├── Traffic.jpg
├── COCO_labels.txt
├── TestOpenCV_TensorFlow.cbp
├── LICENSE
├── README.md
├── MobileNetV1.cpp
├── ssd_mobilenet_v1_coco_2017_11_17.pbtxt
└── ssd_mobilenet_v2_coco_2018_03_29.pbtxt
/Traffic.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Qengineering/MobileNet_SSD_OpenCV_TensorFlow/HEAD/Traffic.jpg
--------------------------------------------------------------------------------
/COCO_labels.txt:
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1 | unlabeled
2 | person
3 | bicycle
4 | car
5 | motorcycle
6 | airplane
7 | bus
8 | train
9 | truck
10 | boat
11 | trafficlight
12 | firehydrant
13 | streetsign
14 | stopsign
15 | parkingmeter
16 | bench
17 | bird
18 | cat
19 | dog
20 | horse
21 | sheep
22 | cow
23 | elephant
24 | bear
25 | zebra
26 | giraffe
27 | hat
28 | backpack
29 | umbrella
30 | shoe
31 | eyeglasses
32 | handbag
33 | tie
34 | suitcase
35 | frisbee
36 | skis
37 | snowboard
38 | sportsball
39 | kite
40 | baseballbat
41 | baseballglove
42 | skateboard
43 | surfboard
44 | tennisracket
45 | bottle
46 | plate
47 | wineglass
48 | cup
49 | fork
50 | knife
51 | spoon
52 | bowl
53 | banana
54 | apple
55 | sandwich
56 | orange
57 | broccoli
58 | carrot
59 | hotdog
60 | pizza
61 | donut
62 | cake
63 | chair
64 | couch
65 | pottedplant
66 | bed
67 | mirror
68 | diningtable
69 | window
70 | desk
71 | toilet
72 | door
73 | tv
74 | laptop
75 | mouse
76 | remote
77 | keyboard
78 | cellphone
79 | microwave
80 | oven
81 | toaster
82 | sink
83 | refrigerator
84 | blender
85 | book
86 | clock
87 | vase
88 | scissors
89 | teddybear
90 | hairdrier
91 | toothbrush
--------------------------------------------------------------------------------
/TestOpenCV_TensorFlow.cbp:
--------------------------------------------------------------------------------
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/LICENSE:
--------------------------------------------------------------------------------
1 | BSD 3-Clause License
2 |
3 | Copyright (c) 2021, Q-engineering
4 | All rights reserved.
5 |
6 | Redistribution and use in source and binary forms, with or without
7 | modification, are permitted provided that the following conditions are met:
8 |
9 | 1. Redistributions of source code must retain the above copyright notice, this
10 | list of conditions and the following disclaimer.
11 |
12 | 2. Redistributions in binary form must reproduce the above copyright notice,
13 | this list of conditions and the following disclaimer in the documentation
14 | and/or other materials provided with the distribution.
15 |
16 | 3. Neither the name of the copyright holder nor the names of its
17 | contributors may be used to endorse or promote products derived from
18 | this software without specific prior written permission.
19 |
20 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
21 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
22 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
23 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
24 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
25 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
26 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
27 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
28 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
29 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
30 |
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/README.md:
--------------------------------------------------------------------------------
1 | # MobileNetV1/V2_SSD for the DNN modul of OpenCV
2 | 
3 | ## A example of OpenCV dnn framework working on a bare Raspberry Pi with TensorFlow models.
2 | #include
3 | #include
4 | #include
5 | #include
6 | #include
7 | #include
8 |
9 | using namespace cv;
10 | using namespace std;
11 |
12 | const size_t width = 300;
13 | const size_t height = 300;
14 |
15 | dnn::Net net;
16 | std::vector Names;
17 |
18 | static bool getFileContent(std::string fileName)
19 | {
20 |
21 | // Open the File
22 | std::ifstream in(fileName.c_str());
23 | // Check if object is valid
24 | if(!in.is_open()) return false;
25 |
26 | std::string str;
27 | // Read the next line from File untill it reaches the end.
28 | while (std::getline(in, str))
29 | {
30 | // Line contains string of length > 0 then save it in vector
31 | if(str.size()>0) Names.push_back(str);
32 | }
33 | // Close The File
34 | in.close();
35 | return true;
36 | }
37 |
38 | void detect_from_video(Mat &src)
39 | {
40 | Mat blobimg = dnn::blobFromImage(src, 1.0, Size(300, 300), 0.0, true);
41 |
42 | net.setInput(blobimg);
43 |
44 | Mat detection = net.forward("detection_out");
45 | // cout << detection.size[2]<<" "<< detection.size[3] << endl;
46 | Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr());
47 |
48 | const float confidence_threshold = 0.25;
49 | for(int i=0; i(i, 2);
51 |
52 | if(detect_confidence > confidence_threshold){
53 | size_t det_index = (size_t)detectionMat.at(i, 1);
54 | float x1 = detectionMat.at(i, 3)*src.cols;
55 | float y1 = detectionMat.at(i, 4)*src.rows;
56 | float x2 = detectionMat.at(i, 5)*src.cols;
57 | float y2 = detectionMat.at(i, 6)*src.rows;
58 | Rect rec((int)x1, (int)y1, (int)(x2 - x1), (int)(y2 - y1));
59 | rectangle(src,rec, Scalar(0, 0, 255), 1, 8, 0);
60 | putText(src, format("%s", Names[det_index].c_str()), Point(x1, y1-5) ,FONT_HERSHEY_SIMPLEX,0.5, Scalar(0, 0, 255), 1, 8, 0);
61 | }
62 | }
63 | }
64 |
65 | int main(int argc,char ** argv)
66 | {
67 | float f;
68 | float FPS[16];
69 | int i, Fcnt=0;
70 | Mat frame;
71 | chrono::steady_clock::time_point Tbegin, Tend;
72 |
73 | for(i=0;i<16;i++) FPS[i]=0.0;
74 |
75 | //MobileNetV1
76 | net = dnn::readNetFromTensorflow("frozen_inference_graph_V1.pb","ssd_mobilenet_v1_coco_2017_11_17.pbtxt");
77 | //MobileNetV2
78 | //net = dnn::readNetFromTensorflow("frozen_inference_graph_V2.pb","ssd_mobilenet_v2_coco_2018_03_29.pbtxt");
79 | if (net.empty()){
80 | cout << "init the model net error";
81 | exit(-1);
82 | }
83 |
84 | // Get the names
85 | bool result = getFileContent("COCO_labels.txt");
86 | if(!result)
87 | {
88 | cout << "loading labels failed";
89 | exit(-1);
90 | }
91 |
92 | //cout << "Switched to " << (cv::ocl::useOpenCL() ? "OpenCL enabled" : "CPU") << endl;
93 | //net.setPreferableTarget(DNN_TARGET_OPENCL);
94 |
95 | cout << "Start grabbing, press ESC on Live window to terminate" << endl;
96 | while(1){
97 | frame=imread("Traffic.jpg"); //need to refresh frame before dnn class detection
98 |
99 | Tbegin = chrono::steady_clock::now();
100 |
101 | detect_from_video(frame);
102 |
103 | Tend = chrono::steady_clock::now();
104 | //calculate frame rate
105 | f = chrono::duration_cast (Tend - Tbegin).count();
106 | if(f>0.0) FPS[((Fcnt++)&0x0F)]=1000.0/f;
107 | for(f=0.0, i=0;i<16;i++){ f+=FPS[i]; }
108 | putText(frame, format("FPS %0.2f", f/16),Point(10,20),FONT_HERSHEY_SIMPLEX,0.6, Scalar(0, 0, 255));
109 | //show output
110 | imshow("frame", frame);
111 |
112 | char esc = waitKey(5);
113 | if(esc == 27) break;
114 | }
115 |
116 | cout << "Closing the camera" << endl;
117 | destroyAllWindows();
118 | cout << "Bye!" << endl;
119 |
120 | return 0;
121 | }
122 |
--------------------------------------------------------------------------------
/ssd_mobilenet_v1_coco_2017_11_17.pbtxt:
--------------------------------------------------------------------------------
1 | node {
2 | name: "image_tensor"
3 | op: "Placeholder"
4 | attr {
5 | key: "dtype"
6 | value {
7 | type: DT_UINT8
8 | }
9 | }
10 | attr {
11 | key: "shape"
12 | value {
13 | shape {
14 | dim {
15 | size: -1
16 | }
17 | dim {
18 | size: -1
19 | }
20 | dim {
21 | size: -1
22 | }
23 | dim {
24 | size: 3
25 | }
26 | }
27 | }
28 | }
29 | }
30 | node {
31 | name: "Preprocessor/mul"
32 | op: "Mul"
33 | input: "image_tensor"
34 | input: "Preprocessor/mul/x"
35 | }
36 | node {
37 | name: "Preprocessor/sub"
38 | op: "Sub"
39 | input: "Preprocessor/mul"
40 | input: "Preprocessor/sub/y"
41 | }
42 | node {
43 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1"
44 | op: "Conv2D"
45 | input: "Preprocessor/sub"
46 | input: "FeatureExtractor/MobilenetV1/Conv2d_0/weights/read/_104__cf__107"
47 | input: "^FeatureExtractor/Assert/Assert"
48 | attr {
49 | key: "data_format"
50 | value {
51 | s: "NHWC"
52 | }
53 | }
54 | attr {
55 | key: "padding"
56 | value {
57 | s: "SAME"
58 | }
59 | }
60 | attr {
61 | key: "strides"
62 | value {
63 | list {
64 | i: 1
65 | i: 2
66 | i: 2
67 | i: 1
68 | }
69 | }
70 | }
71 | }
72 | node {
73 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/add_1"
74 | op: "Add"
75 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1"
76 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/sub/_103__cf__106"
77 | }
78 | node {
79 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6"
80 | op: "Relu6"
81 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/add_1"
82 | }
83 | node {
84 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise"
85 | op: "DepthwiseConv2dNative"
86 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6"
87 | input: "FeatureExtractor/MobilenetV1/Conv2d_1_depthwise/depthwise_weights/read/_101__cf__104"
88 | attr {
89 | key: "data_format"
90 | value {
91 | s: "NHWC"
92 | }
93 | }
94 | attr {
95 | key: "padding"
96 | value {
97 | s: "SAME"
98 | }
99 | }
100 | attr {
101 | key: "strides"
102 | value {
103 | list {
104 | i: 1
105 | i: 1
106 | i: 1
107 | i: 1
108 | }
109 | }
110 | }
111 | }
112 | node {
113 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/mul_1"
114 | op: "Mul"
115 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise"
116 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/mul/_99__cf__102"
117 | }
118 | node {
119 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/add_1"
120 | op: "Add"
121 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/mul_1"
122 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/sub/_100__cf__103"
123 | }
124 | node {
125 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6"
126 | op: "Relu6"
127 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/add_1"
128 | }
129 | node {
130 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/mul_1"
131 | op: "Conv2D"
132 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6"
133 | input: "FeatureExtractor/MobilenetV1/Conv2d_1_pointwise/weights/read/_98__cf__101"
134 | attr {
135 | key: "data_format"
136 | value {
137 | s: "NHWC"
138 | }
139 | }
140 | attr {
141 | key: "padding"
142 | value {
143 | s: "SAME"
144 | }
145 | }
146 | attr {
147 | key: "strides"
148 | value {
149 | list {
150 | i: 1
151 | i: 1
152 | i: 1
153 | i: 1
154 | }
155 | }
156 | }
157 | }
158 | node {
159 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/add_1"
160 | op: "Add"
161 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/mul_1"
162 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/sub/_97__cf__100"
163 | }
164 | node {
165 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6"
166 | op: "Relu6"
167 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/add_1"
168 | }
169 | node {
170 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/depthwise"
171 | op: "DepthwiseConv2dNative"
172 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6"
173 | input: "FeatureExtractor/MobilenetV1/Conv2d_2_depthwise/depthwise_weights/read/_95__cf__98"
174 | attr {
175 | key: "data_format"
176 | value {
177 | s: "NHWC"
178 | }
179 | }
180 | attr {
181 | key: "padding"
182 | value {
183 | s: "SAME"
184 | }
185 | }
186 | attr {
187 | key: "strides"
188 | value {
189 | list {
190 | i: 1
191 | i: 2
192 | i: 2
193 | i: 1
194 | }
195 | }
196 | }
197 | }
198 | node {
199 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/mul_1"
200 | op: "Mul"
201 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/depthwise"
202 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/mul/_93__cf__96"
203 | }
204 | node {
205 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/add_1"
206 | op: "Add"
207 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/mul_1"
208 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/sub/_94__cf__97"
209 | }
210 | node {
211 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6"
212 | op: "Relu6"
213 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/add_1"
214 | }
215 | node {
216 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/mul_1"
217 | op: "Conv2D"
218 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6"
219 | input: "FeatureExtractor/MobilenetV1/Conv2d_2_pointwise/weights/read/_92__cf__95"
220 | attr {
221 | key: "data_format"
222 | value {
223 | s: "NHWC"
224 | }
225 | }
226 | attr {
227 | key: "padding"
228 | value {
229 | s: "SAME"
230 | }
231 | }
232 | attr {
233 | key: "strides"
234 | value {
235 | list {
236 | i: 1
237 | i: 1
238 | i: 1
239 | i: 1
240 | }
241 | }
242 | }
243 | }
244 | node {
245 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/add_1"
246 | op: "Add"
247 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/mul_1"
248 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/sub/_91__cf__94"
249 | }
250 | node {
251 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6"
252 | op: "Relu6"
253 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/add_1"
254 | }
255 | node {
256 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/depthwise"
257 | op: "DepthwiseConv2dNative"
258 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6"
259 | input: "FeatureExtractor/MobilenetV1/Conv2d_3_depthwise/depthwise_weights/read/_89__cf__92"
260 | attr {
261 | key: "data_format"
262 | value {
263 | s: "NHWC"
264 | }
265 | }
266 | attr {
267 | key: "padding"
268 | value {
269 | s: "SAME"
270 | }
271 | }
272 | attr {
273 | key: "strides"
274 | value {
275 | list {
276 | i: 1
277 | i: 1
278 | i: 1
279 | i: 1
280 | }
281 | }
282 | }
283 | }
284 | node {
285 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm/batchnorm/mul_1"
286 | op: "Mul"
287 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/depthwise"
288 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm/batchnorm/mul/_87__cf__90"
289 | }
290 | node {
291 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm/batchnorm/add_1"
292 | op: "Add"
293 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm/batchnorm/mul_1"
294 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm/batchnorm/sub/_88__cf__91"
295 | }
296 | node {
297 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6"
298 | op: "Relu6"
299 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm/batchnorm/add_1"
300 | }
301 | node {
302 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/mul_1"
303 | op: "Conv2D"
304 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6"
305 | input: "FeatureExtractor/MobilenetV1/Conv2d_3_pointwise/weights/read/_86__cf__89"
306 | attr {
307 | key: "data_format"
308 | value {
309 | s: "NHWC"
310 | }
311 | }
312 | attr {
313 | key: "padding"
314 | value {
315 | s: "SAME"
316 | }
317 | }
318 | attr {
319 | key: "strides"
320 | value {
321 | list {
322 | i: 1
323 | i: 1
324 | i: 1
325 | i: 1
326 | }
327 | }
328 | }
329 | }
330 | node {
331 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/add_1"
332 | op: "Add"
333 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/mul_1"
334 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/sub/_85__cf__88"
335 | }
336 | node {
337 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6"
338 | op: "Relu6"
339 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/add_1"
340 | }
341 | node {
342 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/depthwise"
343 | op: "DepthwiseConv2dNative"
344 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6"
345 | input: "FeatureExtractor/MobilenetV1/Conv2d_4_depthwise/depthwise_weights/read/_83__cf__86"
346 | attr {
347 | key: "data_format"
348 | value {
349 | s: "NHWC"
350 | }
351 | }
352 | attr {
353 | key: "padding"
354 | value {
355 | s: "SAME"
356 | }
357 | }
358 | attr {
359 | key: "strides"
360 | value {
361 | list {
362 | i: 1
363 | i: 2
364 | i: 2
365 | i: 1
366 | }
367 | }
368 | }
369 | }
370 | node {
371 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/mul_1"
372 | op: "Mul"
373 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/depthwise"
374 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/mul/_81__cf__84"
375 | }
376 | node {
377 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/add_1"
378 | op: "Add"
379 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/mul_1"
380 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/sub/_82__cf__85"
381 | }
382 | node {
383 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6"
384 | op: "Relu6"
385 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/add_1"
386 | }
387 | node {
388 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/mul_1"
389 | op: "Conv2D"
390 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6"
391 | input: "FeatureExtractor/MobilenetV1/Conv2d_4_pointwise/weights/read/_80__cf__83"
392 | attr {
393 | key: "data_format"
394 | value {
395 | s: "NHWC"
396 | }
397 | }
398 | attr {
399 | key: "padding"
400 | value {
401 | s: "SAME"
402 | }
403 | }
404 | attr {
405 | key: "strides"
406 | value {
407 | list {
408 | i: 1
409 | i: 1
410 | i: 1
411 | i: 1
412 | }
413 | }
414 | }
415 | }
416 | node {
417 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/add_1"
418 | op: "Add"
419 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/mul_1"
420 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/sub/_79__cf__82"
421 | }
422 | node {
423 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6"
424 | op: "Relu6"
425 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/add_1"
426 | }
427 | node {
428 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/depthwise"
429 | op: "DepthwiseConv2dNative"
430 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6"
431 | input: "FeatureExtractor/MobilenetV1/Conv2d_5_depthwise/depthwise_weights/read/_77__cf__80"
432 | attr {
433 | key: "data_format"
434 | value {
435 | s: "NHWC"
436 | }
437 | }
438 | attr {
439 | key: "padding"
440 | value {
441 | s: "SAME"
442 | }
443 | }
444 | attr {
445 | key: "strides"
446 | value {
447 | list {
448 | i: 1
449 | i: 1
450 | i: 1
451 | i: 1
452 | }
453 | }
454 | }
455 | }
456 | node {
457 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/mul_1"
458 | op: "Mul"
459 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/depthwise"
460 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/mul/_75__cf__78"
461 | }
462 | node {
463 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/add_1"
464 | op: "Add"
465 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/mul_1"
466 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/sub/_76__cf__79"
467 | }
468 | node {
469 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6"
470 | op: "Relu6"
471 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/add_1"
472 | }
473 | node {
474 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/mul_1"
475 | op: "Conv2D"
476 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6"
477 | input: "FeatureExtractor/MobilenetV1/Conv2d_5_pointwise/weights/read/_74__cf__77"
478 | attr {
479 | key: "data_format"
480 | value {
481 | s: "NHWC"
482 | }
483 | }
484 | attr {
485 | key: "padding"
486 | value {
487 | s: "SAME"
488 | }
489 | }
490 | attr {
491 | key: "strides"
492 | value {
493 | list {
494 | i: 1
495 | i: 1
496 | i: 1
497 | i: 1
498 | }
499 | }
500 | }
501 | }
502 | node {
503 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/add_1"
504 | op: "Add"
505 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/mul_1"
506 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/sub/_73__cf__76"
507 | }
508 | node {
509 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6"
510 | op: "Relu6"
511 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/add_1"
512 | }
513 | node {
514 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/depthwise"
515 | op: "DepthwiseConv2dNative"
516 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6"
517 | input: "FeatureExtractor/MobilenetV1/Conv2d_6_depthwise/depthwise_weights/read/_71__cf__74"
518 | attr {
519 | key: "data_format"
520 | value {
521 | s: "NHWC"
522 | }
523 | }
524 | attr {
525 | key: "padding"
526 | value {
527 | s: "SAME"
528 | }
529 | }
530 | attr {
531 | key: "strides"
532 | value {
533 | list {
534 | i: 1
535 | i: 2
536 | i: 2
537 | i: 1
538 | }
539 | }
540 | }
541 | }
542 | node {
543 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/mul_1"
544 | op: "Mul"
545 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/depthwise"
546 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/mul/_69__cf__72"
547 | }
548 | node {
549 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/add_1"
550 | op: "Add"
551 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/mul_1"
552 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/sub/_70__cf__73"
553 | }
554 | node {
555 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6"
556 | op: "Relu6"
557 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/add_1"
558 | }
559 | node {
560 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/mul_1"
561 | op: "Conv2D"
562 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6"
563 | input: "FeatureExtractor/MobilenetV1/Conv2d_6_pointwise/weights/read/_68__cf__71"
564 | attr {
565 | key: "data_format"
566 | value {
567 | s: "NHWC"
568 | }
569 | }
570 | attr {
571 | key: "padding"
572 | value {
573 | s: "SAME"
574 | }
575 | }
576 | attr {
577 | key: "strides"
578 | value {
579 | list {
580 | i: 1
581 | i: 1
582 | i: 1
583 | i: 1
584 | }
585 | }
586 | }
587 | }
588 | node {
589 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/add_1"
590 | op: "Add"
591 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/mul_1"
592 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/sub/_67__cf__70"
593 | }
594 | node {
595 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6"
596 | op: "Relu6"
597 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/add_1"
598 | }
599 | node {
600 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/depthwise"
601 | op: "DepthwiseConv2dNative"
602 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6"
603 | input: "FeatureExtractor/MobilenetV1/Conv2d_7_depthwise/depthwise_weights/read/_65__cf__68"
604 | attr {
605 | key: "data_format"
606 | value {
607 | s: "NHWC"
608 | }
609 | }
610 | attr {
611 | key: "padding"
612 | value {
613 | s: "SAME"
614 | }
615 | }
616 | attr {
617 | key: "strides"
618 | value {
619 | list {
620 | i: 1
621 | i: 1
622 | i: 1
623 | i: 1
624 | }
625 | }
626 | }
627 | }
628 | node {
629 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/mul_1"
630 | op: "Mul"
631 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/depthwise"
632 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/mul/_63__cf__66"
633 | }
634 | node {
635 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/add_1"
636 | op: "Add"
637 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/mul_1"
638 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/sub/_64__cf__67"
639 | }
640 | node {
641 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6"
642 | op: "Relu6"
643 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/add_1"
644 | }
645 | node {
646 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/mul_1"
647 | op: "Conv2D"
648 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6"
649 | input: "FeatureExtractor/MobilenetV1/Conv2d_7_pointwise/weights/read/_62__cf__65"
650 | attr {
651 | key: "data_format"
652 | value {
653 | s: "NHWC"
654 | }
655 | }
656 | attr {
657 | key: "padding"
658 | value {
659 | s: "SAME"
660 | }
661 | }
662 | attr {
663 | key: "strides"
664 | value {
665 | list {
666 | i: 1
667 | i: 1
668 | i: 1
669 | i: 1
670 | }
671 | }
672 | }
673 | }
674 | node {
675 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/add_1"
676 | op: "Add"
677 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/mul_1"
678 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/sub/_61__cf__64"
679 | }
680 | node {
681 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6"
682 | op: "Relu6"
683 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/add_1"
684 | }
685 | node {
686 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/depthwise"
687 | op: "DepthwiseConv2dNative"
688 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6"
689 | input: "FeatureExtractor/MobilenetV1/Conv2d_8_depthwise/depthwise_weights/read/_59__cf__62"
690 | attr {
691 | key: "data_format"
692 | value {
693 | s: "NHWC"
694 | }
695 | }
696 | attr {
697 | key: "padding"
698 | value {
699 | s: "SAME"
700 | }
701 | }
702 | attr {
703 | key: "strides"
704 | value {
705 | list {
706 | i: 1
707 | i: 1
708 | i: 1
709 | i: 1
710 | }
711 | }
712 | }
713 | }
714 | node {
715 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/mul_1"
716 | op: "Mul"
717 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/depthwise"
718 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/mul/_57__cf__60"
719 | }
720 | node {
721 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/add_1"
722 | op: "Add"
723 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/mul_1"
724 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/sub/_58__cf__61"
725 | }
726 | node {
727 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6"
728 | op: "Relu6"
729 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/add_1"
730 | }
731 | node {
732 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/mul_1"
733 | op: "Conv2D"
734 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6"
735 | input: "FeatureExtractor/MobilenetV1/Conv2d_8_pointwise/weights/read/_56__cf__59"
736 | attr {
737 | key: "data_format"
738 | value {
739 | s: "NHWC"
740 | }
741 | }
742 | attr {
743 | key: "padding"
744 | value {
745 | s: "SAME"
746 | }
747 | }
748 | attr {
749 | key: "strides"
750 | value {
751 | list {
752 | i: 1
753 | i: 1
754 | i: 1
755 | i: 1
756 | }
757 | }
758 | }
759 | }
760 | node {
761 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/add_1"
762 | op: "Add"
763 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/mul_1"
764 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/sub/_55__cf__58"
765 | }
766 | node {
767 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6"
768 | op: "Relu6"
769 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/add_1"
770 | }
771 | node {
772 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/depthwise"
773 | op: "DepthwiseConv2dNative"
774 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6"
775 | input: "FeatureExtractor/MobilenetV1/Conv2d_9_depthwise/depthwise_weights/read/_53__cf__56"
776 | attr {
777 | key: "data_format"
778 | value {
779 | s: "NHWC"
780 | }
781 | }
782 | attr {
783 | key: "padding"
784 | value {
785 | s: "SAME"
786 | }
787 | }
788 | attr {
789 | key: "strides"
790 | value {
791 | list {
792 | i: 1
793 | i: 1
794 | i: 1
795 | i: 1
796 | }
797 | }
798 | }
799 | }
800 | node {
801 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/mul_1"
802 | op: "Mul"
803 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/depthwise"
804 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/mul/_51__cf__54"
805 | }
806 | node {
807 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/add_1"
808 | op: "Add"
809 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/mul_1"
810 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/sub/_52__cf__55"
811 | }
812 | node {
813 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6"
814 | op: "Relu6"
815 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/add_1"
816 | }
817 | node {
818 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/mul_1"
819 | op: "Conv2D"
820 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6"
821 | input: "FeatureExtractor/MobilenetV1/Conv2d_9_pointwise/weights/read/_50__cf__53"
822 | attr {
823 | key: "data_format"
824 | value {
825 | s: "NHWC"
826 | }
827 | }
828 | attr {
829 | key: "padding"
830 | value {
831 | s: "SAME"
832 | }
833 | }
834 | attr {
835 | key: "strides"
836 | value {
837 | list {
838 | i: 1
839 | i: 1
840 | i: 1
841 | i: 1
842 | }
843 | }
844 | }
845 | }
846 | node {
847 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/add_1"
848 | op: "Add"
849 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/mul_1"
850 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/sub/_49__cf__52"
851 | }
852 | node {
853 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6"
854 | op: "Relu6"
855 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/add_1"
856 | }
857 | node {
858 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise"
859 | op: "DepthwiseConv2dNative"
860 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6"
861 | input: "FeatureExtractor/MobilenetV1/Conv2d_10_depthwise/depthwise_weights/read/_47__cf__50"
862 | attr {
863 | key: "data_format"
864 | value {
865 | s: "NHWC"
866 | }
867 | }
868 | attr {
869 | key: "padding"
870 | value {
871 | s: "SAME"
872 | }
873 | }
874 | attr {
875 | key: "strides"
876 | value {
877 | list {
878 | i: 1
879 | i: 1
880 | i: 1
881 | i: 1
882 | }
883 | }
884 | }
885 | }
886 | node {
887 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/mul_1"
888 | op: "Mul"
889 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise"
890 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/mul/_45__cf__48"
891 | }
892 | node {
893 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/add_1"
894 | op: "Add"
895 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/mul_1"
896 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/sub/_46__cf__49"
897 | }
898 | node {
899 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6"
900 | op: "Relu6"
901 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/add_1"
902 | }
903 | node {
904 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/mul_1"
905 | op: "Conv2D"
906 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6"
907 | input: "FeatureExtractor/MobilenetV1/Conv2d_10_pointwise/weights/read/_44__cf__47"
908 | attr {
909 | key: "data_format"
910 | value {
911 | s: "NHWC"
912 | }
913 | }
914 | attr {
915 | key: "padding"
916 | value {
917 | s: "SAME"
918 | }
919 | }
920 | attr {
921 | key: "strides"
922 | value {
923 | list {
924 | i: 1
925 | i: 1
926 | i: 1
927 | i: 1
928 | }
929 | }
930 | }
931 | }
932 | node {
933 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/add_1"
934 | op: "Add"
935 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/mul_1"
936 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/sub/_43__cf__46"
937 | }
938 | node {
939 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6"
940 | op: "Relu6"
941 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/add_1"
942 | }
943 | node {
944 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/depthwise"
945 | op: "DepthwiseConv2dNative"
946 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6"
947 | input: "FeatureExtractor/MobilenetV1/Conv2d_11_depthwise/depthwise_weights/read/_41__cf__44"
948 | attr {
949 | key: "data_format"
950 | value {
951 | s: "NHWC"
952 | }
953 | }
954 | attr {
955 | key: "padding"
956 | value {
957 | s: "SAME"
958 | }
959 | }
960 | attr {
961 | key: "strides"
962 | value {
963 | list {
964 | i: 1
965 | i: 1
966 | i: 1
967 | i: 1
968 | }
969 | }
970 | }
971 | }
972 | node {
973 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm/batchnorm/mul_1"
974 | op: "Mul"
975 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/depthwise"
976 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm/batchnorm/mul/_39__cf__42"
977 | }
978 | node {
979 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm/batchnorm/add_1"
980 | op: "Add"
981 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm/batchnorm/mul_1"
982 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm/batchnorm/sub/_40__cf__43"
983 | }
984 | node {
985 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6"
986 | op: "Relu6"
987 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm/batchnorm/add_1"
988 | }
989 | node {
990 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/BatchNorm/batchnorm/mul_1"
991 | op: "Conv2D"
992 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6"
993 | input: "FeatureExtractor/MobilenetV1/Conv2d_11_pointwise/weights/read/_38__cf__41"
994 | attr {
995 | key: "data_format"
996 | value {
997 | s: "NHWC"
998 | }
999 | }
1000 | attr {
1001 | key: "padding"
1002 | value {
1003 | s: "SAME"
1004 | }
1005 | }
1006 | attr {
1007 | key: "strides"
1008 | value {
1009 | list {
1010 | i: 1
1011 | i: 1
1012 | i: 1
1013 | i: 1
1014 | }
1015 | }
1016 | }
1017 | }
1018 | node {
1019 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/BatchNorm/batchnorm/add_1"
1020 | op: "Add"
1021 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/BatchNorm/batchnorm/mul_1"
1022 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/BatchNorm/batchnorm/sub/_37__cf__40"
1023 | }
1024 | node {
1025 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6"
1026 | op: "Relu6"
1027 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/BatchNorm/batchnorm/add_1"
1028 | }
1029 | node {
1030 | name: "BoxPredictor_0/ClassPredictor/Conv2D"
1031 | op: "Conv2D"
1032 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6"
1033 | input: "BoxPredictor_0/ClassPredictor/weights/read/_178__cf__181"
1034 | attr {
1035 | key: "data_format"
1036 | value {
1037 | s: "NHWC"
1038 | }
1039 | }
1040 | attr {
1041 | key: "padding"
1042 | value {
1043 | s: "SAME"
1044 | }
1045 | }
1046 | attr {
1047 | key: "strides"
1048 | value {
1049 | list {
1050 | i: 1
1051 | i: 1
1052 | i: 1
1053 | i: 1
1054 | }
1055 | }
1056 | }
1057 | }
1058 | node {
1059 | name: "BoxPredictor_0/ClassPredictor/BiasAdd"
1060 | op: "BiasAdd"
1061 | input: "BoxPredictor_0/ClassPredictor/Conv2D"
1062 | input: "BoxPredictor_0/ClassPredictor/biases/read/_177__cf__180"
1063 | attr {
1064 | key: "data_format"
1065 | value {
1066 | s: "NHWC"
1067 | }
1068 | }
1069 | }
1070 | node {
1071 | name: "BoxPredictor_0/BoxEncodingPredictor/Conv2D"
1072 | op: "Conv2D"
1073 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6"
1074 | input: "BoxPredictor_0/BoxEncodingPredictor/weights/read/_116__cf__119"
1075 | attr {
1076 | key: "data_format"
1077 | value {
1078 | s: "NHWC"
1079 | }
1080 | }
1081 | attr {
1082 | key: "loc_pred_transposed"
1083 | value {
1084 | b: true
1085 | }
1086 | }
1087 | attr {
1088 | key: "padding"
1089 | value {
1090 | s: "SAME"
1091 | }
1092 | }
1093 | attr {
1094 | key: "strides"
1095 | value {
1096 | list {
1097 | i: 1
1098 | i: 1
1099 | i: 1
1100 | i: 1
1101 | }
1102 | }
1103 | }
1104 | }
1105 | node {
1106 | name: "BoxPredictor_0/BoxEncodingPredictor/BiasAdd"
1107 | op: "BiasAdd"
1108 | input: "BoxPredictor_0/BoxEncodingPredictor/Conv2D"
1109 | input: "BoxPredictor_0/BoxEncodingPredictor/biases/read/_115__cf__118"
1110 | attr {
1111 | key: "data_format"
1112 | value {
1113 | s: "NHWC"
1114 | }
1115 | }
1116 | }
1117 | node {
1118 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise"
1119 | op: "DepthwiseConv2dNative"
1120 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6"
1121 | input: "FeatureExtractor/MobilenetV1/Conv2d_12_depthwise/depthwise_weights/read/_35__cf__38"
1122 | attr {
1123 | key: "data_format"
1124 | value {
1125 | s: "NHWC"
1126 | }
1127 | }
1128 | attr {
1129 | key: "padding"
1130 | value {
1131 | s: "SAME"
1132 | }
1133 | }
1134 | attr {
1135 | key: "strides"
1136 | value {
1137 | list {
1138 | i: 1
1139 | i: 2
1140 | i: 2
1141 | i: 1
1142 | }
1143 | }
1144 | }
1145 | }
1146 | node {
1147 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/mul_1"
1148 | op: "Mul"
1149 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise"
1150 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/mul/_33__cf__36"
1151 | }
1152 | node {
1153 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/add_1"
1154 | op: "Add"
1155 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/mul_1"
1156 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/sub/_34__cf__37"
1157 | }
1158 | node {
1159 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6"
1160 | op: "Relu6"
1161 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/add_1"
1162 | }
1163 | node {
1164 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/mul_1"
1165 | op: "Conv2D"
1166 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6"
1167 | input: "FeatureExtractor/MobilenetV1/Conv2d_12_pointwise/weights/read/_32__cf__35"
1168 | attr {
1169 | key: "data_format"
1170 | value {
1171 | s: "NHWC"
1172 | }
1173 | }
1174 | attr {
1175 | key: "padding"
1176 | value {
1177 | s: "SAME"
1178 | }
1179 | }
1180 | attr {
1181 | key: "strides"
1182 | value {
1183 | list {
1184 | i: 1
1185 | i: 1
1186 | i: 1
1187 | i: 1
1188 | }
1189 | }
1190 | }
1191 | }
1192 | node {
1193 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/add_1"
1194 | op: "Add"
1195 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/mul_1"
1196 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/sub/_31__cf__34"
1197 | }
1198 | node {
1199 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6"
1200 | op: "Relu6"
1201 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/add_1"
1202 | }
1203 | node {
1204 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/depthwise"
1205 | op: "DepthwiseConv2dNative"
1206 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6"
1207 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_depthwise/depthwise_weights/read/_29__cf__32"
1208 | attr {
1209 | key: "data_format"
1210 | value {
1211 | s: "NHWC"
1212 | }
1213 | }
1214 | attr {
1215 | key: "padding"
1216 | value {
1217 | s: "SAME"
1218 | }
1219 | }
1220 | attr {
1221 | key: "strides"
1222 | value {
1223 | list {
1224 | i: 1
1225 | i: 1
1226 | i: 1
1227 | i: 1
1228 | }
1229 | }
1230 | }
1231 | }
1232 | node {
1233 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/mul_1"
1234 | op: "Mul"
1235 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/depthwise"
1236 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/mul/_27__cf__30"
1237 | }
1238 | node {
1239 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/add_1"
1240 | op: "Add"
1241 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/mul_1"
1242 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/sub/_28__cf__31"
1243 | }
1244 | node {
1245 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6"
1246 | op: "Relu6"
1247 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/add_1"
1248 | }
1249 | node {
1250 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/mul_1"
1251 | op: "Conv2D"
1252 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6"
1253 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise/weights/read/_26__cf__29"
1254 | attr {
1255 | key: "data_format"
1256 | value {
1257 | s: "NHWC"
1258 | }
1259 | }
1260 | attr {
1261 | key: "padding"
1262 | value {
1263 | s: "SAME"
1264 | }
1265 | }
1266 | attr {
1267 | key: "strides"
1268 | value {
1269 | list {
1270 | i: 1
1271 | i: 1
1272 | i: 1
1273 | i: 1
1274 | }
1275 | }
1276 | }
1277 | }
1278 | node {
1279 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/add_1"
1280 | op: "Add"
1281 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/mul_1"
1282 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/sub/_25__cf__28"
1283 | }
1284 | node {
1285 | name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6"
1286 | op: "Relu6"
1287 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/add_1"
1288 | }
1289 | node {
1290 | name: "BoxPredictor_1/ClassPredictor/Conv2D"
1291 | op: "Conv2D"
1292 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6"
1293 | input: "BoxPredictor_1/ClassPredictor/weights/read/_176__cf__179"
1294 | attr {
1295 | key: "data_format"
1296 | value {
1297 | s: "NHWC"
1298 | }
1299 | }
1300 | attr {
1301 | key: "padding"
1302 | value {
1303 | s: "SAME"
1304 | }
1305 | }
1306 | attr {
1307 | key: "strides"
1308 | value {
1309 | list {
1310 | i: 1
1311 | i: 1
1312 | i: 1
1313 | i: 1
1314 | }
1315 | }
1316 | }
1317 | }
1318 | node {
1319 | name: "BoxPredictor_1/ClassPredictor/BiasAdd"
1320 | op: "BiasAdd"
1321 | input: "BoxPredictor_1/ClassPredictor/Conv2D"
1322 | input: "BoxPredictor_1/ClassPredictor/biases/read/_175__cf__178"
1323 | attr {
1324 | key: "data_format"
1325 | value {
1326 | s: "NHWC"
1327 | }
1328 | }
1329 | }
1330 | node {
1331 | name: "BoxPredictor_1/BoxEncodingPredictor/Conv2D"
1332 | op: "Conv2D"
1333 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6"
1334 | input: "BoxPredictor_1/BoxEncodingPredictor/weights/read/_114__cf__117"
1335 | attr {
1336 | key: "data_format"
1337 | value {
1338 | s: "NHWC"
1339 | }
1340 | }
1341 | attr {
1342 | key: "loc_pred_transposed"
1343 | value {
1344 | b: true
1345 | }
1346 | }
1347 | attr {
1348 | key: "padding"
1349 | value {
1350 | s: "SAME"
1351 | }
1352 | }
1353 | attr {
1354 | key: "strides"
1355 | value {
1356 | list {
1357 | i: 1
1358 | i: 1
1359 | i: 1
1360 | i: 1
1361 | }
1362 | }
1363 | }
1364 | }
1365 | node {
1366 | name: "BoxPredictor_1/BoxEncodingPredictor/BiasAdd"
1367 | op: "BiasAdd"
1368 | input: "BoxPredictor_1/BoxEncodingPredictor/Conv2D"
1369 | input: "BoxPredictor_1/BoxEncodingPredictor/biases/read/_113__cf__116"
1370 | attr {
1371 | key: "data_format"
1372 | value {
1373 | s: "NHWC"
1374 | }
1375 | }
1376 | }
1377 | node {
1378 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/mul_1"
1379 | op: "Conv2D"
1380 | input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6"
1381 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/weights/read/_23__cf__26"
1382 | attr {
1383 | key: "data_format"
1384 | value {
1385 | s: "NHWC"
1386 | }
1387 | }
1388 | attr {
1389 | key: "padding"
1390 | value {
1391 | s: "SAME"
1392 | }
1393 | }
1394 | attr {
1395 | key: "strides"
1396 | value {
1397 | list {
1398 | i: 1
1399 | i: 1
1400 | i: 1
1401 | i: 1
1402 | }
1403 | }
1404 | }
1405 | }
1406 | node {
1407 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/add_1"
1408 | op: "Add"
1409 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/mul_1"
1410 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/sub/_22__cf__25"
1411 | }
1412 | node {
1413 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/Relu6"
1414 | op: "Relu6"
1415 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/add_1"
1416 | }
1417 | node {
1418 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/mul_1"
1419 | op: "Conv2D"
1420 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/Relu6"
1421 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/weights/read/_20__cf__23"
1422 | attr {
1423 | key: "data_format"
1424 | value {
1425 | s: "NHWC"
1426 | }
1427 | }
1428 | attr {
1429 | key: "padding"
1430 | value {
1431 | s: "SAME"
1432 | }
1433 | }
1434 | attr {
1435 | key: "strides"
1436 | value {
1437 | list {
1438 | i: 1
1439 | i: 2
1440 | i: 2
1441 | i: 1
1442 | }
1443 | }
1444 | }
1445 | }
1446 | node {
1447 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/add_1"
1448 | op: "Add"
1449 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/mul_1"
1450 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/sub/_19__cf__22"
1451 | }
1452 | node {
1453 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/Relu6"
1454 | op: "Relu6"
1455 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/add_1"
1456 | }
1457 | node {
1458 | name: "BoxPredictor_2/ClassPredictor/Conv2D"
1459 | op: "Conv2D"
1460 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/Relu6"
1461 | input: "BoxPredictor_2/ClassPredictor/weights/read/_174__cf__177"
1462 | attr {
1463 | key: "data_format"
1464 | value {
1465 | s: "NHWC"
1466 | }
1467 | }
1468 | attr {
1469 | key: "padding"
1470 | value {
1471 | s: "SAME"
1472 | }
1473 | }
1474 | attr {
1475 | key: "strides"
1476 | value {
1477 | list {
1478 | i: 1
1479 | i: 1
1480 | i: 1
1481 | i: 1
1482 | }
1483 | }
1484 | }
1485 | }
1486 | node {
1487 | name: "BoxPredictor_2/ClassPredictor/BiasAdd"
1488 | op: "BiasAdd"
1489 | input: "BoxPredictor_2/ClassPredictor/Conv2D"
1490 | input: "BoxPredictor_2/ClassPredictor/biases/read/_173__cf__176"
1491 | attr {
1492 | key: "data_format"
1493 | value {
1494 | s: "NHWC"
1495 | }
1496 | }
1497 | }
1498 | node {
1499 | name: "BoxPredictor_2/BoxEncodingPredictor/Conv2D"
1500 | op: "Conv2D"
1501 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/Relu6"
1502 | input: "BoxPredictor_2/BoxEncodingPredictor/weights/read/_112__cf__115"
1503 | attr {
1504 | key: "data_format"
1505 | value {
1506 | s: "NHWC"
1507 | }
1508 | }
1509 | attr {
1510 | key: "loc_pred_transposed"
1511 | value {
1512 | b: true
1513 | }
1514 | }
1515 | attr {
1516 | key: "padding"
1517 | value {
1518 | s: "SAME"
1519 | }
1520 | }
1521 | attr {
1522 | key: "strides"
1523 | value {
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1525 | i: 1
1526 | i: 1
1527 | i: 1
1528 | i: 1
1529 | }
1530 | }
1531 | }
1532 | }
1533 | node {
1534 | name: "BoxPredictor_2/BoxEncodingPredictor/BiasAdd"
1535 | op: "BiasAdd"
1536 | input: "BoxPredictor_2/BoxEncodingPredictor/Conv2D"
1537 | input: "BoxPredictor_2/BoxEncodingPredictor/biases/read/_111__cf__114"
1538 | attr {
1539 | key: "data_format"
1540 | value {
1541 | s: "NHWC"
1542 | }
1543 | }
1544 | }
1545 | node {
1546 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/mul_1"
1547 | op: "Conv2D"
1548 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/Relu6"
1549 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/weights/read/_17__cf__20"
1550 | attr {
1551 | key: "data_format"
1552 | value {
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1555 | }
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1558 | value {
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1560 | }
1561 | }
1562 | attr {
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1566 | i: 1
1567 | i: 1
1568 | i: 1
1569 | i: 1
1570 | }
1571 | }
1572 | }
1573 | }
1574 | node {
1575 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/add_1"
1576 | op: "Add"
1577 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/mul_1"
1578 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/sub/_16__cf__19"
1579 | }
1580 | node {
1581 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/Relu6"
1582 | op: "Relu6"
1583 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/add_1"
1584 | }
1585 | node {
1586 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/BatchNorm/batchnorm/mul_1"
1587 | op: "Conv2D"
1588 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/Relu6"
1589 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/weights/read/_14__cf__17"
1590 | attr {
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1600 | }
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1607 | i: 2
1608 | i: 2
1609 | i: 1
1610 | }
1611 | }
1612 | }
1613 | }
1614 | node {
1615 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/BatchNorm/batchnorm/add_1"
1616 | op: "Add"
1617 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/BatchNorm/batchnorm/mul_1"
1618 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/BatchNorm/batchnorm/sub/_13__cf__16"
1619 | }
1620 | node {
1621 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/Relu6"
1622 | op: "Relu6"
1623 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/BatchNorm/batchnorm/add_1"
1624 | }
1625 | node {
1626 | name: "BoxPredictor_3/ClassPredictor/Conv2D"
1627 | op: "Conv2D"
1628 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/Relu6"
1629 | input: "BoxPredictor_3/ClassPredictor/weights/read/_172__cf__175"
1630 | attr {
1631 | key: "data_format"
1632 | value {
1633 | s: "NHWC"
1634 | }
1635 | }
1636 | attr {
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1638 | value {
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1640 | }
1641 | }
1642 | attr {
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1646 | i: 1
1647 | i: 1
1648 | i: 1
1649 | i: 1
1650 | }
1651 | }
1652 | }
1653 | }
1654 | node {
1655 | name: "BoxPredictor_3/ClassPredictor/BiasAdd"
1656 | op: "BiasAdd"
1657 | input: "BoxPredictor_3/ClassPredictor/Conv2D"
1658 | input: "BoxPredictor_3/ClassPredictor/biases/read/_171__cf__174"
1659 | attr {
1660 | key: "data_format"
1661 | value {
1662 | s: "NHWC"
1663 | }
1664 | }
1665 | }
1666 | node {
1667 | name: "BoxPredictor_3/BoxEncodingPredictor/Conv2D"
1668 | op: "Conv2D"
1669 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/Relu6"
1670 | input: "BoxPredictor_3/BoxEncodingPredictor/weights/read/_110__cf__113"
1671 | attr {
1672 | key: "data_format"
1673 | value {
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1675 | }
1676 | }
1677 | attr {
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1679 | value {
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1681 | }
1682 | }
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1687 | }
1688 | }
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1693 | i: 1
1694 | i: 1
1695 | i: 1
1696 | i: 1
1697 | }
1698 | }
1699 | }
1700 | }
1701 | node {
1702 | name: "BoxPredictor_3/BoxEncodingPredictor/BiasAdd"
1703 | op: "BiasAdd"
1704 | input: "BoxPredictor_3/BoxEncodingPredictor/Conv2D"
1705 | input: "BoxPredictor_3/BoxEncodingPredictor/biases/read/_109__cf__112"
1706 | attr {
1707 | key: "data_format"
1708 | value {
1709 | s: "NHWC"
1710 | }
1711 | }
1712 | }
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1715 | op: "Conv2D"
1716 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/Relu6"
1717 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/weights/read/_11__cf__14"
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1720 | value {
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1735 | i: 1
1736 | i: 1
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1740 | }
1741 | }
1742 | node {
1743 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/BatchNorm/batchnorm/add_1"
1744 | op: "Add"
1745 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/BatchNorm/batchnorm/mul_1"
1746 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/BatchNorm/batchnorm/sub/_10__cf__13"
1747 | }
1748 | node {
1749 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/Relu6"
1750 | op: "Relu6"
1751 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/BatchNorm/batchnorm/add_1"
1752 | }
1753 | node {
1754 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/BatchNorm/batchnorm/mul_1"
1755 | op: "Conv2D"
1756 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/Relu6"
1757 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/weights/read/_8__cf__11"
1758 | attr {
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1775 | i: 2
1776 | i: 2
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1779 | }
1780 | }
1781 | }
1782 | node {
1783 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/BatchNorm/batchnorm/add_1"
1784 | op: "Add"
1785 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/BatchNorm/batchnorm/mul_1"
1786 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/BatchNorm/batchnorm/sub/_7__cf__10"
1787 | }
1788 | node {
1789 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/Relu6"
1790 | op: "Relu6"
1791 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/BatchNorm/batchnorm/add_1"
1792 | }
1793 | node {
1794 | name: "BoxPredictor_4/ClassPredictor/Conv2D"
1795 | op: "Conv2D"
1796 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/Relu6"
1797 | input: "BoxPredictor_4/ClassPredictor/weights/read/_170__cf__173"
1798 | attr {
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1800 | value {
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1802 | }
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1809 | }
1810 | attr {
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1815 | i: 1
1816 | i: 1
1817 | i: 1
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1819 | }
1820 | }
1821 | }
1822 | node {
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1824 | op: "BiasAdd"
1825 | input: "BoxPredictor_4/ClassPredictor/Conv2D"
1826 | input: "BoxPredictor_4/ClassPredictor/biases/read/_169__cf__172"
1827 | attr {
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1829 | value {
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1831 | }
1832 | }
1833 | }
1834 | node {
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1836 | op: "Conv2D"
1837 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/Relu6"
1838 | input: "BoxPredictor_4/BoxEncodingPredictor/weights/read/_108__cf__111"
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1841 | value {
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1873 | input: "BoxPredictor_4/BoxEncodingPredictor/biases/read/_107__cf__110"
1874 | attr {
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1876 | value {
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1884 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/Relu6"
1885 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/weights/read/_5__cf__8"
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1911 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/BatchNorm/batchnorm/add_1"
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1913 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/BatchNorm/batchnorm/mul_1"
1914 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/BatchNorm/batchnorm/sub/_4__cf__7"
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1916 | node {
1917 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/Relu6"
1918 | op: "Relu6"
1919 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/BatchNorm/batchnorm/add_1"
1920 | }
1921 | node {
1922 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/BatchNorm/batchnorm/mul_1"
1923 | op: "Conv2D"
1924 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/Relu6"
1925 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/weights/read/_2__cf__5"
1926 | attr {
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1930 | }
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1932 | attr {
1933 | key: "padding"
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1937 | }
1938 | attr {
1939 | key: "strides"
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1942 | i: 1
1943 | i: 2
1944 | i: 2
1945 | i: 1
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1948 | }
1949 | }
1950 | node {
1951 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/BatchNorm/batchnorm/add_1"
1952 | op: "Add"
1953 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/BatchNorm/batchnorm/mul_1"
1954 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/BatchNorm/batchnorm/sub/_1__cf__4"
1955 | }
1956 | node {
1957 | name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/Relu6"
1958 | op: "Relu6"
1959 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/BatchNorm/batchnorm/add_1"
1960 | }
1961 | node {
1962 | name: "BoxPredictor_5/ClassPredictor/Conv2D"
1963 | op: "Conv2D"
1964 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/Relu6"
1965 | input: "BoxPredictor_5/ClassPredictor/weights/read/_168__cf__171"
1966 | attr {
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1977 | }
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1983 | i: 1
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1985 | i: 1
1986 | }
1987 | }
1988 | }
1989 | }
1990 | node {
1991 | name: "BoxPredictor_5/ClassPredictor/BiasAdd"
1992 | op: "BiasAdd"
1993 | input: "BoxPredictor_5/ClassPredictor/Conv2D"
1994 | input: "BoxPredictor_5/ClassPredictor/biases/read/_167__cf__170"
1995 | attr {
1996 | key: "data_format"
1997 | value {
1998 | s: "NHWC"
1999 | }
2000 | }
2001 | }
2002 | node {
2003 | name: "BoxPredictor_5/BoxEncodingPredictor/Conv2D"
2004 | op: "Conv2D"
2005 | input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/Relu6"
2006 | input: "BoxPredictor_5/BoxEncodingPredictor/weights/read/_106__cf__109"
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2011 | }
2012 | }
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2015 | value {
2016 | b: true
2017 | }
2018 | }
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2020 | key: "padding"
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2030 | i: 1
2031 | i: 1
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2034 | }
2035 | }
2036 | }
2037 | node {
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2039 | op: "BiasAdd"
2040 | input: "BoxPredictor_5/BoxEncodingPredictor/Conv2D"
2041 | input: "BoxPredictor_5/BoxEncodingPredictor/biases/read/_105__cf__108"
2042 | attr {
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2044 | value {
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2046 | }
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2067 | node {
2068 | name: "BoxPredictor_0/ClassPredictor/BiasAdd/Flatten"
2069 | op: "Flatten"
2070 | input: "BoxPredictor_0/ClassPredictor/BiasAdd"
2071 | }
2072 | node {
2073 | name: "BoxPredictor_1/ClassPredictor/BiasAdd/Flatten"
2074 | op: "Flatten"
2075 | input: "BoxPredictor_1/ClassPredictor/BiasAdd"
2076 | }
2077 | node {
2078 | name: "BoxPredictor_2/ClassPredictor/BiasAdd/Flatten"
2079 | op: "Flatten"
2080 | input: "BoxPredictor_2/ClassPredictor/BiasAdd"
2081 | }
2082 | node {
2083 | name: "BoxPredictor_3/ClassPredictor/BiasAdd/Flatten"
2084 | op: "Flatten"
2085 | input: "BoxPredictor_3/ClassPredictor/BiasAdd"
2086 | }
2087 | node {
2088 | name: "BoxPredictor_4/ClassPredictor/BiasAdd/Flatten"
2089 | op: "Flatten"
2090 | input: "BoxPredictor_4/ClassPredictor/BiasAdd"
2091 | }
2092 | node {
2093 | name: "BoxPredictor_5/ClassPredictor/BiasAdd/Flatten"
2094 | op: "Flatten"
2095 | input: "BoxPredictor_5/ClassPredictor/BiasAdd"
2096 | }
2097 | node {
2098 | name: "ClassPredictor/concat"
2099 | op: "ConcatV2"
2100 | input: "BoxPredictor_0/ClassPredictor/BiasAdd/Flatten"
2101 | input: "BoxPredictor_1/ClassPredictor/BiasAdd/Flatten"
2102 | input: "BoxPredictor_2/ClassPredictor/BiasAdd/Flatten"
2103 | input: "BoxPredictor_3/ClassPredictor/BiasAdd/Flatten"
2104 | input: "BoxPredictor_4/ClassPredictor/BiasAdd/Flatten"
2105 | input: "BoxPredictor_5/ClassPredictor/BiasAdd/Flatten"
2106 | input: "concat/axis_flatten"
2107 | }
2108 | node {
2109 | name: "BoxPredictor_0/BoxEncodingPredictor/BiasAdd/Flatten"
2110 | op: "Flatten"
2111 | input: "BoxPredictor_0/BoxEncodingPredictor/BiasAdd"
2112 | }
2113 | node {
2114 | name: "BoxPredictor_1/BoxEncodingPredictor/BiasAdd/Flatten"
2115 | op: "Flatten"
2116 | input: "BoxPredictor_1/BoxEncodingPredictor/BiasAdd"
2117 | }
2118 | node {
2119 | name: "BoxPredictor_2/BoxEncodingPredictor/BiasAdd/Flatten"
2120 | op: "Flatten"
2121 | input: "BoxPredictor_2/BoxEncodingPredictor/BiasAdd"
2122 | }
2123 | node {
2124 | name: "BoxPredictor_3/BoxEncodingPredictor/BiasAdd/Flatten"
2125 | op: "Flatten"
2126 | input: "BoxPredictor_3/BoxEncodingPredictor/BiasAdd"
2127 | }
2128 | node {
2129 | name: "BoxPredictor_4/BoxEncodingPredictor/BiasAdd/Flatten"
2130 | op: "Flatten"
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270 | i: 1
271 | }
272 | }
273 | }
274 | attr {
275 | key: "padding"
276 | value {
277 | s: "SAME"
278 | }
279 | }
280 | attr {
281 | key: "strides"
282 | value {
283 | list {
284 | i: 1
285 | i: 2
286 | i: 2
287 | i: 1
288 | }
289 | }
290 | }
291 | }
292 | node {
293 | name: "FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/BatchNorm/batchnorm/add_1"
294 | op: "FusedBatchNorm"
295 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/depthwise"
296 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/BatchNorm/gamma"
297 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/BatchNorm/beta"
298 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/BatchNorm/moving_mean"
299 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/BatchNorm/moving_variance"
300 | attr {
301 | key: "epsilon"
302 | value {
303 | f: 0.0010000000475
304 | }
305 | }
306 | }
307 | node {
308 | name: "FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/Relu6"
309 | op: "Relu6"
310 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/BatchNorm/batchnorm/add_1"
311 | }
312 | node {
313 | name: "FeatureExtractor/MobilenetV2/expanded_conv_1/project/Conv2D"
314 | op: "Conv2D"
315 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/Relu6"
316 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/project/weights"
317 | attr {
318 | key: "dilations"
319 | value {
320 | list {
321 | i: 1
322 | i: 1
323 | i: 1
324 | i: 1
325 | }
326 | }
327 | }
328 | attr {
329 | key: "padding"
330 | value {
331 | s: "SAME"
332 | }
333 | }
334 | attr {
335 | key: "strides"
336 | value {
337 | list {
338 | i: 1
339 | i: 1
340 | i: 1
341 | i: 1
342 | }
343 | }
344 | }
345 | }
346 | node {
347 | name: "FeatureExtractor/MobilenetV2/expanded_conv_1/project/BatchNorm/batchnorm/add_1"
348 | op: "FusedBatchNorm"
349 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/project/Conv2D"
350 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/project/BatchNorm/gamma"
351 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/project/BatchNorm/beta"
352 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/project/BatchNorm/moving_mean"
353 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/project/BatchNorm/moving_variance"
354 | attr {
355 | key: "epsilon"
356 | value {
357 | f: 0.0010000000475
358 | }
359 | }
360 | }
361 | node {
362 | name: "FeatureExtractor/MobilenetV2/expanded_conv_1/output"
363 | op: "Identity"
364 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/project/BatchNorm/batchnorm/add_1"
365 | }
366 | node {
367 | name: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/Conv2D"
368 | op: "Conv2D"
369 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/output"
370 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/weights"
371 | attr {
372 | key: "dilations"
373 | value {
374 | list {
375 | i: 1
376 | i: 1
377 | i: 1
378 | i: 1
379 | }
380 | }
381 | }
382 | attr {
383 | key: "padding"
384 | value {
385 | s: "SAME"
386 | }
387 | }
388 | attr {
389 | key: "strides"
390 | value {
391 | list {
392 | i: 1
393 | i: 1
394 | i: 1
395 | i: 1
396 | }
397 | }
398 | }
399 | }
400 | node {
401 | name: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/BatchNorm/batchnorm/add_1"
402 | op: "FusedBatchNorm"
403 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/Conv2D"
404 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/BatchNorm/gamma"
405 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/BatchNorm/beta"
406 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/BatchNorm/moving_mean"
407 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/BatchNorm/moving_variance"
408 | attr {
409 | key: "epsilon"
410 | value {
411 | f: 0.0010000000475
412 | }
413 | }
414 | }
415 | node {
416 | name: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/Relu6"
417 | op: "Relu6"
418 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/BatchNorm/batchnorm/add_1"
419 | }
420 | node {
421 | name: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/depthwise"
422 | op: "DepthwiseConv2dNative"
423 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/expand/Relu6"
424 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/depthwise_weights"
425 | attr {
426 | key: "dilations"
427 | value {
428 | list {
429 | i: 1
430 | i: 1
431 | i: 1
432 | i: 1
433 | }
434 | }
435 | }
436 | attr {
437 | key: "padding"
438 | value {
439 | s: "SAME"
440 | }
441 | }
442 | attr {
443 | key: "strides"
444 | value {
445 | list {
446 | i: 1
447 | i: 1
448 | i: 1
449 | i: 1
450 | }
451 | }
452 | }
453 | }
454 | node {
455 | name: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/BatchNorm/batchnorm/add_1"
456 | op: "FusedBatchNorm"
457 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/depthwise"
458 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/BatchNorm/gamma"
459 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/BatchNorm/beta"
460 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/BatchNorm/moving_mean"
461 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/BatchNorm/moving_variance"
462 | attr {
463 | key: "epsilon"
464 | value {
465 | f: 0.0010000000475
466 | }
467 | }
468 | }
469 | node {
470 | name: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/Relu6"
471 | op: "Relu6"
472 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/BatchNorm/batchnorm/add_1"
473 | }
474 | node {
475 | name: "FeatureExtractor/MobilenetV2/expanded_conv_2/project/Conv2D"
476 | op: "Conv2D"
477 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/Relu6"
478 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/project/weights"
479 | attr {
480 | key: "dilations"
481 | value {
482 | list {
483 | i: 1
484 | i: 1
485 | i: 1
486 | i: 1
487 | }
488 | }
489 | }
490 | attr {
491 | key: "padding"
492 | value {
493 | s: "SAME"
494 | }
495 | }
496 | attr {
497 | key: "strides"
498 | value {
499 | list {
500 | i: 1
501 | i: 1
502 | i: 1
503 | i: 1
504 | }
505 | }
506 | }
507 | }
508 | node {
509 | name: "FeatureExtractor/MobilenetV2/expanded_conv_2/project/BatchNorm/batchnorm/add_1"
510 | op: "FusedBatchNorm"
511 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/project/Conv2D"
512 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/project/BatchNorm/gamma"
513 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/project/BatchNorm/beta"
514 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/project/BatchNorm/moving_mean"
515 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/project/BatchNorm/moving_variance"
516 | attr {
517 | key: "epsilon"
518 | value {
519 | f: 0.0010000000475
520 | }
521 | }
522 | }
523 | node {
524 | name: "FeatureExtractor/MobilenetV2/expanded_conv_2/add"
525 | op: "Add"
526 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/project/BatchNorm/batchnorm/add_1"
527 | input: "FeatureExtractor/MobilenetV2/expanded_conv_1/output"
528 | }
529 | node {
530 | name: "FeatureExtractor/MobilenetV2/expanded_conv_3/input"
531 | op: "Identity"
532 | input: "FeatureExtractor/MobilenetV2/expanded_conv_2/add"
533 | }
534 | node {
535 | name: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/Conv2D"
536 | op: "Conv2D"
537 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/input"
538 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/weights"
539 | attr {
540 | key: "dilations"
541 | value {
542 | list {
543 | i: 1
544 | i: 1
545 | i: 1
546 | i: 1
547 | }
548 | }
549 | }
550 | attr {
551 | key: "padding"
552 | value {
553 | s: "SAME"
554 | }
555 | }
556 | attr {
557 | key: "strides"
558 | value {
559 | list {
560 | i: 1
561 | i: 1
562 | i: 1
563 | i: 1
564 | }
565 | }
566 | }
567 | }
568 | node {
569 | name: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/BatchNorm/batchnorm/add_1"
570 | op: "FusedBatchNorm"
571 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/Conv2D"
572 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/BatchNorm/gamma"
573 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/BatchNorm/beta"
574 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/BatchNorm/moving_mean"
575 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/BatchNorm/moving_variance"
576 | attr {
577 | key: "epsilon"
578 | value {
579 | f: 0.0010000000475
580 | }
581 | }
582 | }
583 | node {
584 | name: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/Relu6"
585 | op: "Relu6"
586 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/BatchNorm/batchnorm/add_1"
587 | }
588 | node {
589 | name: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/depthwise"
590 | op: "DepthwiseConv2dNative"
591 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/expand/Relu6"
592 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/depthwise_weights"
593 | attr {
594 | key: "dilations"
595 | value {
596 | list {
597 | i: 1
598 | i: 1
599 | i: 1
600 | i: 1
601 | }
602 | }
603 | }
604 | attr {
605 | key: "padding"
606 | value {
607 | s: "SAME"
608 | }
609 | }
610 | attr {
611 | key: "strides"
612 | value {
613 | list {
614 | i: 1
615 | i: 2
616 | i: 2
617 | i: 1
618 | }
619 | }
620 | }
621 | }
622 | node {
623 | name: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/BatchNorm/batchnorm/add_1"
624 | op: "FusedBatchNorm"
625 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/depthwise"
626 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/BatchNorm/gamma"
627 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/BatchNorm/beta"
628 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/BatchNorm/moving_mean"
629 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/BatchNorm/moving_variance"
630 | attr {
631 | key: "epsilon"
632 | value {
633 | f: 0.0010000000475
634 | }
635 | }
636 | }
637 | node {
638 | name: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/Relu6"
639 | op: "Relu6"
640 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/BatchNorm/batchnorm/add_1"
641 | }
642 | node {
643 | name: "FeatureExtractor/MobilenetV2/expanded_conv_3/project/Conv2D"
644 | op: "Conv2D"
645 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/Relu6"
646 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/project/weights"
647 | attr {
648 | key: "dilations"
649 | value {
650 | list {
651 | i: 1
652 | i: 1
653 | i: 1
654 | i: 1
655 | }
656 | }
657 | }
658 | attr {
659 | key: "padding"
660 | value {
661 | s: "SAME"
662 | }
663 | }
664 | attr {
665 | key: "strides"
666 | value {
667 | list {
668 | i: 1
669 | i: 1
670 | i: 1
671 | i: 1
672 | }
673 | }
674 | }
675 | }
676 | node {
677 | name: "FeatureExtractor/MobilenetV2/expanded_conv_3/project/BatchNorm/batchnorm/add_1"
678 | op: "FusedBatchNorm"
679 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/project/Conv2D"
680 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/project/BatchNorm/gamma"
681 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/project/BatchNorm/beta"
682 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/project/BatchNorm/moving_mean"
683 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/project/BatchNorm/moving_variance"
684 | attr {
685 | key: "epsilon"
686 | value {
687 | f: 0.0010000000475
688 | }
689 | }
690 | }
691 | node {
692 | name: "FeatureExtractor/MobilenetV2/expanded_conv_3/output"
693 | op: "Identity"
694 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/project/BatchNorm/batchnorm/add_1"
695 | }
696 | node {
697 | name: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/Conv2D"
698 | op: "Conv2D"
699 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/output"
700 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/weights"
701 | attr {
702 | key: "dilations"
703 | value {
704 | list {
705 | i: 1
706 | i: 1
707 | i: 1
708 | i: 1
709 | }
710 | }
711 | }
712 | attr {
713 | key: "padding"
714 | value {
715 | s: "SAME"
716 | }
717 | }
718 | attr {
719 | key: "strides"
720 | value {
721 | list {
722 | i: 1
723 | i: 1
724 | i: 1
725 | i: 1
726 | }
727 | }
728 | }
729 | }
730 | node {
731 | name: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/BatchNorm/batchnorm/add_1"
732 | op: "FusedBatchNorm"
733 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/Conv2D"
734 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/BatchNorm/gamma"
735 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/BatchNorm/beta"
736 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/BatchNorm/moving_mean"
737 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/BatchNorm/moving_variance"
738 | attr {
739 | key: "epsilon"
740 | value {
741 | f: 0.0010000000475
742 | }
743 | }
744 | }
745 | node {
746 | name: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/Relu6"
747 | op: "Relu6"
748 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/BatchNorm/batchnorm/add_1"
749 | }
750 | node {
751 | name: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/depthwise"
752 | op: "DepthwiseConv2dNative"
753 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/expand/Relu6"
754 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/depthwise_weights"
755 | attr {
756 | key: "dilations"
757 | value {
758 | list {
759 | i: 1
760 | i: 1
761 | i: 1
762 | i: 1
763 | }
764 | }
765 | }
766 | attr {
767 | key: "padding"
768 | value {
769 | s: "SAME"
770 | }
771 | }
772 | attr {
773 | key: "strides"
774 | value {
775 | list {
776 | i: 1
777 | i: 1
778 | i: 1
779 | i: 1
780 | }
781 | }
782 | }
783 | }
784 | node {
785 | name: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/BatchNorm/batchnorm/add_1"
786 | op: "FusedBatchNorm"
787 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/depthwise"
788 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/BatchNorm/gamma"
789 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/BatchNorm/beta"
790 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/BatchNorm/moving_mean"
791 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/BatchNorm/moving_variance"
792 | attr {
793 | key: "epsilon"
794 | value {
795 | f: 0.0010000000475
796 | }
797 | }
798 | }
799 | node {
800 | name: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/Relu6"
801 | op: "Relu6"
802 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/BatchNorm/batchnorm/add_1"
803 | }
804 | node {
805 | name: "FeatureExtractor/MobilenetV2/expanded_conv_4/project/Conv2D"
806 | op: "Conv2D"
807 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/Relu6"
808 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/project/weights"
809 | attr {
810 | key: "dilations"
811 | value {
812 | list {
813 | i: 1
814 | i: 1
815 | i: 1
816 | i: 1
817 | }
818 | }
819 | }
820 | attr {
821 | key: "padding"
822 | value {
823 | s: "SAME"
824 | }
825 | }
826 | attr {
827 | key: "strides"
828 | value {
829 | list {
830 | i: 1
831 | i: 1
832 | i: 1
833 | i: 1
834 | }
835 | }
836 | }
837 | }
838 | node {
839 | name: "FeatureExtractor/MobilenetV2/expanded_conv_4/project/BatchNorm/batchnorm/add_1"
840 | op: "FusedBatchNorm"
841 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/project/Conv2D"
842 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/project/BatchNorm/gamma"
843 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/project/BatchNorm/beta"
844 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/project/BatchNorm/moving_mean"
845 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/project/BatchNorm/moving_variance"
846 | attr {
847 | key: "epsilon"
848 | value {
849 | f: 0.0010000000475
850 | }
851 | }
852 | }
853 | node {
854 | name: "FeatureExtractor/MobilenetV2/expanded_conv_4/add"
855 | op: "Add"
856 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/project/BatchNorm/batchnorm/add_1"
857 | input: "FeatureExtractor/MobilenetV2/expanded_conv_3/output"
858 | }
859 | node {
860 | name: "FeatureExtractor/MobilenetV2/expanded_conv_5/input"
861 | op: "Identity"
862 | input: "FeatureExtractor/MobilenetV2/expanded_conv_4/add"
863 | }
864 | node {
865 | name: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/Conv2D"
866 | op: "Conv2D"
867 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/input"
868 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/weights"
869 | attr {
870 | key: "dilations"
871 | value {
872 | list {
873 | i: 1
874 | i: 1
875 | i: 1
876 | i: 1
877 | }
878 | }
879 | }
880 | attr {
881 | key: "padding"
882 | value {
883 | s: "SAME"
884 | }
885 | }
886 | attr {
887 | key: "strides"
888 | value {
889 | list {
890 | i: 1
891 | i: 1
892 | i: 1
893 | i: 1
894 | }
895 | }
896 | }
897 | }
898 | node {
899 | name: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/BatchNorm/batchnorm/add_1"
900 | op: "FusedBatchNorm"
901 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/Conv2D"
902 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/BatchNorm/gamma"
903 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/BatchNorm/beta"
904 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/BatchNorm/moving_mean"
905 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/BatchNorm/moving_variance"
906 | attr {
907 | key: "epsilon"
908 | value {
909 | f: 0.0010000000475
910 | }
911 | }
912 | }
913 | node {
914 | name: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/Relu6"
915 | op: "Relu6"
916 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/BatchNorm/batchnorm/add_1"
917 | }
918 | node {
919 | name: "FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/depthwise"
920 | op: "DepthwiseConv2dNative"
921 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/expand/Relu6"
922 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/depthwise_weights"
923 | attr {
924 | key: "dilations"
925 | value {
926 | list {
927 | i: 1
928 | i: 1
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930 | i: 1
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950 | }
951 | }
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955 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/depthwise"
956 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/BatchNorm/gamma"
957 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/BatchNorm/beta"
958 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/BatchNorm/moving_mean"
959 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/BatchNorm/moving_variance"
960 | attr {
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970 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/BatchNorm/batchnorm/add_1"
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975 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/Relu6"
976 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/project/weights"
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1002 | }
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1004 | }
1005 | }
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1008 | op: "FusedBatchNorm"
1009 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/project/Conv2D"
1010 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/project/BatchNorm/gamma"
1011 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/project/BatchNorm/beta"
1012 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/project/BatchNorm/moving_mean"
1013 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/project/BatchNorm/moving_variance"
1014 | attr {
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1023 | op: "Add"
1024 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/project/BatchNorm/batchnorm/add_1"
1025 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/input"
1026 | }
1027 | node {
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1029 | op: "Identity"
1030 | input: "FeatureExtractor/MobilenetV2/expanded_conv_5/add"
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1035 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/input"
1036 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/expand/weights"
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1069 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/expand/Conv2D"
1070 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/expand/BatchNorm/gamma"
1071 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/expand/BatchNorm/beta"
1072 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/expand/BatchNorm/moving_mean"
1073 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/expand/BatchNorm/moving_variance"
1074 | attr {
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1079 | }
1080 | }
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1083 | op: "Relu6"
1084 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/expand/BatchNorm/batchnorm/add_1"
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1089 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/expand/Relu6"
1090 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/depthwise_weights"
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1124 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/BatchNorm/gamma"
1125 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/BatchNorm/beta"
1126 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/BatchNorm/moving_mean"
1127 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/BatchNorm/moving_variance"
1128 | attr {
1129 | key: "epsilon"
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1132 | }
1133 | }
1134 | }
1135 | node {
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1137 | op: "Relu6"
1138 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/BatchNorm/batchnorm/add_1"
1139 | }
1140 | node {
1141 | name: "FeatureExtractor/MobilenetV2/expanded_conv_6/project/Conv2D"
1142 | op: "Conv2D"
1143 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/Relu6"
1144 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/project/weights"
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1177 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/project/Conv2D"
1178 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/project/BatchNorm/gamma"
1179 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/project/BatchNorm/beta"
1180 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/project/BatchNorm/moving_mean"
1181 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/project/BatchNorm/moving_variance"
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1188 | }
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1191 | op: "Identity"
1192 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/project/BatchNorm/batchnorm/add_1"
1193 | }
1194 | node {
1195 | name: "FeatureExtractor/MobilenetV2/expanded_conv_7/expand/Conv2D"
1196 | op: "Conv2D"
1197 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/output"
1198 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/expand/weights"
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1200 | key: "dilations"
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1231 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/expand/Conv2D"
1232 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/expand/BatchNorm/gamma"
1233 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/expand/BatchNorm/beta"
1234 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/expand/BatchNorm/moving_mean"
1235 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/expand/BatchNorm/moving_variance"
1236 | attr {
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1242 | }
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1245 | op: "Relu6"
1246 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/expand/BatchNorm/batchnorm/add_1"
1247 | }
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1250 | op: "DepthwiseConv2dNative"
1251 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/expand/Relu6"
1252 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/depthwise_weights"
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1286 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/BatchNorm/gamma"
1287 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/BatchNorm/beta"
1288 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/BatchNorm/moving_mean"
1289 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/BatchNorm/moving_variance"
1290 | attr {
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1296 | }
1297 | node {
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1299 | op: "Relu6"
1300 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/BatchNorm/batchnorm/add_1"
1301 | }
1302 | node {
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1304 | op: "Conv2D"
1305 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/Relu6"
1306 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/project/weights"
1307 | attr {
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1338 | op: "FusedBatchNorm"
1339 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/project/Conv2D"
1340 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/project/BatchNorm/gamma"
1341 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/project/BatchNorm/beta"
1342 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/project/BatchNorm/moving_mean"
1343 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/project/BatchNorm/moving_variance"
1344 | attr {
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1350 | }
1351 | node {
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1353 | op: "Add"
1354 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/project/BatchNorm/batchnorm/add_1"
1355 | input: "FeatureExtractor/MobilenetV2/expanded_conv_6/output"
1356 | }
1357 | node {
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1359 | op: "Identity"
1360 | input: "FeatureExtractor/MobilenetV2/expanded_conv_7/add"
1361 | }
1362 | node {
1363 | name: "FeatureExtractor/MobilenetV2/expanded_conv_8/expand/Conv2D"
1364 | op: "Conv2D"
1365 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/input"
1366 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/expand/weights"
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1398 | op: "FusedBatchNorm"
1399 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/expand/Conv2D"
1400 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/expand/BatchNorm/gamma"
1401 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/expand/BatchNorm/beta"
1402 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/expand/BatchNorm/moving_mean"
1403 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/expand/BatchNorm/moving_variance"
1404 | attr {
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1413 | op: "Relu6"
1414 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/expand/BatchNorm/batchnorm/add_1"
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1418 | op: "DepthwiseConv2dNative"
1419 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/expand/Relu6"
1420 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/depthwise_weights"
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1453 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/depthwise"
1454 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/BatchNorm/gamma"
1455 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/BatchNorm/beta"
1456 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/BatchNorm/moving_mean"
1457 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/BatchNorm/moving_variance"
1458 | attr {
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1460 | value {
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1464 | }
1465 | node {
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1467 | op: "Relu6"
1468 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/BatchNorm/batchnorm/add_1"
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1470 | node {
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1472 | op: "Conv2D"
1473 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/Relu6"
1474 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/project/weights"
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1500 | }
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1506 | op: "FusedBatchNorm"
1507 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/project/Conv2D"
1508 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/project/BatchNorm/gamma"
1509 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/project/BatchNorm/beta"
1510 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/project/BatchNorm/moving_mean"
1511 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/project/BatchNorm/moving_variance"
1512 | attr {
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1518 | }
1519 | node {
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1521 | op: "Add"
1522 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/project/BatchNorm/batchnorm/add_1"
1523 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/input"
1524 | }
1525 | node {
1526 | name: "FeatureExtractor/MobilenetV2/expanded_conv_9/input"
1527 | op: "Identity"
1528 | input: "FeatureExtractor/MobilenetV2/expanded_conv_8/add"
1529 | }
1530 | node {
1531 | name: "FeatureExtractor/MobilenetV2/expanded_conv_9/expand/Conv2D"
1532 | op: "Conv2D"
1533 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/input"
1534 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/expand/weights"
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1568 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/expand/BatchNorm/gamma"
1569 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/expand/BatchNorm/beta"
1570 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/expand/BatchNorm/moving_mean"
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1582 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/expand/BatchNorm/batchnorm/add_1"
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1587 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/expand/Relu6"
1588 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/depthwise_weights"
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1621 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/depthwise"
1622 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/BatchNorm/gamma"
1623 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/BatchNorm/beta"
1624 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/BatchNorm/moving_mean"
1625 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/BatchNorm/moving_variance"
1626 | attr {
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1630 | }
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1632 | }
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1636 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/BatchNorm/batchnorm/add_1"
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1641 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/Relu6"
1642 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/project/weights"
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1676 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/project/BatchNorm/gamma"
1677 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/project/BatchNorm/beta"
1678 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/project/BatchNorm/moving_mean"
1679 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/project/BatchNorm/moving_variance"
1680 | attr {
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1690 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/project/BatchNorm/batchnorm/add_1"
1691 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/input"
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1696 | input: "FeatureExtractor/MobilenetV2/expanded_conv_9/add"
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1702 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/expand/weights"
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1735 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/expand/Conv2D"
1736 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/expand/BatchNorm/gamma"
1737 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/expand/BatchNorm/beta"
1738 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/expand/BatchNorm/moving_mean"
1739 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/expand/BatchNorm/moving_variance"
1740 | attr {
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1750 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/expand/BatchNorm/batchnorm/add_1"
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1755 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/expand/Relu6"
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1790 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/BatchNorm/gamma"
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1792 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/BatchNorm/moving_mean"
1793 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/BatchNorm/moving_variance"
1794 | attr {
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1800 | }
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1804 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/BatchNorm/batchnorm/add_1"
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1810 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/project/weights"
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1844 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/project/BatchNorm/gamma"
1845 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/project/BatchNorm/beta"
1846 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/project/BatchNorm/moving_mean"
1847 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/project/BatchNorm/moving_variance"
1848 | attr {
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1857 | op: "Identity"
1858 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/project/BatchNorm/batchnorm/add_1"
1859 | }
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1864 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/expand/weights"
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1898 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/expand/BatchNorm/gamma"
1899 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/expand/BatchNorm/beta"
1900 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/expand/BatchNorm/moving_mean"
1901 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/expand/BatchNorm/moving_variance"
1902 | attr {
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1911 | op: "Relu6"
1912 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/expand/BatchNorm/batchnorm/add_1"
1913 | }
1914 | node {
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1917 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/expand/Relu6"
1918 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/depthwise_weights"
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1947 | }
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1950 | op: "FusedBatchNorm"
1951 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/depthwise"
1952 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/BatchNorm/gamma"
1953 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/BatchNorm/beta"
1954 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/BatchNorm/moving_mean"
1955 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/BatchNorm/moving_variance"
1956 | attr {
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1958 | value {
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1961 | }
1962 | }
1963 | node {
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1965 | op: "Relu6"
1966 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/BatchNorm/batchnorm/add_1"
1967 | }
1968 | node {
1969 | name: "FeatureExtractor/MobilenetV2/expanded_conv_11/project/Conv2D"
1970 | op: "Conv2D"
1971 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/Relu6"
1972 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/project/weights"
1973 | attr {
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1975 | value {
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1989 | }
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2001 | }
2002 | node {
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2004 | op: "FusedBatchNorm"
2005 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/project/Conv2D"
2006 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/project/BatchNorm/gamma"
2007 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/project/BatchNorm/beta"
2008 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/project/BatchNorm/moving_mean"
2009 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/project/BatchNorm/moving_variance"
2010 | attr {
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2012 | value {
2013 | f: 0.0010000000475
2014 | }
2015 | }
2016 | }
2017 | node {
2018 | name: "FeatureExtractor/MobilenetV2/expanded_conv_11/add"
2019 | op: "Add"
2020 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/project/BatchNorm/batchnorm/add_1"
2021 | input: "FeatureExtractor/MobilenetV2/expanded_conv_10/output"
2022 | }
2023 | node {
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2025 | op: "Identity"
2026 | input: "FeatureExtractor/MobilenetV2/expanded_conv_11/add"
2027 | }
2028 | node {
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2031 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/input"
2032 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/expand/weights"
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2066 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/expand/BatchNorm/gamma"
2067 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/expand/BatchNorm/beta"
2068 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/expand/BatchNorm/moving_mean"
2069 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/expand/BatchNorm/moving_variance"
2070 | attr {
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2076 | }
2077 | node {
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2079 | op: "Relu6"
2080 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/expand/BatchNorm/batchnorm/add_1"
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2084 | op: "DepthwiseConv2dNative"
2085 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/expand/Relu6"
2086 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/depthwise_weights"
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2118 | op: "FusedBatchNorm"
2119 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/depthwise"
2120 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/BatchNorm/gamma"
2121 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/BatchNorm/beta"
2122 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/BatchNorm/moving_mean"
2123 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/BatchNorm/moving_variance"
2124 | attr {
2125 | key: "epsilon"
2126 | value {
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2128 | }
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2130 | }
2131 | node {
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2133 | op: "Relu6"
2134 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/BatchNorm/batchnorm/add_1"
2135 | }
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2138 | op: "Conv2D"
2139 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/Relu6"
2140 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/project/weights"
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2169 | }
2170 | node {
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2172 | op: "FusedBatchNorm"
2173 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/project/Conv2D"
2174 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/project/BatchNorm/gamma"
2175 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/project/BatchNorm/beta"
2176 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/project/BatchNorm/moving_mean"
2177 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/project/BatchNorm/moving_variance"
2178 | attr {
2179 | key: "epsilon"
2180 | value {
2181 | f: 0.0010000000475
2182 | }
2183 | }
2184 | }
2185 | node {
2186 | name: "FeatureExtractor/MobilenetV2/expanded_conv_12/add"
2187 | op: "Add"
2188 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/project/BatchNorm/batchnorm/add_1"
2189 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/input"
2190 | }
2191 | node {
2192 | name: "FeatureExtractor/MobilenetV2/expanded_conv_13/input"
2193 | op: "Identity"
2194 | input: "FeatureExtractor/MobilenetV2/expanded_conv_12/add"
2195 | }
2196 | node {
2197 | name: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/Conv2D"
2198 | op: "Conv2D"
2199 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/input"
2200 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/weights"
2201 | attr {
2202 | key: "dilations"
2203 | value {
2204 | list {
2205 | i: 1
2206 | i: 1
2207 | i: 1
2208 | i: 1
2209 | }
2210 | }
2211 | }
2212 | attr {
2213 | key: "padding"
2214 | value {
2215 | s: "SAME"
2216 | }
2217 | }
2218 | attr {
2219 | key: "strides"
2220 | value {
2221 | list {
2222 | i: 1
2223 | i: 1
2224 | i: 1
2225 | i: 1
2226 | }
2227 | }
2228 | }
2229 | }
2230 | node {
2231 | name: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/BatchNorm/batchnorm/add_1"
2232 | op: "FusedBatchNorm"
2233 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/Conv2D"
2234 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/BatchNorm/gamma"
2235 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/BatchNorm/beta"
2236 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/BatchNorm/moving_mean"
2237 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/BatchNorm/moving_variance"
2238 | attr {
2239 | key: "epsilon"
2240 | value {
2241 | f: 0.0010000000475
2242 | }
2243 | }
2244 | }
2245 | node {
2246 | name: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/Relu6"
2247 | op: "Relu6"
2248 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/BatchNorm/batchnorm/add_1"
2249 | }
2250 | node {
2251 | name: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/depthwise"
2252 | op: "DepthwiseConv2dNative"
2253 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/Relu6"
2254 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/depthwise_weights"
2255 | attr {
2256 | key: "dilations"
2257 | value {
2258 | list {
2259 | i: 1
2260 | i: 1
2261 | i: 1
2262 | i: 1
2263 | }
2264 | }
2265 | }
2266 | attr {
2267 | key: "padding"
2268 | value {
2269 | s: "SAME"
2270 | }
2271 | }
2272 | attr {
2273 | key: "strides"
2274 | value {
2275 | list {
2276 | i: 1
2277 | i: 2
2278 | i: 2
2279 | i: 1
2280 | }
2281 | }
2282 | }
2283 | }
2284 | node {
2285 | name: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/BatchNorm/batchnorm/add_1"
2286 | op: "FusedBatchNorm"
2287 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/depthwise"
2288 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/BatchNorm/gamma"
2289 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/BatchNorm/beta"
2290 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/BatchNorm/moving_mean"
2291 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/BatchNorm/moving_variance"
2292 | attr {
2293 | key: "epsilon"
2294 | value {
2295 | f: 0.0010000000475
2296 | }
2297 | }
2298 | }
2299 | node {
2300 | name: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/Relu6"
2301 | op: "Relu6"
2302 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/BatchNorm/batchnorm/add_1"
2303 | }
2304 | node {
2305 | name: "FeatureExtractor/MobilenetV2/expanded_conv_13/project/Conv2D"
2306 | op: "Conv2D"
2307 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/Relu6"
2308 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/project/weights"
2309 | attr {
2310 | key: "dilations"
2311 | value {
2312 | list {
2313 | i: 1
2314 | i: 1
2315 | i: 1
2316 | i: 1
2317 | }
2318 | }
2319 | }
2320 | attr {
2321 | key: "padding"
2322 | value {
2323 | s: "SAME"
2324 | }
2325 | }
2326 | attr {
2327 | key: "strides"
2328 | value {
2329 | list {
2330 | i: 1
2331 | i: 1
2332 | i: 1
2333 | i: 1
2334 | }
2335 | }
2336 | }
2337 | }
2338 | node {
2339 | name: "FeatureExtractor/MobilenetV2/expanded_conv_13/project/BatchNorm/batchnorm/add_1"
2340 | op: "FusedBatchNorm"
2341 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/project/Conv2D"
2342 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/project/BatchNorm/gamma"
2343 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/project/BatchNorm/beta"
2344 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/project/BatchNorm/moving_mean"
2345 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/project/BatchNorm/moving_variance"
2346 | attr {
2347 | key: "epsilon"
2348 | value {
2349 | f: 0.0010000000475
2350 | }
2351 | }
2352 | }
2353 | node {
2354 | name: "FeatureExtractor/MobilenetV2/expanded_conv_13/output"
2355 | op: "Identity"
2356 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/project/BatchNorm/batchnorm/add_1"
2357 | }
2358 | node {
2359 | name: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/Conv2D"
2360 | op: "Conv2D"
2361 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/output"
2362 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/weights"
2363 | attr {
2364 | key: "dilations"
2365 | value {
2366 | list {
2367 | i: 1
2368 | i: 1
2369 | i: 1
2370 | i: 1
2371 | }
2372 | }
2373 | }
2374 | attr {
2375 | key: "padding"
2376 | value {
2377 | s: "SAME"
2378 | }
2379 | }
2380 | attr {
2381 | key: "strides"
2382 | value {
2383 | list {
2384 | i: 1
2385 | i: 1
2386 | i: 1
2387 | i: 1
2388 | }
2389 | }
2390 | }
2391 | }
2392 | node {
2393 | name: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/BatchNorm/batchnorm/add_1"
2394 | op: "FusedBatchNorm"
2395 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/Conv2D"
2396 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/BatchNorm/gamma"
2397 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/BatchNorm/beta"
2398 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/BatchNorm/moving_mean"
2399 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/BatchNorm/moving_variance"
2400 | attr {
2401 | key: "epsilon"
2402 | value {
2403 | f: 0.0010000000475
2404 | }
2405 | }
2406 | }
2407 | node {
2408 | name: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/Relu6"
2409 | op: "Relu6"
2410 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/BatchNorm/batchnorm/add_1"
2411 | }
2412 | node {
2413 | name: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/depthwise"
2414 | op: "DepthwiseConv2dNative"
2415 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/expand/Relu6"
2416 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/depthwise_weights"
2417 | attr {
2418 | key: "dilations"
2419 | value {
2420 | list {
2421 | i: 1
2422 | i: 1
2423 | i: 1
2424 | i: 1
2425 | }
2426 | }
2427 | }
2428 | attr {
2429 | key: "padding"
2430 | value {
2431 | s: "SAME"
2432 | }
2433 | }
2434 | attr {
2435 | key: "strides"
2436 | value {
2437 | list {
2438 | i: 1
2439 | i: 1
2440 | i: 1
2441 | i: 1
2442 | }
2443 | }
2444 | }
2445 | }
2446 | node {
2447 | name: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/BatchNorm/batchnorm/add_1"
2448 | op: "FusedBatchNorm"
2449 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/depthwise"
2450 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/BatchNorm/gamma"
2451 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/BatchNorm/beta"
2452 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/BatchNorm/moving_mean"
2453 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/BatchNorm/moving_variance"
2454 | attr {
2455 | key: "epsilon"
2456 | value {
2457 | f: 0.0010000000475
2458 | }
2459 | }
2460 | }
2461 | node {
2462 | name: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/Relu6"
2463 | op: "Relu6"
2464 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/BatchNorm/batchnorm/add_1"
2465 | }
2466 | node {
2467 | name: "FeatureExtractor/MobilenetV2/expanded_conv_14/project/Conv2D"
2468 | op: "Conv2D"
2469 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/Relu6"
2470 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/project/weights"
2471 | attr {
2472 | key: "dilations"
2473 | value {
2474 | list {
2475 | i: 1
2476 | i: 1
2477 | i: 1
2478 | i: 1
2479 | }
2480 | }
2481 | }
2482 | attr {
2483 | key: "padding"
2484 | value {
2485 | s: "SAME"
2486 | }
2487 | }
2488 | attr {
2489 | key: "strides"
2490 | value {
2491 | list {
2492 | i: 1
2493 | i: 1
2494 | i: 1
2495 | i: 1
2496 | }
2497 | }
2498 | }
2499 | }
2500 | node {
2501 | name: "FeatureExtractor/MobilenetV2/expanded_conv_14/project/BatchNorm/batchnorm/add_1"
2502 | op: "FusedBatchNorm"
2503 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/project/Conv2D"
2504 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/project/BatchNorm/gamma"
2505 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/project/BatchNorm/beta"
2506 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/project/BatchNorm/moving_mean"
2507 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/project/BatchNorm/moving_variance"
2508 | attr {
2509 | key: "epsilon"
2510 | value {
2511 | f: 0.0010000000475
2512 | }
2513 | }
2514 | }
2515 | node {
2516 | name: "FeatureExtractor/MobilenetV2/expanded_conv_14/add"
2517 | op: "Add"
2518 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/project/BatchNorm/batchnorm/add_1"
2519 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/output"
2520 | }
2521 | node {
2522 | name: "FeatureExtractor/MobilenetV2/expanded_conv_15/input"
2523 | op: "Identity"
2524 | input: "FeatureExtractor/MobilenetV2/expanded_conv_14/add"
2525 | }
2526 | node {
2527 | name: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/Conv2D"
2528 | op: "Conv2D"
2529 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/input"
2530 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/weights"
2531 | attr {
2532 | key: "dilations"
2533 | value {
2534 | list {
2535 | i: 1
2536 | i: 1
2537 | i: 1
2538 | i: 1
2539 | }
2540 | }
2541 | }
2542 | attr {
2543 | key: "padding"
2544 | value {
2545 | s: "SAME"
2546 | }
2547 | }
2548 | attr {
2549 | key: "strides"
2550 | value {
2551 | list {
2552 | i: 1
2553 | i: 1
2554 | i: 1
2555 | i: 1
2556 | }
2557 | }
2558 | }
2559 | }
2560 | node {
2561 | name: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/BatchNorm/batchnorm/add_1"
2562 | op: "FusedBatchNorm"
2563 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/Conv2D"
2564 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/BatchNorm/gamma"
2565 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/BatchNorm/beta"
2566 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/BatchNorm/moving_mean"
2567 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/BatchNorm/moving_variance"
2568 | attr {
2569 | key: "epsilon"
2570 | value {
2571 | f: 0.0010000000475
2572 | }
2573 | }
2574 | }
2575 | node {
2576 | name: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/Relu6"
2577 | op: "Relu6"
2578 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/BatchNorm/batchnorm/add_1"
2579 | }
2580 | node {
2581 | name: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/depthwise"
2582 | op: "DepthwiseConv2dNative"
2583 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/expand/Relu6"
2584 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/depthwise_weights"
2585 | attr {
2586 | key: "dilations"
2587 | value {
2588 | list {
2589 | i: 1
2590 | i: 1
2591 | i: 1
2592 | i: 1
2593 | }
2594 | }
2595 | }
2596 | attr {
2597 | key: "padding"
2598 | value {
2599 | s: "SAME"
2600 | }
2601 | }
2602 | attr {
2603 | key: "strides"
2604 | value {
2605 | list {
2606 | i: 1
2607 | i: 1
2608 | i: 1
2609 | i: 1
2610 | }
2611 | }
2612 | }
2613 | }
2614 | node {
2615 | name: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/BatchNorm/batchnorm/add_1"
2616 | op: "FusedBatchNorm"
2617 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/depthwise"
2618 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/BatchNorm/gamma"
2619 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/BatchNorm/beta"
2620 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/BatchNorm/moving_mean"
2621 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/BatchNorm/moving_variance"
2622 | attr {
2623 | key: "epsilon"
2624 | value {
2625 | f: 0.0010000000475
2626 | }
2627 | }
2628 | }
2629 | node {
2630 | name: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/Relu6"
2631 | op: "Relu6"
2632 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/BatchNorm/batchnorm/add_1"
2633 | }
2634 | node {
2635 | name: "FeatureExtractor/MobilenetV2/expanded_conv_15/project/Conv2D"
2636 | op: "Conv2D"
2637 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/Relu6"
2638 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/project/weights"
2639 | attr {
2640 | key: "dilations"
2641 | value {
2642 | list {
2643 | i: 1
2644 | i: 1
2645 | i: 1
2646 | i: 1
2647 | }
2648 | }
2649 | }
2650 | attr {
2651 | key: "padding"
2652 | value {
2653 | s: "SAME"
2654 | }
2655 | }
2656 | attr {
2657 | key: "strides"
2658 | value {
2659 | list {
2660 | i: 1
2661 | i: 1
2662 | i: 1
2663 | i: 1
2664 | }
2665 | }
2666 | }
2667 | }
2668 | node {
2669 | name: "FeatureExtractor/MobilenetV2/expanded_conv_15/project/BatchNorm/batchnorm/add_1"
2670 | op: "FusedBatchNorm"
2671 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/project/Conv2D"
2672 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/project/BatchNorm/gamma"
2673 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/project/BatchNorm/beta"
2674 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/project/BatchNorm/moving_mean"
2675 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/project/BatchNorm/moving_variance"
2676 | attr {
2677 | key: "epsilon"
2678 | value {
2679 | f: 0.0010000000475
2680 | }
2681 | }
2682 | }
2683 | node {
2684 | name: "FeatureExtractor/MobilenetV2/expanded_conv_15/add"
2685 | op: "Add"
2686 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/project/BatchNorm/batchnorm/add_1"
2687 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/input"
2688 | }
2689 | node {
2690 | name: "FeatureExtractor/MobilenetV2/expanded_conv_16/input"
2691 | op: "Identity"
2692 | input: "FeatureExtractor/MobilenetV2/expanded_conv_15/add"
2693 | }
2694 | node {
2695 | name: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/Conv2D"
2696 | op: "Conv2D"
2697 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/input"
2698 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/weights"
2699 | attr {
2700 | key: "dilations"
2701 | value {
2702 | list {
2703 | i: 1
2704 | i: 1
2705 | i: 1
2706 | i: 1
2707 | }
2708 | }
2709 | }
2710 | attr {
2711 | key: "padding"
2712 | value {
2713 | s: "SAME"
2714 | }
2715 | }
2716 | attr {
2717 | key: "strides"
2718 | value {
2719 | list {
2720 | i: 1
2721 | i: 1
2722 | i: 1
2723 | i: 1
2724 | }
2725 | }
2726 | }
2727 | }
2728 | node {
2729 | name: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/BatchNorm/batchnorm/add_1"
2730 | op: "FusedBatchNorm"
2731 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/Conv2D"
2732 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/BatchNorm/gamma"
2733 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/BatchNorm/beta"
2734 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/BatchNorm/moving_mean"
2735 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/BatchNorm/moving_variance"
2736 | attr {
2737 | key: "epsilon"
2738 | value {
2739 | f: 0.0010000000475
2740 | }
2741 | }
2742 | }
2743 | node {
2744 | name: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/Relu6"
2745 | op: "Relu6"
2746 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/BatchNorm/batchnorm/add_1"
2747 | }
2748 | node {
2749 | name: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/depthwise"
2750 | op: "DepthwiseConv2dNative"
2751 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/expand/Relu6"
2752 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/depthwise_weights"
2753 | attr {
2754 | key: "dilations"
2755 | value {
2756 | list {
2757 | i: 1
2758 | i: 1
2759 | i: 1
2760 | i: 1
2761 | }
2762 | }
2763 | }
2764 | attr {
2765 | key: "padding"
2766 | value {
2767 | s: "SAME"
2768 | }
2769 | }
2770 | attr {
2771 | key: "strides"
2772 | value {
2773 | list {
2774 | i: 1
2775 | i: 1
2776 | i: 1
2777 | i: 1
2778 | }
2779 | }
2780 | }
2781 | }
2782 | node {
2783 | name: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/BatchNorm/batchnorm/add_1"
2784 | op: "FusedBatchNorm"
2785 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/depthwise"
2786 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/BatchNorm/gamma"
2787 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/BatchNorm/beta"
2788 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/BatchNorm/moving_mean"
2789 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/BatchNorm/moving_variance"
2790 | attr {
2791 | key: "epsilon"
2792 | value {
2793 | f: 0.0010000000475
2794 | }
2795 | }
2796 | }
2797 | node {
2798 | name: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/Relu6"
2799 | op: "Relu6"
2800 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/BatchNorm/batchnorm/add_1"
2801 | }
2802 | node {
2803 | name: "FeatureExtractor/MobilenetV2/expanded_conv_16/project/Conv2D"
2804 | op: "Conv2D"
2805 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/Relu6"
2806 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/project/weights"
2807 | attr {
2808 | key: "dilations"
2809 | value {
2810 | list {
2811 | i: 1
2812 | i: 1
2813 | i: 1
2814 | i: 1
2815 | }
2816 | }
2817 | }
2818 | attr {
2819 | key: "padding"
2820 | value {
2821 | s: "SAME"
2822 | }
2823 | }
2824 | attr {
2825 | key: "strides"
2826 | value {
2827 | list {
2828 | i: 1
2829 | i: 1
2830 | i: 1
2831 | i: 1
2832 | }
2833 | }
2834 | }
2835 | }
2836 | node {
2837 | name: "FeatureExtractor/MobilenetV2/expanded_conv_16/project/BatchNorm/batchnorm/add_1"
2838 | op: "FusedBatchNorm"
2839 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/project/Conv2D"
2840 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/project/BatchNorm/gamma"
2841 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/project/BatchNorm/beta"
2842 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/project/BatchNorm/moving_mean"
2843 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/project/BatchNorm/moving_variance"
2844 | attr {
2845 | key: "epsilon"
2846 | value {
2847 | f: 0.0010000000475
2848 | }
2849 | }
2850 | }
2851 | node {
2852 | name: "FeatureExtractor/MobilenetV2/expanded_conv_16/output"
2853 | op: "Identity"
2854 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/project/BatchNorm/batchnorm/add_1"
2855 | }
2856 | node {
2857 | name: "FeatureExtractor/MobilenetV2/Conv_1/Conv2D"
2858 | op: "Conv2D"
2859 | input: "FeatureExtractor/MobilenetV2/expanded_conv_16/output"
2860 | input: "FeatureExtractor/MobilenetV2/Conv_1/weights"
2861 | attr {
2862 | key: "dilations"
2863 | value {
2864 | list {
2865 | i: 1
2866 | i: 1
2867 | i: 1
2868 | i: 1
2869 | }
2870 | }
2871 | }
2872 | attr {
2873 | key: "padding"
2874 | value {
2875 | s: "SAME"
2876 | }
2877 | }
2878 | attr {
2879 | key: "strides"
2880 | value {
2881 | list {
2882 | i: 1
2883 | i: 1
2884 | i: 1
2885 | i: 1
2886 | }
2887 | }
2888 | }
2889 | }
2890 | node {
2891 | name: "FeatureExtractor/MobilenetV2/Conv_1/BatchNorm/batchnorm/add_1"
2892 | op: "FusedBatchNorm"
2893 | input: "FeatureExtractor/MobilenetV2/Conv_1/Conv2D"
2894 | input: "FeatureExtractor/MobilenetV2/Conv_1/BatchNorm/gamma"
2895 | input: "FeatureExtractor/MobilenetV2/Conv_1/BatchNorm/beta"
2896 | input: "FeatureExtractor/MobilenetV2/Conv_1/BatchNorm/moving_mean"
2897 | input: "FeatureExtractor/MobilenetV2/Conv_1/BatchNorm/moving_variance"
2898 | attr {
2899 | key: "epsilon"
2900 | value {
2901 | f: 0.0010000000475
2902 | }
2903 | }
2904 | }
2905 | node {
2906 | name: "FeatureExtractor/MobilenetV2/Conv_1/Relu6"
2907 | op: "Relu6"
2908 | input: "FeatureExtractor/MobilenetV2/Conv_1/BatchNorm/batchnorm/add_1"
2909 | }
2910 | node {
2911 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/Conv2D"
2912 | op: "Conv2D"
2913 | input: "FeatureExtractor/MobilenetV2/Conv_1/Relu6"
2914 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/weights"
2915 | attr {
2916 | key: "dilations"
2917 | value {
2918 | list {
2919 | i: 1
2920 | i: 1
2921 | i: 1
2922 | i: 1
2923 | }
2924 | }
2925 | }
2926 | attr {
2927 | key: "padding"
2928 | value {
2929 | s: "SAME"
2930 | }
2931 | }
2932 | attr {
2933 | key: "strides"
2934 | value {
2935 | list {
2936 | i: 1
2937 | i: 1
2938 | i: 1
2939 | i: 1
2940 | }
2941 | }
2942 | }
2943 | }
2944 | node {
2945 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/add_1"
2946 | op: "FusedBatchNorm"
2947 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/Conv2D"
2948 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/BatchNorm/gamma"
2949 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/BatchNorm/beta"
2950 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/BatchNorm/moving_mean"
2951 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/BatchNorm/moving_variance"
2952 | attr {
2953 | key: "epsilon"
2954 | value {
2955 | f: 0.0010000000475
2956 | }
2957 | }
2958 | }
2959 | node {
2960 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/Relu6"
2961 | op: "Relu6"
2962 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/add_1"
2963 | }
2964 | node {
2965 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/depthwise"
2966 | op: "DepthwiseConv2dNative"
2967 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/Relu6"
2968 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/depthwise_weights"
2969 | attr {
2970 | key: "dilations"
2971 | value {
2972 | list {
2973 | i: 1
2974 | i: 1
2975 | i: 1
2976 | i: 1
2977 | }
2978 | }
2979 | }
2980 | attr {
2981 | key: "padding"
2982 | value {
2983 | s: "SAME"
2984 | }
2985 | }
2986 | attr {
2987 | key: "strides"
2988 | value {
2989 | list {
2990 | i: 1
2991 | i: 2
2992 | i: 2
2993 | i: 1
2994 | }
2995 | }
2996 | }
2997 | }
2998 | node {
2999 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/BatchNorm/batchnorm/add_1"
3000 | op: "FusedBatchNorm"
3001 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/depthwise"
3002 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/BatchNorm/gamma"
3003 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/BatchNorm/beta"
3004 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/BatchNorm/moving_mean"
3005 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/BatchNorm/moving_variance"
3006 | attr {
3007 | key: "epsilon"
3008 | value {
3009 | f: 0.0010000000475
3010 | }
3011 | }
3012 | }
3013 | node {
3014 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/Relu6"
3015 | op: "Relu6"
3016 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/BatchNorm/batchnorm/add_1"
3017 | }
3018 | node {
3019 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/Conv2D"
3020 | op: "Conv2D"
3021 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/Relu6"
3022 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/weights"
3023 | attr {
3024 | key: "dilations"
3025 | value {
3026 | list {
3027 | i: 1
3028 | i: 1
3029 | i: 1
3030 | i: 1
3031 | }
3032 | }
3033 | }
3034 | attr {
3035 | key: "padding"
3036 | value {
3037 | s: "SAME"
3038 | }
3039 | }
3040 | attr {
3041 | key: "strides"
3042 | value {
3043 | list {
3044 | i: 1
3045 | i: 1
3046 | i: 1
3047 | i: 1
3048 | }
3049 | }
3050 | }
3051 | }
3052 | node {
3053 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/add_1"
3054 | op: "FusedBatchNorm"
3055 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/Conv2D"
3056 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/BatchNorm/gamma"
3057 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/BatchNorm/beta"
3058 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/BatchNorm/moving_mean"
3059 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/BatchNorm/moving_variance"
3060 | attr {
3061 | key: "epsilon"
3062 | value {
3063 | f: 0.0010000000475
3064 | }
3065 | }
3066 | }
3067 | node {
3068 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/Relu6"
3069 | op: "Relu6"
3070 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/add_1"
3071 | }
3072 | node {
3073 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/Conv2D"
3074 | op: "Conv2D"
3075 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/Relu6"
3076 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/weights"
3077 | attr {
3078 | key: "dilations"
3079 | value {
3080 | list {
3081 | i: 1
3082 | i: 1
3083 | i: 1
3084 | i: 1
3085 | }
3086 | }
3087 | }
3088 | attr {
3089 | key: "padding"
3090 | value {
3091 | s: "SAME"
3092 | }
3093 | }
3094 | attr {
3095 | key: "strides"
3096 | value {
3097 | list {
3098 | i: 1
3099 | i: 1
3100 | i: 1
3101 | i: 1
3102 | }
3103 | }
3104 | }
3105 | }
3106 | node {
3107 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/add_1"
3108 | op: "FusedBatchNorm"
3109 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/Conv2D"
3110 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/BatchNorm/gamma"
3111 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/BatchNorm/beta"
3112 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/BatchNorm/moving_mean"
3113 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/BatchNorm/moving_variance"
3114 | attr {
3115 | key: "epsilon"
3116 | value {
3117 | f: 0.0010000000475
3118 | }
3119 | }
3120 | }
3121 | node {
3122 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/Relu6"
3123 | op: "Relu6"
3124 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/add_1"
3125 | }
3126 | node {
3127 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/depthwise"
3128 | op: "DepthwiseConv2dNative"
3129 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/Relu6"
3130 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/depthwise_weights"
3131 | attr {
3132 | key: "dilations"
3133 | value {
3134 | list {
3135 | i: 1
3136 | i: 1
3137 | i: 1
3138 | i: 1
3139 | }
3140 | }
3141 | }
3142 | attr {
3143 | key: "padding"
3144 | value {
3145 | s: "SAME"
3146 | }
3147 | }
3148 | attr {
3149 | key: "strides"
3150 | value {
3151 | list {
3152 | i: 1
3153 | i: 2
3154 | i: 2
3155 | i: 1
3156 | }
3157 | }
3158 | }
3159 | }
3160 | node {
3161 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/BatchNorm/batchnorm/add_1"
3162 | op: "FusedBatchNorm"
3163 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/depthwise"
3164 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/BatchNorm/gamma"
3165 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/BatchNorm/beta"
3166 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/BatchNorm/moving_mean"
3167 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/BatchNorm/moving_variance"
3168 | attr {
3169 | key: "epsilon"
3170 | value {
3171 | f: 0.0010000000475
3172 | }
3173 | }
3174 | }
3175 | node {
3176 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/Relu6"
3177 | op: "Relu6"
3178 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/BatchNorm/batchnorm/add_1"
3179 | }
3180 | node {
3181 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/Conv2D"
3182 | op: "Conv2D"
3183 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/Relu6"
3184 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/weights"
3185 | attr {
3186 | key: "dilations"
3187 | value {
3188 | list {
3189 | i: 1
3190 | i: 1
3191 | i: 1
3192 | i: 1
3193 | }
3194 | }
3195 | }
3196 | attr {
3197 | key: "padding"
3198 | value {
3199 | s: "SAME"
3200 | }
3201 | }
3202 | attr {
3203 | key: "strides"
3204 | value {
3205 | list {
3206 | i: 1
3207 | i: 1
3208 | i: 1
3209 | i: 1
3210 | }
3211 | }
3212 | }
3213 | }
3214 | node {
3215 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/BatchNorm/batchnorm/add_1"
3216 | op: "FusedBatchNorm"
3217 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/Conv2D"
3218 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/BatchNorm/gamma"
3219 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/BatchNorm/beta"
3220 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/BatchNorm/moving_mean"
3221 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/BatchNorm/moving_variance"
3222 | attr {
3223 | key: "epsilon"
3224 | value {
3225 | f: 0.0010000000475
3226 | }
3227 | }
3228 | }
3229 | node {
3230 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/Relu6"
3231 | op: "Relu6"
3232 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/BatchNorm/batchnorm/add_1"
3233 | }
3234 | node {
3235 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/Conv2D"
3236 | op: "Conv2D"
3237 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/Relu6"
3238 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/weights"
3239 | attr {
3240 | key: "dilations"
3241 | value {
3242 | list {
3243 | i: 1
3244 | i: 1
3245 | i: 1
3246 | i: 1
3247 | }
3248 | }
3249 | }
3250 | attr {
3251 | key: "padding"
3252 | value {
3253 | s: "SAME"
3254 | }
3255 | }
3256 | attr {
3257 | key: "strides"
3258 | value {
3259 | list {
3260 | i: 1
3261 | i: 1
3262 | i: 1
3263 | i: 1
3264 | }
3265 | }
3266 | }
3267 | }
3268 | node {
3269 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/BatchNorm/batchnorm/add_1"
3270 | op: "FusedBatchNorm"
3271 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/Conv2D"
3272 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/BatchNorm/gamma"
3273 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/BatchNorm/beta"
3274 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/BatchNorm/moving_mean"
3275 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/BatchNorm/moving_variance"
3276 | attr {
3277 | key: "epsilon"
3278 | value {
3279 | f: 0.0010000000475
3280 | }
3281 | }
3282 | }
3283 | node {
3284 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/Relu6"
3285 | op: "Relu6"
3286 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/BatchNorm/batchnorm/add_1"
3287 | }
3288 | node {
3289 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/depthwise"
3290 | op: "DepthwiseConv2dNative"
3291 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/Relu6"
3292 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/depthwise_weights"
3293 | attr {
3294 | key: "dilations"
3295 | value {
3296 | list {
3297 | i: 1
3298 | i: 1
3299 | i: 1
3300 | i: 1
3301 | }
3302 | }
3303 | }
3304 | attr {
3305 | key: "padding"
3306 | value {
3307 | s: "SAME"
3308 | }
3309 | }
3310 | attr {
3311 | key: "strides"
3312 | value {
3313 | list {
3314 | i: 1
3315 | i: 2
3316 | i: 2
3317 | i: 1
3318 | }
3319 | }
3320 | }
3321 | }
3322 | node {
3323 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/BatchNorm/batchnorm/add_1"
3324 | op: "FusedBatchNorm"
3325 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/depthwise"
3326 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/BatchNorm/gamma"
3327 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/BatchNorm/beta"
3328 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/BatchNorm/moving_mean"
3329 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/BatchNorm/moving_variance"
3330 | attr {
3331 | key: "epsilon"
3332 | value {
3333 | f: 0.0010000000475
3334 | }
3335 | }
3336 | }
3337 | node {
3338 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/Relu6"
3339 | op: "Relu6"
3340 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/BatchNorm/batchnorm/add_1"
3341 | }
3342 | node {
3343 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/Conv2D"
3344 | op: "Conv2D"
3345 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/Relu6"
3346 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/weights"
3347 | attr {
3348 | key: "dilations"
3349 | value {
3350 | list {
3351 | i: 1
3352 | i: 1
3353 | i: 1
3354 | i: 1
3355 | }
3356 | }
3357 | }
3358 | attr {
3359 | key: "padding"
3360 | value {
3361 | s: "SAME"
3362 | }
3363 | }
3364 | attr {
3365 | key: "strides"
3366 | value {
3367 | list {
3368 | i: 1
3369 | i: 1
3370 | i: 1
3371 | i: 1
3372 | }
3373 | }
3374 | }
3375 | }
3376 | node {
3377 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/BatchNorm/batchnorm/add_1"
3378 | op: "FusedBatchNorm"
3379 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/Conv2D"
3380 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/BatchNorm/gamma"
3381 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/BatchNorm/beta"
3382 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/BatchNorm/moving_mean"
3383 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/BatchNorm/moving_variance"
3384 | attr {
3385 | key: "epsilon"
3386 | value {
3387 | f: 0.0010000000475
3388 | }
3389 | }
3390 | }
3391 | node {
3392 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/Relu6"
3393 | op: "Relu6"
3394 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/BatchNorm/batchnorm/add_1"
3395 | }
3396 | node {
3397 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/Conv2D"
3398 | op: "Conv2D"
3399 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/Relu6"
3400 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/weights"
3401 | attr {
3402 | key: "dilations"
3403 | value {
3404 | list {
3405 | i: 1
3406 | i: 1
3407 | i: 1
3408 | i: 1
3409 | }
3410 | }
3411 | }
3412 | attr {
3413 | key: "padding"
3414 | value {
3415 | s: "SAME"
3416 | }
3417 | }
3418 | attr {
3419 | key: "strides"
3420 | value {
3421 | list {
3422 | i: 1
3423 | i: 1
3424 | i: 1
3425 | i: 1
3426 | }
3427 | }
3428 | }
3429 | }
3430 | node {
3431 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/BatchNorm/batchnorm/add_1"
3432 | op: "FusedBatchNorm"
3433 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/Conv2D"
3434 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/BatchNorm/gamma"
3435 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/BatchNorm/beta"
3436 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/BatchNorm/moving_mean"
3437 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/BatchNorm/moving_variance"
3438 | attr {
3439 | key: "epsilon"
3440 | value {
3441 | f: 0.0010000000475
3442 | }
3443 | }
3444 | }
3445 | node {
3446 | name: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/Relu6"
3447 | op: "Relu6"
3448 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/BatchNorm/batchnorm/add_1"
3449 | }
3450 | node {
3451 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/depthwise"
3452 | op: "DepthwiseConv2dNative"
3453 | input: "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/Relu6"
3454 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/depthwise_weights"
3455 | attr {
3456 | key: "dilations"
3457 | value {
3458 | list {
3459 | i: 1
3460 | i: 1
3461 | i: 1
3462 | i: 1
3463 | }
3464 | }
3465 | }
3466 | attr {
3467 | key: "padding"
3468 | value {
3469 | s: "SAME"
3470 | }
3471 | }
3472 | attr {
3473 | key: "strides"
3474 | value {
3475 | list {
3476 | i: 1
3477 | i: 2
3478 | i: 2
3479 | i: 1
3480 | }
3481 | }
3482 | }
3483 | }
3484 | node {
3485 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/BatchNorm/batchnorm/add_1"
3486 | op: "FusedBatchNorm"
3487 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/depthwise"
3488 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/BatchNorm/gamma"
3489 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/BatchNorm/beta"
3490 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/BatchNorm/moving_mean"
3491 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/BatchNorm/moving_variance"
3492 | attr {
3493 | key: "epsilon"
3494 | value {
3495 | f: 0.0010000000475
3496 | }
3497 | }
3498 | }
3499 | node {
3500 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/Relu6"
3501 | op: "Relu6"
3502 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/BatchNorm/batchnorm/add_1"
3503 | }
3504 | node {
3505 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/Conv2D"
3506 | op: "Conv2D"
3507 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/Relu6"
3508 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/weights"
3509 | attr {
3510 | key: "dilations"
3511 | value {
3512 | list {
3513 | i: 1
3514 | i: 1
3515 | i: 1
3516 | i: 1
3517 | }
3518 | }
3519 | }
3520 | attr {
3521 | key: "padding"
3522 | value {
3523 | s: "SAME"
3524 | }
3525 | }
3526 | attr {
3527 | key: "strides"
3528 | value {
3529 | list {
3530 | i: 1
3531 | i: 1
3532 | i: 1
3533 | i: 1
3534 | }
3535 | }
3536 | }
3537 | }
3538 | node {
3539 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/BatchNorm/batchnorm/add_1"
3540 | op: "FusedBatchNorm"
3541 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/Conv2D"
3542 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/BatchNorm/gamma"
3543 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/BatchNorm/beta"
3544 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/BatchNorm/moving_mean"
3545 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/BatchNorm/moving_variance"
3546 | attr {
3547 | key: "epsilon"
3548 | value {
3549 | f: 0.0010000000475
3550 | }
3551 | }
3552 | }
3553 | node {
3554 | name: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/Relu6"
3555 | op: "Relu6"
3556 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/BatchNorm/batchnorm/add_1"
3557 | }
3558 | node {
3559 | name: "BoxPredictor_0/BoxEncodingPredictor/Conv2D"
3560 | op: "Conv2D"
3561 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/Relu6"
3562 | input: "BoxPredictor_0/BoxEncodingPredictor/weights"
3563 | attr {
3564 | key: "dilations"
3565 | value {
3566 | list {
3567 | i: 1
3568 | i: 1
3569 | i: 1
3570 | i: 1
3571 | }
3572 | }
3573 | }
3574 | attr {
3575 | key: "loc_pred_transposed"
3576 | value {
3577 | b: true
3578 | }
3579 | }
3580 | attr {
3581 | key: "padding"
3582 | value {
3583 | s: "SAME"
3584 | }
3585 | }
3586 | attr {
3587 | key: "strides"
3588 | value {
3589 | list {
3590 | i: 1
3591 | i: 1
3592 | i: 1
3593 | i: 1
3594 | }
3595 | }
3596 | }
3597 | }
3598 | node {
3599 | name: "BoxPredictor_0/BoxEncodingPredictor/BiasAdd"
3600 | op: "BiasAdd"
3601 | input: "BoxPredictor_0/BoxEncodingPredictor/Conv2D"
3602 | input: "BoxPredictor_0/BoxEncodingPredictor/biases"
3603 | }
3604 | node {
3605 | name: "BoxPredictor_0/ClassPredictor/Conv2D"
3606 | op: "Conv2D"
3607 | input: "FeatureExtractor/MobilenetV2/expanded_conv_13/expand/Relu6"
3608 | input: "BoxPredictor_0/ClassPredictor/weights"
3609 | attr {
3610 | key: "dilations"
3611 | value {
3612 | list {
3613 | i: 1
3614 | i: 1
3615 | i: 1
3616 | i: 1
3617 | }
3618 | }
3619 | }
3620 | attr {
3621 | key: "padding"
3622 | value {
3623 | s: "SAME"
3624 | }
3625 | }
3626 | attr {
3627 | key: "strides"
3628 | value {
3629 | list {
3630 | i: 1
3631 | i: 1
3632 | i: 1
3633 | i: 1
3634 | }
3635 | }
3636 | }
3637 | }
3638 | node {
3639 | name: "BoxPredictor_0/ClassPredictor/BiasAdd"
3640 | op: "BiasAdd"
3641 | input: "BoxPredictor_0/ClassPredictor/Conv2D"
3642 | input: "BoxPredictor_0/ClassPredictor/biases"
3643 | }
3644 | node {
3645 | name: "BoxPredictor_1/BoxEncodingPredictor/Conv2D"
3646 | op: "Conv2D"
3647 | input: "FeatureExtractor/MobilenetV2/Conv_1/Relu6"
3648 | input: "BoxPredictor_1/BoxEncodingPredictor/weights"
3649 | attr {
3650 | key: "dilations"
3651 | value {
3652 | list {
3653 | i: 1
3654 | i: 1
3655 | i: 1
3656 | i: 1
3657 | }
3658 | }
3659 | }
3660 | attr {
3661 | key: "loc_pred_transposed"
3662 | value {
3663 | b: true
3664 | }
3665 | }
3666 | attr {
3667 | key: "padding"
3668 | value {
3669 | s: "SAME"
3670 | }
3671 | }
3672 | attr {
3673 | key: "strides"
3674 | value {
3675 | list {
3676 | i: 1
3677 | i: 1
3678 | i: 1
3679 | i: 1
3680 | }
3681 | }
3682 | }
3683 | }
3684 | node {
3685 | name: "BoxPredictor_1/BoxEncodingPredictor/BiasAdd"
3686 | op: "BiasAdd"
3687 | input: "BoxPredictor_1/BoxEncodingPredictor/Conv2D"
3688 | input: "BoxPredictor_1/BoxEncodingPredictor/biases"
3689 | }
3690 | node {
3691 | name: "BoxPredictor_1/ClassPredictor/Conv2D"
3692 | op: "Conv2D"
3693 | input: "FeatureExtractor/MobilenetV2/Conv_1/Relu6"
3694 | input: "BoxPredictor_1/ClassPredictor/weights"
3695 | attr {
3696 | key: "dilations"
3697 | value {
3698 | list {
3699 | i: 1
3700 | i: 1
3701 | i: 1
3702 | i: 1
3703 | }
3704 | }
3705 | }
3706 | attr {
3707 | key: "padding"
3708 | value {
3709 | s: "SAME"
3710 | }
3711 | }
3712 | attr {
3713 | key: "strides"
3714 | value {
3715 | list {
3716 | i: 1
3717 | i: 1
3718 | i: 1
3719 | i: 1
3720 | }
3721 | }
3722 | }
3723 | }
3724 | node {
3725 | name: "BoxPredictor_1/ClassPredictor/BiasAdd"
3726 | op: "BiasAdd"
3727 | input: "BoxPredictor_1/ClassPredictor/Conv2D"
3728 | input: "BoxPredictor_1/ClassPredictor/biases"
3729 | }
3730 | node {
3731 | name: "BoxPredictor_2/BoxEncodingPredictor/Conv2D"
3732 | op: "Conv2D"
3733 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/Relu6"
3734 | input: "BoxPredictor_2/BoxEncodingPredictor/weights"
3735 | attr {
3736 | key: "dilations"
3737 | value {
3738 | list {
3739 | i: 1
3740 | i: 1
3741 | i: 1
3742 | i: 1
3743 | }
3744 | }
3745 | }
3746 | attr {
3747 | key: "loc_pred_transposed"
3748 | value {
3749 | b: true
3750 | }
3751 | }
3752 | attr {
3753 | key: "padding"
3754 | value {
3755 | s: "SAME"
3756 | }
3757 | }
3758 | attr {
3759 | key: "strides"
3760 | value {
3761 | list {
3762 | i: 1
3763 | i: 1
3764 | i: 1
3765 | i: 1
3766 | }
3767 | }
3768 | }
3769 | }
3770 | node {
3771 | name: "BoxPredictor_2/BoxEncodingPredictor/BiasAdd"
3772 | op: "BiasAdd"
3773 | input: "BoxPredictor_2/BoxEncodingPredictor/Conv2D"
3774 | input: "BoxPredictor_2/BoxEncodingPredictor/biases"
3775 | }
3776 | node {
3777 | name: "BoxPredictor_2/ClassPredictor/Conv2D"
3778 | op: "Conv2D"
3779 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/Relu6"
3780 | input: "BoxPredictor_2/ClassPredictor/weights"
3781 | attr {
3782 | key: "dilations"
3783 | value {
3784 | list {
3785 | i: 1
3786 | i: 1
3787 | i: 1
3788 | i: 1
3789 | }
3790 | }
3791 | }
3792 | attr {
3793 | key: "padding"
3794 | value {
3795 | s: "SAME"
3796 | }
3797 | }
3798 | attr {
3799 | key: "strides"
3800 | value {
3801 | list {
3802 | i: 1
3803 | i: 1
3804 | i: 1
3805 | i: 1
3806 | }
3807 | }
3808 | }
3809 | }
3810 | node {
3811 | name: "BoxPredictor_2/ClassPredictor/BiasAdd"
3812 | op: "BiasAdd"
3813 | input: "BoxPredictor_2/ClassPredictor/Conv2D"
3814 | input: "BoxPredictor_2/ClassPredictor/biases"
3815 | }
3816 | node {
3817 | name: "BoxPredictor_3/BoxEncodingPredictor/Conv2D"
3818 | op: "Conv2D"
3819 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/Relu6"
3820 | input: "BoxPredictor_3/BoxEncodingPredictor/weights"
3821 | attr {
3822 | key: "dilations"
3823 | value {
3824 | list {
3825 | i: 1
3826 | i: 1
3827 | i: 1
3828 | i: 1
3829 | }
3830 | }
3831 | }
3832 | attr {
3833 | key: "loc_pred_transposed"
3834 | value {
3835 | b: true
3836 | }
3837 | }
3838 | attr {
3839 | key: "padding"
3840 | value {
3841 | s: "SAME"
3842 | }
3843 | }
3844 | attr {
3845 | key: "strides"
3846 | value {
3847 | list {
3848 | i: 1
3849 | i: 1
3850 | i: 1
3851 | i: 1
3852 | }
3853 | }
3854 | }
3855 | }
3856 | node {
3857 | name: "BoxPredictor_3/BoxEncodingPredictor/BiasAdd"
3858 | op: "BiasAdd"
3859 | input: "BoxPredictor_3/BoxEncodingPredictor/Conv2D"
3860 | input: "BoxPredictor_3/BoxEncodingPredictor/biases"
3861 | }
3862 | node {
3863 | name: "BoxPredictor_3/ClassPredictor/Conv2D"
3864 | op: "Conv2D"
3865 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/Relu6"
3866 | input: "BoxPredictor_3/ClassPredictor/weights"
3867 | attr {
3868 | key: "dilations"
3869 | value {
3870 | list {
3871 | i: 1
3872 | i: 1
3873 | i: 1
3874 | i: 1
3875 | }
3876 | }
3877 | }
3878 | attr {
3879 | key: "padding"
3880 | value {
3881 | s: "SAME"
3882 | }
3883 | }
3884 | attr {
3885 | key: "strides"
3886 | value {
3887 | list {
3888 | i: 1
3889 | i: 1
3890 | i: 1
3891 | i: 1
3892 | }
3893 | }
3894 | }
3895 | }
3896 | node {
3897 | name: "BoxPredictor_3/ClassPredictor/BiasAdd"
3898 | op: "BiasAdd"
3899 | input: "BoxPredictor_3/ClassPredictor/Conv2D"
3900 | input: "BoxPredictor_3/ClassPredictor/biases"
3901 | }
3902 | node {
3903 | name: "BoxPredictor_4/BoxEncodingPredictor/Conv2D"
3904 | op: "Conv2D"
3905 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/Relu6"
3906 | input: "BoxPredictor_4/BoxEncodingPredictor/weights"
3907 | attr {
3908 | key: "dilations"
3909 | value {
3910 | list {
3911 | i: 1
3912 | i: 1
3913 | i: 1
3914 | i: 1
3915 | }
3916 | }
3917 | }
3918 | attr {
3919 | key: "loc_pred_transposed"
3920 | value {
3921 | b: true
3922 | }
3923 | }
3924 | attr {
3925 | key: "padding"
3926 | value {
3927 | s: "SAME"
3928 | }
3929 | }
3930 | attr {
3931 | key: "strides"
3932 | value {
3933 | list {
3934 | i: 1
3935 | i: 1
3936 | i: 1
3937 | i: 1
3938 | }
3939 | }
3940 | }
3941 | }
3942 | node {
3943 | name: "BoxPredictor_4/BoxEncodingPredictor/BiasAdd"
3944 | op: "BiasAdd"
3945 | input: "BoxPredictor_4/BoxEncodingPredictor/Conv2D"
3946 | input: "BoxPredictor_4/BoxEncodingPredictor/biases"
3947 | }
3948 | node {
3949 | name: "BoxPredictor_4/ClassPredictor/Conv2D"
3950 | op: "Conv2D"
3951 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/Relu6"
3952 | input: "BoxPredictor_4/ClassPredictor/weights"
3953 | attr {
3954 | key: "dilations"
3955 | value {
3956 | list {
3957 | i: 1
3958 | i: 1
3959 | i: 1
3960 | i: 1
3961 | }
3962 | }
3963 | }
3964 | attr {
3965 | key: "padding"
3966 | value {
3967 | s: "SAME"
3968 | }
3969 | }
3970 | attr {
3971 | key: "strides"
3972 | value {
3973 | list {
3974 | i: 1
3975 | i: 1
3976 | i: 1
3977 | i: 1
3978 | }
3979 | }
3980 | }
3981 | }
3982 | node {
3983 | name: "BoxPredictor_4/ClassPredictor/BiasAdd"
3984 | op: "BiasAdd"
3985 | input: "BoxPredictor_4/ClassPredictor/Conv2D"
3986 | input: "BoxPredictor_4/ClassPredictor/biases"
3987 | }
3988 | node {
3989 | name: "BoxPredictor_5/BoxEncodingPredictor/Conv2D"
3990 | op: "Conv2D"
3991 | input: "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/Relu6"
3992 | input: "BoxPredictor_5/BoxEncodingPredictor/weights"
3993 | attr {
3994 | key: "dilations"
3995 | value {
3996 | list {
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