TensorRT-Demo/yolo/yolo_to_onnx.py

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# yolo_to_onnx.py
#
# Copyright 1993-2019 NVIDIA Corporation. All rights reserved.
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import os
import sys
import argparse
from collections import OrderedDict
import numpy as np
import onnx
from onnx import helper, TensorProto
MAX_BATCH_SIZE = 1
def parse_args():
"""Parse command-line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
'-c', '--category_num', type=int,
help='number of object categories (obsolete)')
parser.add_argument(
'-m', '--model', type=str, required=True,
help=('[yolov3-tiny|yolov3|yolov3-spp|yolov4-tiny|yolov4|'
'yolov4-csp|yolov4x-mish|yolov4-p5]-[{dimension}], where '
'{dimension} could be either a single number (e.g. '
'288, 416, 608) or 2 numbers, WxH (e.g. 416x256)'))
args = parser.parse_args()
return args
def rreplace(s, old, new, occurrence=1):
"""Replace old pattern in the string with new from the right."""
return new.join(s.rsplit(old, occurrence))
def is_pan_arch(cfg_file_path):
"""Determine whether the yolo model is with PAN architecture."""
with open(cfg_file_path, 'r') as f:
cfg_lines = [l.strip() for l in f.readlines()]
yolos_or_upsamples = [l for l in cfg_lines
if l in ['[yolo]', '[upsample]']]
yolo_count = len([l for l in yolos_or_upsamples if l == '[yolo]'])
upsample_count = len(yolos_or_upsamples) - yolo_count
assert yolo_count in (2, 3, 4) # at most 4 yolo layers
assert upsample_count == yolo_count - 1 or upsample_count == 0
# the model is with PAN if an upsample layer appears before the 1st yolo
return yolos_or_upsamples[0] == '[upsample]'
def get_output_convs(layer_configs):
"""Find output conv layer names from layer configs.
The output conv layers are those conv layers immediately proceeding
the yolo layers.
# Arguments
layer_configs: output of the DarkNetParser, i.e. a OrderedDict of
the yolo layers.
"""
output_convs = []
previous_layer = None
for current_layer in layer_configs.keys():
if previous_layer is not None and current_layer.endswith('yolo'):
assert previous_layer.endswith('convolutional')
activation = layer_configs[previous_layer]['activation']
if activation == 'linear':
output_convs.append(previous_layer)
elif activation == 'logistic':
output_convs.append(previous_layer + '_lgx')
else:
raise TypeError('unexpected activation: %s' % activation)
previous_layer = current_layer
return output_convs
# 从cfg中解析出分类数
# 返回值int 分类个数
def get_category_num(cfg_file_path):
"""Find number of output classes of the yolo model."""
with open(cfg_file_path, 'r') as f:
cfg_lines = [l.strip() for l in f.readlines()]
classes_lines = [l for l in cfg_lines if l.startswith('classes=')]
assert len(set(classes_lines)) == 1
return int(classes_lines[-1].split('=')[-1].strip())
def get_h_and_w(layer_configs):
"""Find input height and width of the yolo model from layer configs."""
net_config = layer_configs['000_net']
return net_config['height'], net_config['width']
def get_anchors(cfg_file_path):
"""Get anchors of all yolo layers from the cfg file."""
with open(cfg_file_path, 'r') as f:
cfg_lines = f.readlines()
yolo_lines = [l.strip() for l in cfg_lines if l.startswith('[yolo]')]
mask_lines = [l.strip() for l in cfg_lines if l.startswith('mask')]
anch_lines = [l.strip() for l in cfg_lines if l.startswith('anchors')]
assert len(mask_lines) == len(yolo_lines)
assert len(anch_lines) == len(yolo_lines)
anchor_list = eval('[%s]' % anch_lines[0].split('=')[-1])
mask_strs = [l.split('=')[-1] for l in mask_lines]
masks = [eval('[%s]' % s) for s in mask_strs]
anchors = []
for mask in masks:
curr_anchors = []
for m in mask:
curr_anchors.append(anchor_list[m * 2])
curr_anchors.append(anchor_list[m * 2 + 1])
anchors.append(curr_anchors)
return anchors
def get_anchor_num(cfg_file_path):
"""Find number of anchors (masks) of the yolo model."""
anchors = get_anchors(cfg_file_path)
num_anchors = [len(a) // 2 for a in anchors]
assert len(num_anchors) > 0, 'Found no `mask` fields in config'
assert len(set(num_anchors)) == 1, 'Found different num anchors'
return num_anchors[0]
class DarkNetParser(object):
"""Definition of a parser for DarkNet-based YOLO model."""
def __init__(self, supported_layers=None):
"""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 YOLO layers containing dictionaries with all layer
# parameters:
# OrderedDic 可以保持键值对的顺序[]
self.layer_configs = OrderedDict()
# 支持的节点类型
self.supported_layers = supported_layers if supported_layers else \
['net', # 超参数层,无操作
'convolutional', # 卷积层
'maxpool', # 池化层
'shortcut', # 捷径层
'route', # 路由层
'upsample', # 上采样层
'yolo'] # 输出层
self.layer_counter = 0
# 加载网络模型文件.cfg
def parse_cfg_file(self, cfg_file_path):
"""Takes the yolov?.cfg file and parses it layer by layer,
appending each layer's parameters as a dictionary to layer_configs.
Keyword argument:
cfg_file_path
"""
with open(cfg_file_path, 'r') as cfg_file:
remainder = cfg_file.read()
while remainder is not None:
# 从字符串中加载一层网络,并生成字典结构
layer_dict, layer_name, remainder = self._next_layer(remainder)
if layer_dict is not None:
# 将一层网络结构根据名称,依次加入有序字典中,生成整个网络结构
self.layer_configs[layer_name] = layer_dict
return self.layer_configs
# 返回当前层生成的字典键值对,并指向下一层结构
# layer_dict 一层内的网络结构字典, layer_name 层名称, remainder 剩余字符串
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)
while len(remainder[0]) > 0 and remainder[0][-1] == '#':
# '#[...' case (the left bracket is proceeded by a pound sign),
# assuming this layer is commented out, so go find the next '['
remainder = remainder[1].split('[', 1)
if len(remainder) == 2:
remainder = remainder[1]
else:
# no left bracket found in remainder
return None, None, None
remainder = remainder.split(']', 1)
if len(remainder) == 2:
layer_type, remainder = remainder
else:
# no right bracket
raise ValueError('no closing bracket!')
if layer_type not in self.supported_layers:
raise ValueError('%s layer not supported!' % layer_type)
out = remainder.split('\n[', 1)
if len(out) == 2:
layer_param_block, remainder = out[0], '[' + out[1]
else:
layer_param_block, remainder = out[0], ''
layer_param_lines = layer_param_block.split('\n')
# remove empty lines
layer_param_lines = [l.lstrip() for l in layer_param_lines if l.lstrip()]
# don't parse yolo layers
if layer_type == 'yolo': layer_param_lines = []
skip_params = ['steps', 'scales'] if layer_type == 'net' else []
layer_name = str(self.layer_counter).zfill(3) + '_' + layer_type
layer_dict = dict(type=layer_type)
for param_line in layer_param_lines:
param_line = param_line.split('#')[0]
if not param_line: continue
assert '[' not in param_line
param_type, param_value = self._parse_params(param_line, skip_params)
layer_dict[param_type] = param_value
self.layer_counter += 1
return layer_dict, layer_name, remainder
def _parse_params(self, param_line, skip_params=None):
"""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('=')
assert param_value_raw
param_value = None
if skip_params and param_type in skip_params:
param_type = None
elif 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 ResizeParams(object):
#Helper class to store the scale parameter for an Resize node.
def __init__(self, node_name, value):
"""Constructor based on the base node name (e.g. 86_Resize),
and the value of the scale input tensor.
Keyword arguments:
node_name -- base name of this YOLO Resize layer
value -- the value of the scale input to the Resize layer as numpy array
"""
self.node_name = node_name
self.value = value
def generate_param_name(self):
"""Generates the scale parameter name for the Resize node."""
param_name = self.node_name + '_' + "scale"
return param_name
def generate_roi_name(self):
"""Generates the roi input name for the Resize node."""
param_name = self.node_name + '_' + "roi"
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 YOLO .weights file.
Keyword argument:
weights_file_path -- path to the weights file.
"""
self.weights_file = self._open_weights_file(weights_file_path)
def load_resize_scales(self, resize_params):
"""Returns the initializers with the value of the scale input
tensor given by resize_params.
Keyword argument:
resize_params -- a ResizeParams object
"""
initializer = list()
inputs = list()
name = resize_params.generate_param_name()
shape = resize_params.value.shape
data = resize_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)
# In opset 11 an additional input named roi is required. Create a dummy tensor to satisfy this.
# It is a 1D tensor of size of the rank of the input (4)
rank = 4
roi_name = resize_params.generate_roi_name()
roi_input = helper.make_tensor_value_info(roi_name, TensorProto.FLOAT, [rank])
roi_init = helper.make_tensor(roi_name, TensorProto.FLOAT, [rank], [0,0,0,0])
initializer.append(roi_init)
inputs.append(roi_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 YOLO 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, batch_size):
"""Initialize with all DarkNet default parameters used creating
YOLO, 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 = batch_size
self.route_spec = 0 # keeping track of the current active 'route'
def build_onnx_graph(
self,
layer_configs,
weights_file_path,
verbose=True):
"""Iterate over all layer configs (parsed from the DarkNet
representation of YOLO), 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]
# 根据网络结构分别生成onnx节点节点的作用是操作符如conv,relu等每个操作都要写成Onnx格式
major_node_specs = self._make_onnx_node(layer_name, layer_dict)
# 成功生成后,添加到主网络结构中
if major_node_specs.name is not None:
self.major_node_specs.append(major_node_specs)
# remove dummy 'route' and 'yolo' nodes
self.major_node_specs = [node for node in self.major_node_specs
if 'dummy' not in node.name]
outputs = list()
# 遍历输出字典中的名称
for tensor_name in self.output_tensors.keys():
# 输出维度,例[batch,255,13,13]
output_dims = [self.batch_size, ] + \
self.output_tensors[tensor_name]
# 创建Onnx的"变量"ValueInfoProto
output_tensor = helper.make_tensor_value_info(
tensor_name, TensorProto.FLOAT, output_dims)
# 添加到输出列表中
outputs.append(output_tensor)
inputs = [self.input_tensor]
# 加载权重到ndarray中weight文件如何存储的按顺序存储的二进制文件
weight_loader = WeightLoader(weights_file_path)
initializer = list()
# If a layer has parameters, add them to the initializer and input lists.
# 大概是按照layer生成各层级的节点信息并保存权重darknet格式到节点中
# initializer是包含权重信息的input则是ValueInfoProto可以理解为变量
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_resize_scales(
params)
initializer.extend(initializer_layer)
inputs.extend(inputs_layer)
del weight_loader
# 生成onnx图
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_resize_node
node_creators['yolo'] = self._make_yolo_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:
raise TypeError('layer of type %s not supported' % layer_type)
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, [
self.batch_size, channels, height, width])
self.input_tensor = input_tensor
return layer_name, channels
def _get_previous_node_specs(self, target_index=0):
"""Get a previously ONNX node.
Target index can be passed for jumping to a specific index.
Keyword arguments:
target_index -- optional for jumping to a specific index,
default: 0 for the previous element, while
taking 'route' spec into account
"""
if target_index == 0:
if self.route_spec != 0:
previous_node = self.major_node_specs[self.route_spec]
assert 'dummy' not in previous_node.name
self.route_spec = 0
else:
previous_node = self.major_node_specs[-1]
else:
previous_node = self.major_node_specs[target_index]
assert previous_node.created_onnx_node
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 layer_dict.get('batch_normalize', 0) > 0:
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'] == 'mish':
layer_name_softplus = layer_name + '_softplus'
layer_name_tanh = layer_name + '_tanh'
layer_name_mish = layer_name + '_mish'
softplus_node = helper.make_node(
'Softplus',
inputs=inputs,
outputs=[layer_name_softplus],
name=layer_name_softplus
)
self._nodes.append(softplus_node)
tanh_node = helper.make_node(
'Tanh',
inputs=[layer_name_softplus],
outputs=[layer_name_tanh],
name=layer_name_tanh
)
self._nodes.append(tanh_node)
inputs.append(layer_name_tanh)
mish_node = helper.make_node(
'Mul',
inputs=inputs,
outputs=[layer_name_mish],
name=layer_name_mish
)
self._nodes.append(mish_node)
inputs = [layer_name_mish]
layer_name_output = layer_name_mish
elif layer_dict['activation'] == 'swish':
layer_name_sigmoid = layer_name + '_sigmoid'
layer_name_swish = layer_name + '_swish'
sigmoid_node = helper.make_node(
'Sigmoid',
inputs=inputs,
outputs=[layer_name_sigmoid],
name=layer_name_sigmoid
)
self._nodes.append(sigmoid_node)
inputs.append(layer_name_sigmoid)
swish_node = helper.make_node(
'Mul',
inputs=inputs,
outputs=[layer_name_swish],
name=layer_name_swish
)
self._nodes.append(swish_node)
inputs = [layer_name_swish]
layer_name_output = layer_name_swish
elif layer_dict['activation'] == 'logistic':
layer_name_lgx = layer_name + '_lgx'
lgx_node = helper.make_node(
'Sigmoid',
inputs=inputs,
outputs=[layer_name_lgx],
name=layer_name_lgx
)
self._nodes.append(lgx_node)
inputs = [layer_name_lgx]
layer_name_output = layer_name_lgx
elif layer_dict['activation'] == 'linear':
pass
else:
raise TypeError('%s activation not supported' % layer_dict['activation'])
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:
if 'groups' in layer_dict.keys():
# for CSPNet-kind of architecture
assert 'group_id' in layer_dict.keys()
groups = layer_dict['groups']
group_id = int(layer_dict['group_id'])
assert group_id < groups
index = route_node_indexes[0]
if index > 0:
# +1 for input node (same reason as below)
index += 1
route_node_specs = self._get_previous_node_specs(
target_index=index)
assert route_node_specs.channels % groups == 0
channels = route_node_specs.channels // groups
outputs = [layer_name + '_dummy%d' % i for i in range(groups)]
outputs[group_id] = layer_name
route_node = helper.make_node(
'Split',
axis=1,
#split=[channels] * groups, # not needed for opset 11
inputs=[route_node_specs.name],
outputs=outputs,
name=layer_name,
)
self._nodes.append(route_node)
else:
if route_node_indexes[0] < 0:
# route should skip self, thus -1
self.route_spec = route_node_indexes[0] - 1
elif route_node_indexes[0] > 0:
# +1 for input node (same reason as below)
self.route_spec = route_node_indexes[0] + 1
# This dummy route node would be removed in the end.
layer_name = layer_name + '_dummy'
channels = 1
else:
assert 'groups' not in layer_dict.keys(), \
'groups not implemented for multiple-input route layer!'
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_resize_node(self, layer_name, layer_dict):
"""Create an ONNX Resize 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)
"""
resize_scale_factors = float(layer_dict['stride'])
# Create the scale factor array with node parameters
scales=np.array([1.0, 1.0, resize_scale_factors, resize_scale_factors]).astype(np.float32)
previous_node_specs = self._get_previous_node_specs()
inputs = [previous_node_specs.name]
channels = previous_node_specs.channels
assert channels > 0
resize_params = ResizeParams(layer_name, scales)
# roi input is the second input, so append it before scales
roi_name = resize_params.generate_roi_name()
inputs.append(roi_name)
scales_name = resize_params.generate_param_name()
inputs.append(scales_name)
resize_node = helper.make_node(
'Resize',
coordinate_transformation_mode='asymmetric',
mode='nearest',
nearest_mode='floor',
inputs=inputs,
outputs=[layer_name],
name=layer_name,
)
self._nodes.append(resize_node)
self.param_dict[layer_name] = resize_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 _make_yolo_node(self, layer_name, layer_dict):
"""Create an ONNX Yolo node.
These are dummy nodes which would be removed in the end.
"""
channels = 1
return layer_name + '_dummy', channels
def main():
if sys.version_info[0] < 3:
raise SystemExit('ERROR: This modified version of yolov3_to_onnx.py '
'script is only compatible with python3...')
args = parse_args()
# 网络模型路径
cfg_file_path = '%s.cfg' % args.model
if not os.path.isfile(cfg_file_path):
raise SystemExit('ERROR: file (%s) not found!' % cfg_file_path)
# 权重模型路径
weights_file_path = '%s.weights' % args.model
if not os.path.isfile(weights_file_path):
raise SystemExit('ERROR: file (%s) not found!' % weights_file_path)
output_file_path = '%s.onnx' % args.model
# Darknet模型解释器(.cfg->.onnx格式)
print('Parsing DarkNet cfg file...')
parser = DarkNetParser()
# 从cfg文件中加载网络结构并按顺序存入字典中[layer_name,[key,value]]
layer_configs = parser.parse_cfg_file(cfg_file_path)
# 获取yolo输出分类个数单位int
category_num = get_category_num(cfg_file_path)
# 获取输出层名称从yolo层提取用于推算后获取结果
output_tensor_names = get_output_convs(layer_configs)
# e.g. ['036_convolutional', '044_convolutional', '052_convolutional']
# 获取输出维度 (80 + 5) * 3 = 255
c = (category_num + 5) * get_anchor_num(cfg_file_path)
# 获取输入图像宽高
h, w = get_h_and_w(layer_configs)
# 获取输出格式
if len(output_tensor_names) == 2:
# 2种候选框
output_tensor_shapes = [
[c, h // 32, w // 32], [c, h // 16, w // 16]]
elif len(output_tensor_names) == 3:
# 3种候选框
output_tensor_shapes = [
[c, h // 32, w // 32], [c, h // 16, w // 16],
[c, h // 8, w // 8]]
elif len(output_tensor_names) == 4:
# 4种候选框
output_tensor_shapes = [
[c, h // 64, w // 64], [c, h // 32, w // 32],
[c, h // 16, w // 16], [c, h // 8, w // 8]]
# 判断是金字塔模式(自下而上)还是倒金字塔模式(自上而下),决定了输出的顺序
if is_pan_arch(cfg_file_path):
# 输出从大图到小图,改为小图先输出
output_tensor_shapes.reverse()
# 生成输出字典格式,以416为例 [036_convolutional2551313],[044_convolutional,255,26,26],[052_convolutional,255,52,52]
output_tensor_dims = OrderedDict(
zip(output_tensor_names, output_tensor_shapes))
# 创建ONNX生成器
print('Building ONNX graph...')
builder = GraphBuilderONNX(
args.model, # Pytorch模型
output_tensor_dims, # 输出字典[[],[],[]]
MAX_BATCH_SIZE) # 最大Batch数量
# 编译ONNX模型
yolo_model_def = builder.build_onnx_graph(
layer_configs=layer_configs, # 网络层结构
weights_file_path=weights_file_path, # 网络权重
verbose=True) # 显示生成过程
# 检查生成的ONNX模型
print('Checking ONNX model...')
onnx.checker.check_model(yolo_model_def)
# 保存ONNX模型
print('Saving ONNX file...')
onnx.save(yolo_model_def, output_file_path)
print('Done.')
if __name__ == '__main__':
main()