major_node_specs = MajorNodeSpecs(layer_name,
                                                  None)
        return major_node_specs

    def _make_input_tensor(self, layer_name, layer_dict):
        """Create an ONNX input tensor from a 'net' layer and store the batch size.

        Keyword arguments:
        layer_name -- the layer's name (also the corresponding key in layer_configs)
        layer_dict -- a layer parameter dictionary (one element of layer_configs)
        """
        batch_size = layer_dict['batch']
        channels = layer_dict['channels']
        height = layer_dict['height']
        width = layer_dict['width']
        self.batch_size = batch_size
        input_tensor = helper.make_tensor_value_info(
            str(layer_name), TensorProto.FLOAT, [
                batch_size, channels, height, width])
        self.input_tensor = input_tensor
        return layer_name, channels

    def _get_previous_node_specs(self, target_index=-1):
        """Get a previously generated ONNX node (skip those that were not generated).
        Target index can be passed for jumping to a specific index.

        Keyword arguments:
        target_index -- optional for jumping to a specific index (default: -1 for jumping
        to previous element)
        """
        previous_node = None
        for node in self.major_node_specs[target_index::-1]:
            if node.created_onnx_node:
                previous_node = node
                break
        assert previous_node is not None
        return previous_node

    def _make_conv_node(self, layer_name, layer_dict):
        """Create an ONNX Conv node with optional batch normalization and
        activation nodes.

        Keyword arguments:
        layer_name -- the layer's name (also the corresponding key in layer_configs)
        layer_dict -- a layer parameter dictionary (one element of layer_configs)
        """
        previous_node_specs = self._get_previous_node_specs()
        inputs = [previous_node_specs.name]
        previous_channels = previous_node_specs.channels
        kernel_size = layer_dict['size']
        stride = layer_dict['stride']
        filters = layer_dict['filters']
        batch_normalize = False
        if 'batch_normalize' in layer_dict.keys(
        ) and layer_dict['batch_normalize'] == 1:
            batch_normalize = True

        kernel_shape = [kernel_size, kernel_size]
        weights_shape = [filters, previous_channels] + kernel_shape
        conv_params = ConvParams(layer_name, batch_normalize, weights_shape)

        strides = [stride, stride]
        dilations = [1, 1]
        weights_name = conv_params.generate_param_name('conv', 'weights')
        inputs.append(weights_name)
        if not batch_normalize:
            bias_name = conv_params.generate_param_name('conv', 'bias')
            inputs.append(bias_name)

        conv_node = helper.make_node(
            'Conv',
            inputs=inputs,
            outputs=[layer_name],
            kernel_shape=kernel_shape,
            strides=strides,
            auto_pad='SAME_LOWER',
            dilations=dilations,
            name=layer_name
        )
        self._nodes.append(conv_node)
        inputs = [layer_name]
        layer_name_output = layer_name

        if batch_normalize:
            layer_name_bn = layer_name + '_bn'
            bn_param_suffixes = ['scale', 'bias', 'mean', 'var']
            for suffix in bn_param_suffixes:
                bn_param_name = conv_params.generate_param_name('bn', suffix)
                inputs.append(bn_param_name)
            batchnorm_node = helper.make_node(
                'BatchNormalization',
                inputs=inputs,
                outputs=[layer_name_bn],
                epsilon=self.epsilon_bn,
                momentum=self.momentum_bn,
                name=layer_name_bn
            )
            self._nodes.append(batchnorm_node)
            inputs = [layer_name_bn]
            layer_name_output = layer_name_bn

        if layer_dict['activation'] == 'leaky':
            layer_name_lrelu = layer_name + '_lrelu'

            lrelu_node = helper.make_node(
                'LeakyRelu',
                inputs=inputs,
                outputs=[layer_name_lrelu],
                name=layer_name_lrelu,
                alpha=self.alpha_lrelu
            )
            self._nodes.append(lrelu_node)
            inputs = [layer_name_lrelu]
            layer_name_output = layer_name_lrelu
        elif layer_dict['activation'] == 'linear':
            pass
        else:
            print('Activation not supported.')

        self.param_dict[layer_name] = conv_params
        return layer_name_output, filters

    def _make_shortcut_node(self, layer_name, layer_dict):
        """Create an ONNX Add node with the shortcut properties from
        the DarkNet-based graph.

        Keyword arguments:
        layer_name -- the layer's name (also the corresponding key in layer_configs)
        layer_dict -- a layer parameter dictionary (one element of layer_configs)
        """
        shortcut_index = layer_dict['from']
        activation = layer_dict['activation']
        assert activation == 'linear'

        first_node_specs = self._get_previous_node_specs()
        second_node_specs = self._get_previous_node_specs(
            target_index=shortcut_index)
        assert first_node_specs.channels == second_node_specs.channels
        channels = first_node_specs.channels
        inputs = [first_node_specs.name, second_node_specs.name]
        shortcut_node = helper.make_node(
            'Add',
            inputs=inputs,
            outputs=[layer_name],
            name=layer_name,
        )
        self._nodes.append(shortcut_node)
        return layer_name, channels

    def _make_route_node(self, layer_name, layer_dict):
        """If the 'layers' parameter from the DarkNet configuration is only one index, continue
        node creation at the indicated (negative) index. Otherwise, create an ONNX Concat node
        with the route properties from the DarkNet-based graph.

        Keyword arguments:
        layer_name -- the layer's name (also the corresponding key in layer_configs)
        layer_dict -- a layer parameter dictionary (one element of layer_configs)
        """
        route_node_indexes = layer_dict['layers']
        if len(route_node_indexes) == 1:
            split_index = route_node_indexes[0]
            assert split_index < 0
            # Increment by one because we skipped the YOLO layer:
            split_index += 1
            self.major_node_specs = self.major_node_specs[:split_index]
            layer_name = None
            channels = None
        else:
            inputs = list()
            channels = 0
            for index in route_node_indexes:
                if index > 0:
                    # Increment by one because we count the input as a node (DarkNet
                    # does not)
                    index += 1
                route_node_specs = self._get_previous_node_specs(
                    target_index=index)
                inputs.append(route_node_specs.name)
                channels += route_node_specs.channels
            assert inputs
            assert channels > 0

            route_node = helper.make_node(
                'Concat',
                axis=1,
                inputs=inputs,
                outputs=[layer_name],
                name=layer_name,
            )
            self._nodes.append(route_node)
        return layer_name, channels

    def _make_upsample_node(self, layer_name, layer_dict):
        """Create an ONNX Upsample node with the properties from
        the DarkNet-based graph.

        Keyword arguments:
        layer_name -- the layer's name (also the corresponding key in layer_configs)
        layer_dict -- a layer parameter dictionary (one element of layer_configs)
        """
        upsample_factor = float(layer_dict['stride'])
        # Create the scales array with node parameters
        scales=np.array([1.0, 1.0, upsample_factor, upsample_factor]).astype(np.float)
        previous_node_specs = self._get_previous_node_specs()
        inputs = [previous_node_specs.name]

        channels = previous_node_specs.channels
        assert channels > 0
        upsample_params = UpsampleParams(layer_name, scales)
        scales_name = upsample_params.generate_param_name()
        # For ONNX opset >= 9, the Upsample node takes the scales array as an input.
        inputs.append(scales_name)

        upsample_node = helper.make_node(
            'Upsample',
            mode='nearest',
            inputs=inputs,
            outputs=[layer_name],
            name=layer_name,
        )
        self._nodes.append(upsample_node)
        self.param_dict[layer_name] = upsample_params
        return layer_name, channels

    def _make_maxpool_node(self, layer_name, layer_dict):
        """Create an ONNX Maxpool node with the properties from
        the DarkNet-based graph.

        Keyword arguments:
        layer_name -- the layer's name (also the corresponding key in layer_configs)
        layer_dict -- a layer parameter dictionary (one element of layer_configs)
        """
        stride = layer_dict['stride']
        kernel_size = layer_dict['size']
        previous_node_specs = self._get_previous_node_specs()
        inputs = [previous_node_specs.name]
        channels = previous_node_specs.channels
        kernel_shape = [kernel_size, kernel_size]
        strides = [stride, stride]
        assert channels > 0
        maxpool_node = helper.make_node(
            'MaxPool',
            inputs=inputs,
            outputs=[layer_name],
            kernel_shape=kernel_shape,
            strides=strides,
            auto_pad='SAME_UPPER',
            name=layer_name,
        )
        self._nodes.append(maxpool_node)
        return layer_name, channels

def generate_md5_checksum(local_path):
    """Returns the MD5 checksum of a local file.

    Keyword argument:
    local_path -- path of the file whose checksum shall be generated
    """
    with open(local_path, 'rb') as local_file:
        data = local_file.read()
        return hashlib.md5(data).hexdigest()


def main():
    """Run the DarkNet-to-ONNX conversion for YOLOv3."""
    if sys.version_info[0] < 3:
        raise Exception('This modified version of yolov3_to_onnx.py script is only compatible with python3...')

    parser = argparse.ArgumentParser()
    parser.add_argument('--model', type=str, default='yolov3-416',
                        choices=['yolov3-288', 'yolov3-416', 'yolov3-608',
                                 'yolov3-tiny-288', 'yolov3-tiny-416'])
    args = parser.parse_args()

    cfg_file_path = '%s.cfg' % args.model
    weights_file_path = '%s.weights' % args.model
    output_file_path = '%s.onnx' % args.model
    yolo_dim = int(args.model.split('-')[-1])  # 288, 416 or 608

    # These are the only layers DarkNetParser will extract parameters from. The three layers of
    # type 'yolo' are not parsed in detail because they are included in the post-processing later:
    supported_layers = ['net', 'convolutional', 'maxpool',
                        'shortcut', 'route', 'upsample']

    # Create a DarkNetParser object, and the use it to generate an OrderedDict with all
    # layer's configs from the cfg file:
    parser = DarkNetParser(supported_layers)
    layer_configs = parser.parse_cfg_file(cfg_file_path)
    # We do not need the parser anymore after we got layer_configs:
    del parser

    # In above layer_config, there are three outputs that we need to know the output
    # shape of (in CHW format):
    output_tensor_dims = OrderedDict()
    d = yolo_dim
    if 'tiny' in args.model:
        output_tensor_dims['016_convolutional'] = [255, d // 32, d // 32]
        output_tensor_dims['023_convolutional'] = [255, d // 16, d // 16]
    else:
        output_tensor_dims['082_convolutional'] = [255, d // 32, d // 32]
        output_tensor_dims['094_convolutional'] = [255, d // 16, d // 16]
        output_tensor_dims['106_convolutional'] = [255, d //  8, d //  8]

    # Create a GraphBuilderONNX object with the known output tensor dimensions:
    builder = GraphBuilderONNX(args.model, output_tensor_dims)

    # Now generate an ONNX graph with weights from the previously parsed layer configurations
    # and the weights file:
    yolov3_model_def = builder.build_onnx_graph(
        layer_configs=layer_configs,
        weights_file_path=weights_file_path,
        verbose=True)
    # Once we have the model definition, we do not need the builder anymore:
    del builder

    # Perform a sanity check on the ONNX model definition:
    onnx.checker.check_model(yolov3_model_def)

    # Serialize the generated ONNX graph to this file:
    onnx.save(yolov3_model_def, output_file_path)


if __name__ == '__main__':
    main()

在这个py文件的文件夹里准备好yolov3-tiny的.weights和.cfg文件。终端输入：

python3 yolov3_tiny_to_onnx.py --model yolov3-416

同时这个py文件还支持yolov3-416、yolov3-288、yolov3-608、yolov3-tiny-288这些模型。

运行上述命令之后，会生成一个onnx的模型。

onnx—>trt

新建文件：onnx_to_tensorrt.py
添加如下代码：

from __future__ import print_function

import os
import argparse

import tensorrt as trt


EXPLICIT_BATCH = []
if trt.__version__[0] >= '7':
    EXPLICIT_BATCH.append(
        1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))


def build_engine(onnx_file_path, engine_file_path, verbose=False):
    """Takes an ONNX file and creates a TensorRT engine."""
    TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) if verbose else trt.Logger()
    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
        builder.fp16_mode = True
        #builder.strict_type_constraints = True

        # Parse model file
        if not os.path.exists(onnx_file_path):
            print('ONNX file {} not found, please run yolov3_to_onnx.py first to generate it.'.format(onnx_file_path))
            exit(0)
        print('Loading ONNX file from path {}...'.format(onnx_file_path))
        with open(onnx_file_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))
                return None
        if trt.__version__[0] >= '7':
            # The actual yolov3.onnx is generated with batch size 64.
            # Reshape input to batch size 1
            shape = list(network.get_input(0).shape)
            shape[0] = 1
            network.get_input(0).shape = shape
        print('Completed parsing of ONNX file')

        print('Building an engine; this may take a while...')
        engine = builder.build_cuda_engine(network)
        print('Completed creating engine')
        with open(engine_file_path, 'wb') as f:
            f.write(engine.serialize())
        return engine


def main():
    """Create a TensorRT engine for ONNX-based YOLOv3."""
    parser = argparse.ArgumentParser()
    parser.add_argument('-v', '--verbose', action='store_true',
                        help='enable verbose output (for debugging)')
    parser.add_argument('--model', type=str, default='yolov3-416',
                        choices=['yolov3-288', 'yolov3-416', 'yolov3-608',
                                 'yolov3-tiny-288', 'yolov3-tiny-416'])
    args = parser.parse_args()

    onnx_file_path = '%s.onnx' % args.model
    engine_file_path = '%s.trt' % args.model
    _ = build_engine(onnx_file_path, engine_file_path, args.verbose)


if __name__ == '__main__':
    main()

终端运行：

python3 onnx_to_tensorrt.py --model yolov3-416

稍等一会，执行完成后便会生成一个.trt文件。这个便是tensorRT要用到的模型。

运行

识别检测的代码跟寻常的目标检测代码差不多，这里给个tensorrt加载模型和和检测的提示：

class TrtYOLOv3(object):
    """TrtYOLOv3 class encapsulates things needed to run TRT YOLOv3."""

    def _load_engine(self):
        TRTbin = 'yolov3_onnx/%s.trt' % self.model
        with open(TRTbin, 'rb') as f, trt.Runtime(self.trt_logger) as runtime:
            return runtime.deserialize_cuda_engine(f.read())

    def _create_context(self):
        return self.engine.create_execution_context()

    def __init__(self, model, input_shape=(416, 416)):
        """Initialize TensorRT plugins, engine and conetxt."""
        self.model = model
        self.input_shape = input_shape
        h, w = input_shape
        if 'tiny' in model:
            self.output_shapes = [(1, 255, h // 32, w // 32),
                                  (1, 255, h // 16, w // 16)]
        else:
            self.output_shapes = [(1, 255, h // 32, w // 32),
                                  (1, 255, h // 16, w // 16),
                                  (1, 255, h //  8, w //  8)]
        if 'tiny' in model:
            postprocessor_args = {
                # A list of 2 three-dimensional tuples for the Tiny YOLO masks
                'yolo_masks': [(3, 4, 5), (0, 1, 2)],
                # A list of 6 two-dimensional tuples for the Tiny YOLO anchors
                'yolo_anchors': [(10, 14), (23, 27), (37, 58),
                                 (81, 82), (135, 169), (344, 319)],
                # Threshold for non-max suppression algorithm, float
                # value between 0 and 1
                'nms_threshold': 0.5,
                'yolo_input_resolution': input_shape
            }
        else:
            postprocessor_args = {
                # A list of 3 three-dimensional tuples for the YOLO masks
                'yolo_masks': [(6, 7, 8), (3, 4, 5), (0, 1, 2)],
                # A list of 9 two-dimensional tuples for the YOLO anchors
                'yolo_anchors': [(10, 13), (16, 30), (33, 23),
                                 (30, 61), (62, 45), (59, 119),
                                 (116, 90), (156, 198), (373, 326)],
                # Threshold for non-max suppression algorithm, float
                # value between 0 and 1
                # between 0 and 1
                'nms_threshold': 0.5,
                'yolo_input_resolution': input_shape
            }
        self.postprocessor = PostprocessYOLO(**postprocessor_args)

        self.trt_logger = trt.Logger(trt.Logger.INFO)
        self.engine = self._load_engine()
        self.context = self._create_context()
        self.inputs, self.outputs, self.bindings, self.stream = \
            allocate_buffers(self.engine)
        self.inference_fn = do_inference if trt.__version__[0] < '7' \
                                         else do_inference_v2

    def __del__(self):
        """Free CUDA memories."""
        del self.stream
        del self.outputs
        del self.inputs

    def detect(self, img, conf_th=0.3):
        """Detect objects in the input image."""
        shape_orig_WH = (img.shape[1], img.shape[0])
        img_resized = _preprocess_yolov3(img, self.input_shape)

        # Set host input to the image. The do_inference() function
        # will copy the input to the GPU before executing.
        self.inputs[0].host = np.ascontiguousarray(img_resized)
        trt_outputs = self.inference_fn(
            context=self.context,
            bindings=self.bindings,
            inputs=self.inputs,
            outputs=self.outputs,
            stream=self.stream)

        # Before doing post-processing, we need to reshape the outputs
        # as do_inference() will give us flat arrays.
        trt_outputs = [output.reshape(shape) for output, shape
                       in zip(trt_outputs, self.output_shapes)]

        # Run the post-processing algorithms on the TensorRT outputs
        # and get the bounding box details of detected objects
        boxes, classes, scores = self.postprocessor.process(
            trt_outputs, shape_orig_WH, conf_th)
        return boxes, scores, classes

运行结果如下：

虽然没有达到官方说的25fps但也可以了，肉眼看不出卡顿了，nano也不是很吃力。