# yolo_to_onnx.py # # Copyright 1993-2019 NVIDIA Corporation. All rights reserved. # # NOTICE TO LICENSEE: # # This source code and/or documentation ("Licensed Deliverables") are # subject to NVIDIA intellectual property rights under U.S. and # international Copyright laws. # # These Licensed Deliverables contained herein is PROPRIETARY and # CONFIDENTIAL to NVIDIA and is being provided under the terms and # conditions of a form of NVIDIA software license agreement by and # between NVIDIA and Licensee ("License Agreement") or electronically # accepted by Licensee. Notwithstanding any terms or conditions to # the contrary in the License Agreement, reproduction or disclosure # of the Licensed Deliverables to any third party without the express # written consent of NVIDIA is prohibited. # # NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE # LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE # SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE. IT IS # PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND. # NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED # DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY, # NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE. # NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE # LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY # SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY # DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, # WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS # ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE # OF THESE LICENSED DELIVERABLES. # # U.S. Government End Users. These Licensed Deliverables are a # "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT # 1995), consisting of "commercial computer software" and "commercial # computer software documentation" as such terms are used in 48 # C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government # only as a commercial end item. Consistent with 48 C.F.R.12.212 and # 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all # U.S. Government End Users acquire the Licensed Deliverables with # only those rights set forth herein. # # Any use of the Licensed Deliverables in individual and commercial # software must include, in the user documentation and internal # comments to the code, the above Disclaimer and U.S. Government End # Users Notice. # 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 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: self.layer_configs = OrderedDict() self.supported_layers = supported_layers if supported_layers else \ ['net', 'convolutional', 'maxpool', 'shortcut', 'route', 'upsample', 'yolo'] self.layer_counter = 0 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: 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) 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] 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) # 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() 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_resize_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_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 print('Parsing DarkNet cfg file...') parser = DarkNetParser() layer_configs = parser.parse_cfg_file(cfg_file_path) category_num = get_category_num(cfg_file_path) output_tensor_names = get_output_convs(layer_configs) # e.g. ['036_convolutional', '044_convolutional', '052_convolutional'] c = (category_num + 5) * get_anchor_num(cfg_file_path) h, w = get_h_and_w(layer_configs) if len(output_tensor_names) == 2: output_tensor_shapes = [ [c, h // 32, w // 32], [c, h // 16, w // 16]] elif len(output_tensor_names) == 3: output_tensor_shapes = [ [c, h // 32, w // 32], [c, h // 16, w // 16], [c, h // 8, w // 8]] elif len(output_tensor_names) == 4: output_tensor_shapes = [ [c, h // 64, w // 64], [c, h // 32, w // 32], [c, h // 16, w // 16], [c, h // 8, w // 8]] if is_pan_arch(cfg_file_path): output_tensor_shapes.reverse() output_tensor_dims = OrderedDict( zip(output_tensor_names, output_tensor_shapes)) print('Building ONNX graph...') builder = GraphBuilderONNX( args.model, output_tensor_dims, MAX_BATCH_SIZE) yolo_model_def = builder.build_onnx_graph( layer_configs=layer_configs, weights_file_path=weights_file_path, verbose=True) print('Checking ONNX model...') onnx.checker.check_model(yolo_model_def) print('Saving ONNX file...') onnx.save(yolo_model_def, output_file_path) print('Done.') if __name__ == '__main__': main()