1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
# This file is a part of OpenCV project.
# It is a subject to the license terms in the LICENSE file found in the top-level directory
# of this distribution and at http://opencv.org/license.html.
#
# Copyright (C) 2018, Intel Corporation, all rights reserved.
# Third party copyrights are property of their respective owners.
#
# Use this script to get the text graph representation (.pbtxt) of SSD-based
# deep learning network trained in TensorFlow Object Detection API.
# Then you can import it with a binary frozen graph (.pb) using readNetFromTensorflow() function.
# See details and examples on the following wiki page: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API
import tensorflow as tf
import argparse
from math import sqrt
from tensorflow.core.framework.node_def_pb2 import NodeDef
from google.protobuf import text_format
parser = argparse.ArgumentParser(description='Run this script to get a text graph of '
'SSD model from TensorFlow Object Detection API. '
'Then pass it with .pb file to cv::dnn::readNetFromTensorflow function.')
parser.add_argument('--input', required=True, help='Path to frozen TensorFlow graph.')
parser.add_argument('--output', required=True, help='Path to output text graph.')
parser.add_argument('--num_classes', default=90, type=int, help='Number of trained classes.')
parser.add_argument('--min_scale', default=0.2, type=float, help='Hyper-parameter of ssd_anchor_generator from config file.')
parser.add_argument('--max_scale', default=0.95, type=float, help='Hyper-parameter of ssd_anchor_generator from config file.')
parser.add_argument('--num_layers', default=6, type=int, help='Hyper-parameter of ssd_anchor_generator from config file.')
parser.add_argument('--aspect_ratios', default=[1.0, 2.0, 0.5, 3.0, 0.333], type=float, nargs='+',
help='Hyper-parameter of ssd_anchor_generator from config file.')
args = parser.parse_args()
# Nodes that should be kept.
keepOps = ['Conv2D', 'BiasAdd', 'Add', 'Relu6', 'Placeholder', 'FusedBatchNorm',
'DepthwiseConv2dNative', 'ConcatV2', 'Mul', 'MaxPool', 'AvgPool']
# Nodes attributes that could be removed because they are not used during import.
unusedAttrs = ['T', 'data_format', 'Tshape', 'N', 'Tidx', 'Tdim', 'use_cudnn_on_gpu',
'Index', 'Tperm', 'is_training', 'Tpaddings']
# Node with which prefixes should be removed
prefixesToRemove = ('MultipleGridAnchorGenerator/', 'Postprocessor/', 'Preprocessor/')
# Read the graph.
with tf.gfile.FastGFile(args.input, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
def getUnconnectedNodes():
unconnected = []
for node in graph_def.node:
unconnected.append(node.name)
for inp in node.input:
if inp in unconnected:
unconnected.remove(inp)
return unconnected
removedNodes = []
# Detect unfused batch normalization nodes and fuse them.
def fuse_batch_normalization():
pattern = ['Add', 'Rsqrt', 'Mul', 'Mul', 'Mul', 'Sub', 'Add']
candidates = []
for node in graph_def.node:
if node.op == pattern[len(candidates)]:
candidates.append(node)
else:
candidates = []
if len(candidates) == len(pattern):
inp = candidates[3].input[0]
gamma = candidates[2].input[1]
beta = candidates[5].input[0]
moving_mean = candidates[4].input[0]
moving_variance = candidates[0].input[0]
name = node.name
node.Clear()
node.name = name
node.op = 'FusedBatchNorm'
node.input.append(inp)
node.input.append(gamma)
node.input.append(beta)
node.input.append(moving_mean)
node.input.append(moving_variance)
text_format.Merge('f: 0.001', node.attr["epsilon"])
for candidate in candidates[:-1]:
graph_def.node.remove(candidate)
candidates = []
fuse_batch_normalization()
# Removes Identity nodes
def removeIdentity():
identities = {}
for node in graph_def.node:
if node.op == 'Identity':
identities[node.name] = node.input[0]
for node in graph_def.node:
for i in range(len(node.input)):
if node.input[i] in identities:
node.input[i] = identities[node.input[i]]
removeIdentity()
# Remove extra nodes and attributes.
for i in reversed(range(len(graph_def.node))):
op = graph_def.node[i].op
name = graph_def.node[i].name
if (not op in keepOps) or name.startswith(prefixesToRemove):
if op != 'Const':
removedNodes.append(name)
del graph_def.node[i]
else:
for attr in unusedAttrs:
if attr in graph_def.node[i].attr:
del graph_def.node[i].attr[attr]
# Remove references to removed nodes except Const nodes.
for node in graph_def.node:
for i in reversed(range(len(node.input))):
if node.input[i] in removedNodes:
del node.input[i]
# Connect input node to the first layer
assert(graph_def.node[0].op == 'Placeholder')
# assert(graph_def.node[1].op == 'Conv2D')
weights = graph_def.node[1].input[0]
for i in range(len(graph_def.node[1].input)):
graph_def.node[1].input.pop()
graph_def.node[1].input.append(graph_def.node[0].name)
graph_def.node[1].input.append(weights)
# Create SSD postprocessing head ###############################################
# Concatenate predictions of classes, predictions of bounding boxes and proposals.
concatAxis = NodeDef()
concatAxis.name = 'concat/axis_flatten'
concatAxis.op = 'Const'
text_format.Merge(
'tensor {'
' dtype: DT_INT32'
' tensor_shape { }'
' int_val: -1'
'}', concatAxis.attr["value"])
graph_def.node.extend([concatAxis])
def addConcatNode(name, inputs):
concat = NodeDef()
concat.name = name
concat.op = 'ConcatV2'
for inp in inputs:
concat.input.append(inp)
concat.input.append(concatAxis.name)
graph_def.node.extend([concat])
for label in ['ClassPredictor', 'BoxEncodingPredictor']:
concatInputs = []
for i in range(args.num_layers):
# Flatten predictions
flatten = NodeDef()
inpName = 'BoxPredictor_%d/%s/BiasAdd' % (i, label)
flatten.input.append(inpName)
flatten.name = inpName + '/Flatten'
flatten.op = 'Flatten'
concatInputs.append(flatten.name)
graph_def.node.extend([flatten])
addConcatNode('%s/concat' % label, concatInputs)
# Add layers that generate anchors (bounding boxes proposals).
scales = [args.min_scale + (args.max_scale - args.min_scale) * i / (args.num_layers - 1)
for i in range(args.num_layers)] + [1.0]
def tensorMsg(values):
msg = 'tensor { dtype: DT_FLOAT tensor_shape { dim { size: %d } }' % len(values)
for value in values:
msg += 'float_val: %f ' % value
return msg + '}'
priorBoxes = []
for i in range(args.num_layers):
priorBox = NodeDef()
priorBox.name = 'PriorBox_%d' % i
priorBox.op = 'PriorBox'
priorBox.input.append('BoxPredictor_%d/BoxEncodingPredictor/BiasAdd' % i)
priorBox.input.append(graph_def.node[0].name) # image_tensor
text_format.Merge('b: false', priorBox.attr["flip"])
text_format.Merge('b: false', priorBox.attr["clip"])
text_format.Merge('b: true', priorBox.attr["normalized_bbox"])
if i == 0:
widths = [args.min_scale * 0.5, args.min_scale * sqrt(2.0), args.min_scale * sqrt(0.5)]
heights = [args.min_scale * 0.5, args.min_scale / sqrt(2.0), args.min_scale / sqrt(0.5)]
else:
widths = [scales[i] * sqrt(ar) for ar in args.aspect_ratios]
heights = [scales[i] / sqrt(ar) for ar in args.aspect_ratios]
widths += [sqrt(scales[i] * scales[i + 1])]
heights += [sqrt(scales[i] * scales[i + 1])]
text_format.Merge(tensorMsg(widths), priorBox.attr["width"])
text_format.Merge(tensorMsg(heights), priorBox.attr["height"])
text_format.Merge(tensorMsg([0.1, 0.1, 0.2, 0.2]), priorBox.attr["variance"])
graph_def.node.extend([priorBox])
priorBoxes.append(priorBox.name)
addConcatNode('PriorBox/concat', priorBoxes)
# Sigmoid for classes predictions and DetectionOutput layer
sigmoid = NodeDef()
sigmoid.name = 'ClassPredictor/concat/sigmoid'
sigmoid.op = 'Sigmoid'
sigmoid.input.append('ClassPredictor/concat')
graph_def.node.extend([sigmoid])
detectionOut = NodeDef()
detectionOut.name = 'detection_out'
detectionOut.op = 'DetectionOutput'
detectionOut.input.append('BoxEncodingPredictor/concat')
detectionOut.input.append(sigmoid.name)
detectionOut.input.append('PriorBox/concat')
text_format.Merge('i: %d' % (args.num_classes + 1), detectionOut.attr['num_classes'])
text_format.Merge('b: true', detectionOut.attr['share_location'])
text_format.Merge('i: 0', detectionOut.attr['background_label_id'])
text_format.Merge('f: 0.6', detectionOut.attr['nms_threshold'])
text_format.Merge('i: 100', detectionOut.attr['top_k'])
text_format.Merge('s: "CENTER_SIZE"', detectionOut.attr['code_type'])
text_format.Merge('i: 100', detectionOut.attr['keep_top_k'])
text_format.Merge('f: 0.01', detectionOut.attr['confidence_threshold'])
text_format.Merge('b: true', detectionOut.attr['loc_pred_transposed'])
graph_def.node.extend([detectionOut])
while True:
unconnectedNodes = getUnconnectedNodes()
unconnectedNodes.remove(detectionOut.name)
if not unconnectedNodes:
break
for name in unconnectedNodes:
for i in range(len(graph_def.node)):
if graph_def.node[i].name == name:
del graph_def.node[i]
break
# Save as text.
tf.train.write_graph(graph_def, "", args.output, as_text=True)