create_onnx.py

from __future__ import print_function
from collections import OrderedDict
import hashlib
import os.path

# import wget

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

import sys
import argparse


class DarkNetParser(object):
	"""Definition of a parser for DarkNet-based YOLOv3-608 (only tested for this topology)."""

	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()
			# print(remainder)
			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, 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.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_dims = 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='YOLOv3-608',
			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['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.float32)
		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 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) as local_file:
		data = local_file.read()
		return hashlib.md5(data).hexdigest()


def download_file(local_path, link, checksum_reference=None):
	"""Checks if a local file is present and downloads it from the specified path otherwise.
	If checksum_reference is specified, the file's md5 checksum is compared against the
	expected value.

	Keyword arguments:
	local_path -- path of the file whose checksum shall be generated
	link -- link where the file shall be downloaded from if it is not found locally
	checksum_reference -- expected MD5 checksum of the file
	"""
	if not os.path.exists(local_path):
		print('Downloading from %s, this may take a while...' % link)
		wget.download(link, local_path)
		print()
	if checksum_reference is not None:
		checksum = generate_md5_checksum(local_path)
		if checksum != checksum_reference:
			raise ValueError(
				'The MD5 checksum of local file %s differs from %s, please manually remove \
				 the file and try again.' %
				(local_path, checksum_reference))
	return local_path

def arg_parse(): 
	parser = argparse.ArgumentParser(description='YOLO v3 Video Detection Module')

	parser.add_argument("--cfg", dest = 'cfgfile', help = "Config file",
						default = "cfg/yolov3.cfg", type = str)  ## yolov3.cfg  yolov3.weights  ## yolov3_cls_1.cfg 
	parser.add_argument("--weights", dest = 'weightsfile', help = "weightsfile",
						default = "weights/yolov3.weights", type = str)  ## yolov3_cls_1_200.weights   ## yolov3_cls_1_final.weights
	parser.add_argument("--reso", dest = 'reso', help = 
						"Input resolution of the network. Increase to increase accuracy. Decrease to increase speed",
						default = "416", type = int)
	return parser.parse_args()


def main():
	
	args = arg_parse()

	cfg_file_path = args.cfgfile
	weights_file_path = args.weightsfile 
	reso = args.reso

	if not os.path.exists(cfg_file_path) or not os.path.exists(weights_file_path) :
		print("Give Valid Data Paths for Config File and Weights File")
		exit()

	# 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', '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()

	output_tensor_dims['082_convolutional'] = [255, reso//32, reso//32]
	output_tensor_dims['094_convolutional'] = [255, reso//16, reso//16]
	output_tensor_dims['106_convolutional'] = [255, reso//8, reso//8]

	# Create a GraphBuilderONNX object with the known output tensor dimensions:
	builder = GraphBuilderONNX(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:
	output_file_path = 'yolov3.onnx'
	onnx.save(yolov3_model_def, output_file_path)

if __name__ == '__main__':
	main()