apocaliss92 commited on
Commit
157578e
·
verified ·
1 Parent(s): 7cb9ddc

remove animalClassification/animal-cls-v1 (GPL-encumbered Animals-10 weights; superseded by permissive animal-classifier)

Browse files
animalClassification/animal-cls-v1/animal-cls-v1-labels.json DELETED
@@ -1,35 +0,0 @@
1
- {
2
- "labels": [
3
- "butterfly",
4
- "cat",
5
- "chicken",
6
- "cow",
7
- "dog",
8
- "elephant",
9
- "horse",
10
- "sheep",
11
- "spider",
12
- "squirrel"
13
- ],
14
- "input_size": 224,
15
- "num_classes": 10,
16
- "preprocess": {
17
- "normalize": "imagenet",
18
- "mean": [
19
- 0.485,
20
- 0.456,
21
- 0.406
22
- ],
23
- "std": [
24
- 0.229,
25
- 0.224,
26
- 0.225
27
- ],
28
- "channels": "RGB",
29
- "layout": "NCHW"
30
- },
31
- "arch": "resnet18",
32
- "source": "MichaelMM2000/animals10-resnet",
33
- "license": "MIT",
34
- "output": "logits (apply softmax)"
35
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
animalClassification/animal-cls-v1/coreml/animal-cls-v1-labels.json DELETED
@@ -1,35 +0,0 @@
1
- {
2
- "labels": [
3
- "butterfly",
4
- "cat",
5
- "chicken",
6
- "cow",
7
- "dog",
8
- "elephant",
9
- "horse",
10
- "sheep",
11
- "spider",
12
- "squirrel"
13
- ],
14
- "input_size": 224,
15
- "num_classes": 10,
16
- "preprocess": {
17
- "normalize": "imagenet",
18
- "mean": [
19
- 0.485,
20
- 0.456,
21
- 0.406
22
- ],
23
- "std": [
24
- 0.229,
25
- 0.224,
26
- 0.225
27
- ],
28
- "channels": "RGB",
29
- "layout": "NCHW"
30
- },
31
- "arch": "resnet18",
32
- "source": "MichaelMM2000/animals10-resnet",
33
- "license": "MIT",
34
- "output": "logits (apply softmax)"
35
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
animalClassification/animal-cls-v1/coreml/animal-cls-v1.mlpackage/Data/com.apple.CoreML/model.mlmodel DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:fa3fb006f9b312817c18f103b28c3d1c0575f4bec20f3b3c1c618b666b5522a7
3
- size 31463
 
 
 
 
animalClassification/animal-cls-v1/coreml/animal-cls-v1.mlpackage/Data/com.apple.CoreML/weights/weight.bin DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:35b29a2c5c2a2b740ffe0cd30e71414b7ef48f55aa872d66921dc5c1d57d6b26
3
- size 22356436
 
 
 
 
animalClassification/animal-cls-v1/coreml/animal-cls-v1.mlpackage/Manifest.json DELETED
@@ -1,18 +0,0 @@
1
- {
2
- "fileFormatVersion": "1.0.0",
3
- "itemInfoEntries": {
4
- "6F88DD0A-DF0C-4246-87DC-AAEA4213B54A": {
5
- "author": "com.apple.CoreML",
6
- "description": "CoreML Model Specification",
7
- "name": "model.mlmodel",
8
- "path": "com.apple.CoreML/model.mlmodel"
9
- },
10
- "A4D44BE7-19F6-44C8-A250-6108EE36B255": {
11
- "author": "com.apple.CoreML",
12
- "description": "CoreML Model Weights",
13
- "name": "weights",
14
- "path": "com.apple.CoreML/weights"
15
- }
16
- },
17
- "rootModelIdentifier": "6F88DD0A-DF0C-4246-87DC-AAEA4213B54A"
18
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
animalClassification/animal-cls-v1/onnx/animal-cls-v1-labels.json DELETED
@@ -1,35 +0,0 @@
1
- {
2
- "labels": [
3
- "butterfly",
4
- "cat",
5
- "chicken",
6
- "cow",
7
- "dog",
8
- "elephant",
9
- "horse",
10
- "sheep",
11
- "spider",
12
- "squirrel"
13
- ],
14
- "input_size": 224,
15
- "num_classes": 10,
16
- "preprocess": {
17
- "normalize": "imagenet",
18
- "mean": [
19
- 0.485,
20
- 0.456,
21
- 0.406
22
- ],
23
- "std": [
24
- 0.229,
25
- 0.224,
26
- 0.225
27
- ],
28
- "channels": "RGB",
29
- "layout": "NCHW"
30
- },
31
- "arch": "resnet18",
32
- "source": "MichaelMM2000/animals10-resnet",
33
- "license": "MIT",
34
- "output": "logits (apply softmax)"
35
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
animalClassification/animal-cls-v1/onnx/animal-cls-v1.onnx DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:48731f9848c9077651e704df32a7677b17030341520bfe58349ccc07cca04278
3
- size 44717227
 
 
 
 
animalClassification/animal-cls-v1/openvino/animal-cls-v1-labels.json DELETED
@@ -1,35 +0,0 @@
1
- {
2
- "labels": [
3
- "butterfly",
4
- "cat",
5
- "chicken",
6
- "cow",
7
- "dog",
8
- "elephant",
9
- "horse",
10
- "sheep",
11
- "spider",
12
- "squirrel"
13
- ],
14
- "input_size": 224,
15
- "num_classes": 10,
16
- "preprocess": {
17
- "normalize": "imagenet",
18
- "mean": [
19
- 0.485,
20
- 0.456,
21
- 0.406
22
- ],
23
- "std": [
24
- 0.229,
25
- 0.224,
26
- 0.225
27
- ],
28
- "channels": "RGB",
29
- "layout": "NCHW"
30
- },
31
- "arch": "resnet18",
32
- "source": "MichaelMM2000/animals10-resnet",
33
- "license": "MIT",
34
- "output": "logits (apply softmax)"
35
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
animalClassification/animal-cls-v1/openvino/animal-cls-v1.bin DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:eddb683aeb9f2a3a893c75ba8267a938fe5d2a115e517d1b0920e89f96cb58fc
3
- size 22353736
 
 
 
 
animalClassification/animal-cls-v1/openvino/animal-cls-v1.xml DELETED
@@ -1,3188 +0,0 @@
1
- <?xml version="1.0"?>
2
- <net name="onnx_Frontend_IR" version="11">
3
- <layers>
4
- <layer id="0" name="input" type="Parameter" version="opset1">
5
- <data shape="?,3,224,224" element_type="f32" />
6
- <output>
7
- <port id="0" precision="FP32" names="input">
8
- <dim>-1</dim>
9
- <dim>3</dim>
10
- <dim>224</dim>
11
- <dim>224</dim>
12
- </port>
13
- </output>
14
- </layer>
15
- <layer id="1" name="onnx::Conv_193_compressed" type="Const" version="opset1">
16
- <data element_type="f16" shape="64, 3, 7, 7" offset="0" size="18816" />
17
- <output>
18
- <port id="0" precision="FP16" names="onnx::Conv_193">
19
- <dim>64</dim>
20
- <dim>3</dim>
21
- <dim>7</dim>
22
- <dim>7</dim>
23
- </port>
24
- </output>
25
- </layer>
26
- <layer id="2" name="onnx::Conv_193" type="Convert" version="opset1">
27
- <data destination_type="f32" />
28
- <rt_info>
29
- <attribute name="decompression" version="0" />
30
- </rt_info>
31
- <input>
32
- <port id="0" precision="FP16">
33
- <dim>64</dim>
34
- <dim>3</dim>
35
- <dim>7</dim>
36
- <dim>7</dim>
37
- </port>
38
- </input>
39
- <output>
40
- <port id="1" precision="FP32">
41
- <dim>64</dim>
42
- <dim>3</dim>
43
- <dim>7</dim>
44
- <dim>7</dim>
45
- </port>
46
- </output>
47
- </layer>
48
- <layer id="3" name="/conv1/Conv/WithoutBiases" type="Convolution" version="opset1">
49
- <data strides="2, 2" dilations="1, 1" pads_begin="3, 3" pads_end="3, 3" auto_pad="explicit" />
50
- <input>
51
- <port id="0" precision="FP32">
52
- <dim>-1</dim>
53
- <dim>3</dim>
54
- <dim>224</dim>
55
- <dim>224</dim>
56
- </port>
57
- <port id="1" precision="FP32">
58
- <dim>64</dim>
59
- <dim>3</dim>
60
- <dim>7</dim>
61
- <dim>7</dim>
62
- </port>
63
- </input>
64
- <output>
65
- <port id="2" precision="FP32">
66
- <dim>-1</dim>
67
- <dim>64</dim>
68
- <dim>112</dim>
69
- <dim>112</dim>
70
- </port>
71
- </output>
72
- </layer>
73
- <layer id="4" name="Reshape_15_compressed" type="Const" version="opset1">
74
- <data element_type="f16" shape="1, 64, 1, 1" offset="18816" size="128" />
75
- <output>
76
- <port id="0" precision="FP16">
77
- <dim>1</dim>
78
- <dim>64</dim>
79
- <dim>1</dim>
80
- <dim>1</dim>
81
- </port>
82
- </output>
83
- </layer>
84
- <layer id="5" name="Reshape_15" type="Convert" version="opset1">
85
- <data destination_type="f32" />
86
- <rt_info>
87
- <attribute name="decompression" version="0" />
88
- </rt_info>
89
- <input>
90
- <port id="0" precision="FP16">
91
- <dim>1</dim>
92
- <dim>64</dim>
93
- <dim>1</dim>
94
- <dim>1</dim>
95
- </port>
96
- </input>
97
- <output>
98
- <port id="1" precision="FP32">
99
- <dim>1</dim>
100
- <dim>64</dim>
101
- <dim>1</dim>
102
- <dim>1</dim>
103
- </port>
104
- </output>
105
- </layer>
106
- <layer id="6" name="/conv1/Conv" type="Add" version="opset1">
107
- <data auto_broadcast="numpy" />
108
- <input>
109
- <port id="0" precision="FP32">
110
- <dim>-1</dim>
111
- <dim>64</dim>
112
- <dim>112</dim>
113
- <dim>112</dim>
114
- </port>
115
- <port id="1" precision="FP32">
116
- <dim>1</dim>
117
- <dim>64</dim>
118
- <dim>1</dim>
119
- <dim>1</dim>
120
- </port>
121
- </input>
122
- <output>
123
- <port id="2" precision="FP32" names="/conv1/Conv_output_0">
124
- <dim>-1</dim>
125
- <dim>64</dim>
126
- <dim>112</dim>
127
- <dim>112</dim>
128
- </port>
129
- </output>
130
- </layer>
131
- <layer id="7" name="/relu/Relu" type="ReLU" version="opset1">
132
- <input>
133
- <port id="0" precision="FP32">
134
- <dim>-1</dim>
135
- <dim>64</dim>
136
- <dim>112</dim>
137
- <dim>112</dim>
138
- </port>
139
- </input>
140
- <output>
141
- <port id="1" precision="FP32" names="/relu/Relu_output_0">
142
- <dim>-1</dim>
143
- <dim>64</dim>
144
- <dim>112</dim>
145
- <dim>112</dim>
146
- </port>
147
- </output>
148
- </layer>
149
- <layer id="8" name="/maxpool/MaxPool" type="MaxPool" version="opset8">
150
- <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" kernel="3, 3" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" />
151
- <input>
152
- <port id="0" precision="FP32">
153
- <dim>-1</dim>
154
- <dim>64</dim>
155
- <dim>112</dim>
156
- <dim>112</dim>
157
- </port>
158
- </input>
159
- <output>
160
- <port id="1" precision="FP32" names="/maxpool/MaxPool_output_0">
161
- <dim>-1</dim>
162
- <dim>64</dim>
163
- <dim>56</dim>
164
- <dim>56</dim>
165
- </port>
166
- <port id="2" precision="I64">
167
- <dim>-1</dim>
168
- <dim>64</dim>
169
- <dim>56</dim>
170
- <dim>56</dim>
171
- </port>
172
- </output>
173
- </layer>
174
- <layer id="9" name="onnx::Conv_196_compressed" type="Const" version="opset1">
175
- <data element_type="f16" shape="64, 64, 3, 3" offset="18944" size="73728" />
176
- <output>
177
- <port id="0" precision="FP16" names="onnx::Conv_196">
178
- <dim>64</dim>
179
- <dim>64</dim>
180
- <dim>3</dim>
181
- <dim>3</dim>
182
- </port>
183
- </output>
184
- </layer>
185
- <layer id="10" name="onnx::Conv_196" type="Convert" version="opset1">
186
- <data destination_type="f32" />
187
- <rt_info>
188
- <attribute name="decompression" version="0" />
189
- </rt_info>
190
- <input>
191
- <port id="0" precision="FP16">
192
- <dim>64</dim>
193
- <dim>64</dim>
194
- <dim>3</dim>
195
- <dim>3</dim>
196
- </port>
197
- </input>
198
- <output>
199
- <port id="1" precision="FP32">
200
- <dim>64</dim>
201
- <dim>64</dim>
202
- <dim>3</dim>
203
- <dim>3</dim>
204
- </port>
205
- </output>
206
- </layer>
207
- <layer id="11" name="/layer1/layer1.0/conv1/Conv/WithoutBiases" type="Convolution" version="opset1">
208
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
209
- <input>
210
- <port id="0" precision="FP32">
211
- <dim>-1</dim>
212
- <dim>64</dim>
213
- <dim>56</dim>
214
- <dim>56</dim>
215
- </port>
216
- <port id="1" precision="FP32">
217
- <dim>64</dim>
218
- <dim>64</dim>
219
- <dim>3</dim>
220
- <dim>3</dim>
221
- </port>
222
- </input>
223
- <output>
224
- <port id="2" precision="FP32">
225
- <dim>-1</dim>
226
- <dim>64</dim>
227
- <dim>56</dim>
228
- <dim>56</dim>
229
- </port>
230
- </output>
231
- </layer>
232
- <layer id="12" name="Reshape_32_compressed" type="Const" version="opset1">
233
- <data element_type="f16" shape="1, 64, 1, 1" offset="92672" size="128" />
234
- <output>
235
- <port id="0" precision="FP16">
236
- <dim>1</dim>
237
- <dim>64</dim>
238
- <dim>1</dim>
239
- <dim>1</dim>
240
- </port>
241
- </output>
242
- </layer>
243
- <layer id="13" name="Reshape_32" type="Convert" version="opset1">
244
- <data destination_type="f32" />
245
- <rt_info>
246
- <attribute name="decompression" version="0" />
247
- </rt_info>
248
- <input>
249
- <port id="0" precision="FP16">
250
- <dim>1</dim>
251
- <dim>64</dim>
252
- <dim>1</dim>
253
- <dim>1</dim>
254
- </port>
255
- </input>
256
- <output>
257
- <port id="1" precision="FP32">
258
- <dim>1</dim>
259
- <dim>64</dim>
260
- <dim>1</dim>
261
- <dim>1</dim>
262
- </port>
263
- </output>
264
- </layer>
265
- <layer id="14" name="/layer1/layer1.0/conv1/Conv" type="Add" version="opset1">
266
- <data auto_broadcast="numpy" />
267
- <input>
268
- <port id="0" precision="FP32">
269
- <dim>-1</dim>
270
- <dim>64</dim>
271
- <dim>56</dim>
272
- <dim>56</dim>
273
- </port>
274
- <port id="1" precision="FP32">
275
- <dim>1</dim>
276
- <dim>64</dim>
277
- <dim>1</dim>
278
- <dim>1</dim>
279
- </port>
280
- </input>
281
- <output>
282
- <port id="2" precision="FP32" names="/layer1/layer1.0/conv1/Conv_output_0">
283
- <dim>-1</dim>
284
- <dim>64</dim>
285
- <dim>56</dim>
286
- <dim>56</dim>
287
- </port>
288
- </output>
289
- </layer>
290
- <layer id="15" name="/layer1/layer1.0/relu/Relu" type="ReLU" version="opset1">
291
- <input>
292
- <port id="0" precision="FP32">
293
- <dim>-1</dim>
294
- <dim>64</dim>
295
- <dim>56</dim>
296
- <dim>56</dim>
297
- </port>
298
- </input>
299
- <output>
300
- <port id="1" precision="FP32" names="/layer1/layer1.0/relu/Relu_output_0">
301
- <dim>-1</dim>
302
- <dim>64</dim>
303
- <dim>56</dim>
304
- <dim>56</dim>
305
- </port>
306
- </output>
307
- </layer>
308
- <layer id="16" name="onnx::Conv_199_compressed" type="Const" version="opset1">
309
- <data element_type="f16" shape="64, 64, 3, 3" offset="92800" size="73728" />
310
- <output>
311
- <port id="0" precision="FP16" names="onnx::Conv_199">
312
- <dim>64</dim>
313
- <dim>64</dim>
314
- <dim>3</dim>
315
- <dim>3</dim>
316
- </port>
317
- </output>
318
- </layer>
319
- <layer id="17" name="onnx::Conv_199" type="Convert" version="opset1">
320
- <data destination_type="f32" />
321
- <rt_info>
322
- <attribute name="decompression" version="0" />
323
- </rt_info>
324
- <input>
325
- <port id="0" precision="FP16">
326
- <dim>64</dim>
327
- <dim>64</dim>
328
- <dim>3</dim>
329
- <dim>3</dim>
330
- </port>
331
- </input>
332
- <output>
333
- <port id="1" precision="FP32">
334
- <dim>64</dim>
335
- <dim>64</dim>
336
- <dim>3</dim>
337
- <dim>3</dim>
338
- </port>
339
- </output>
340
- </layer>
341
- <layer id="18" name="/layer1/layer1.0/conv2/Conv/WithoutBiases" type="Convolution" version="opset1">
342
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
343
- <input>
344
- <port id="0" precision="FP32">
345
- <dim>-1</dim>
346
- <dim>64</dim>
347
- <dim>56</dim>
348
- <dim>56</dim>
349
- </port>
350
- <port id="1" precision="FP32">
351
- <dim>64</dim>
352
- <dim>64</dim>
353
- <dim>3</dim>
354
- <dim>3</dim>
355
- </port>
356
- </input>
357
- <output>
358
- <port id="2" precision="FP32">
359
- <dim>-1</dim>
360
- <dim>64</dim>
361
- <dim>56</dim>
362
- <dim>56</dim>
363
- </port>
364
- </output>
365
- </layer>
366
- <layer id="19" name="Reshape_48_compressed" type="Const" version="opset1">
367
- <data element_type="f16" shape="1, 64, 1, 1" offset="166528" size="128" />
368
- <output>
369
- <port id="0" precision="FP16">
370
- <dim>1</dim>
371
- <dim>64</dim>
372
- <dim>1</dim>
373
- <dim>1</dim>
374
- </port>
375
- </output>
376
- </layer>
377
- <layer id="20" name="Reshape_48" type="Convert" version="opset1">
378
- <data destination_type="f32" />
379
- <rt_info>
380
- <attribute name="decompression" version="0" />
381
- </rt_info>
382
- <input>
383
- <port id="0" precision="FP16">
384
- <dim>1</dim>
385
- <dim>64</dim>
386
- <dim>1</dim>
387
- <dim>1</dim>
388
- </port>
389
- </input>
390
- <output>
391
- <port id="1" precision="FP32">
392
- <dim>1</dim>
393
- <dim>64</dim>
394
- <dim>1</dim>
395
- <dim>1</dim>
396
- </port>
397
- </output>
398
- </layer>
399
- <layer id="21" name="/layer1/layer1.0/conv2/Conv" type="Add" version="opset1">
400
- <data auto_broadcast="numpy" />
401
- <input>
402
- <port id="0" precision="FP32">
403
- <dim>-1</dim>
404
- <dim>64</dim>
405
- <dim>56</dim>
406
- <dim>56</dim>
407
- </port>
408
- <port id="1" precision="FP32">
409
- <dim>1</dim>
410
- <dim>64</dim>
411
- <dim>1</dim>
412
- <dim>1</dim>
413
- </port>
414
- </input>
415
- <output>
416
- <port id="2" precision="FP32" names="/layer1/layer1.0/conv2/Conv_output_0">
417
- <dim>-1</dim>
418
- <dim>64</dim>
419
- <dim>56</dim>
420
- <dim>56</dim>
421
- </port>
422
- </output>
423
- </layer>
424
- <layer id="22" name="/layer1/layer1.0/Add" type="Add" version="opset1">
425
- <data auto_broadcast="numpy" />
426
- <input>
427
- <port id="0" precision="FP32">
428
- <dim>-1</dim>
429
- <dim>64</dim>
430
- <dim>56</dim>
431
- <dim>56</dim>
432
- </port>
433
- <port id="1" precision="FP32">
434
- <dim>-1</dim>
435
- <dim>64</dim>
436
- <dim>56</dim>
437
- <dim>56</dim>
438
- </port>
439
- </input>
440
- <output>
441
- <port id="2" precision="FP32" names="/layer1/layer1.0/Add_output_0">
442
- <dim>-1</dim>
443
- <dim>64</dim>
444
- <dim>56</dim>
445
- <dim>56</dim>
446
- </port>
447
- </output>
448
- </layer>
449
- <layer id="23" name="/layer1/layer1.0/relu_1/Relu" type="ReLU" version="opset1">
450
- <input>
451
- <port id="0" precision="FP32">
452
- <dim>-1</dim>
453
- <dim>64</dim>
454
- <dim>56</dim>
455
- <dim>56</dim>
456
- </port>
457
- </input>
458
- <output>
459
- <port id="1" precision="FP32" names="/layer1/layer1.0/relu_1/Relu_output_0">
460
- <dim>-1</dim>
461
- <dim>64</dim>
462
- <dim>56</dim>
463
- <dim>56</dim>
464
- </port>
465
- </output>
466
- </layer>
467
- <layer id="24" name="onnx::Conv_202_compressed" type="Const" version="opset1">
468
- <data element_type="f16" shape="64, 64, 3, 3" offset="166656" size="73728" />
469
- <output>
470
- <port id="0" precision="FP16" names="onnx::Conv_202">
471
- <dim>64</dim>
472
- <dim>64</dim>
473
- <dim>3</dim>
474
- <dim>3</dim>
475
- </port>
476
- </output>
477
- </layer>
478
- <layer id="25" name="onnx::Conv_202" type="Convert" version="opset1">
479
- <data destination_type="f32" />
480
- <rt_info>
481
- <attribute name="decompression" version="0" />
482
- </rt_info>
483
- <input>
484
- <port id="0" precision="FP16">
485
- <dim>64</dim>
486
- <dim>64</dim>
487
- <dim>3</dim>
488
- <dim>3</dim>
489
- </port>
490
- </input>
491
- <output>
492
- <port id="1" precision="FP32">
493
- <dim>64</dim>
494
- <dim>64</dim>
495
- <dim>3</dim>
496
- <dim>3</dim>
497
- </port>
498
- </output>
499
- </layer>
500
- <layer id="26" name="/layer1/layer1.1/conv1/Conv/WithoutBiases" type="Convolution" version="opset1">
501
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
502
- <input>
503
- <port id="0" precision="FP32">
504
- <dim>-1</dim>
505
- <dim>64</dim>
506
- <dim>56</dim>
507
- <dim>56</dim>
508
- </port>
509
- <port id="1" precision="FP32">
510
- <dim>64</dim>
511
- <dim>64</dim>
512
- <dim>3</dim>
513
- <dim>3</dim>
514
- </port>
515
- </input>
516
- <output>
517
- <port id="2" precision="FP32">
518
- <dim>-1</dim>
519
- <dim>64</dim>
520
- <dim>56</dim>
521
- <dim>56</dim>
522
- </port>
523
- </output>
524
- </layer>
525
- <layer id="27" name="Reshape_65_compressed" type="Const" version="opset1">
526
- <data element_type="f16" shape="1, 64, 1, 1" offset="240384" size="128" />
527
- <output>
528
- <port id="0" precision="FP16">
529
- <dim>1</dim>
530
- <dim>64</dim>
531
- <dim>1</dim>
532
- <dim>1</dim>
533
- </port>
534
- </output>
535
- </layer>
536
- <layer id="28" name="Reshape_65" type="Convert" version="opset1">
537
- <data destination_type="f32" />
538
- <rt_info>
539
- <attribute name="decompression" version="0" />
540
- </rt_info>
541
- <input>
542
- <port id="0" precision="FP16">
543
- <dim>1</dim>
544
- <dim>64</dim>
545
- <dim>1</dim>
546
- <dim>1</dim>
547
- </port>
548
- </input>
549
- <output>
550
- <port id="1" precision="FP32">
551
- <dim>1</dim>
552
- <dim>64</dim>
553
- <dim>1</dim>
554
- <dim>1</dim>
555
- </port>
556
- </output>
557
- </layer>
558
- <layer id="29" name="/layer1/layer1.1/conv1/Conv" type="Add" version="opset1">
559
- <data auto_broadcast="numpy" />
560
- <input>
561
- <port id="0" precision="FP32">
562
- <dim>-1</dim>
563
- <dim>64</dim>
564
- <dim>56</dim>
565
- <dim>56</dim>
566
- </port>
567
- <port id="1" precision="FP32">
568
- <dim>1</dim>
569
- <dim>64</dim>
570
- <dim>1</dim>
571
- <dim>1</dim>
572
- </port>
573
- </input>
574
- <output>
575
- <port id="2" precision="FP32" names="/layer1/layer1.1/conv1/Conv_output_0">
576
- <dim>-1</dim>
577
- <dim>64</dim>
578
- <dim>56</dim>
579
- <dim>56</dim>
580
- </port>
581
- </output>
582
- </layer>
583
- <layer id="30" name="/layer1/layer1.1/relu/Relu" type="ReLU" version="opset1">
584
- <input>
585
- <port id="0" precision="FP32">
586
- <dim>-1</dim>
587
- <dim>64</dim>
588
- <dim>56</dim>
589
- <dim>56</dim>
590
- </port>
591
- </input>
592
- <output>
593
- <port id="1" precision="FP32" names="/layer1/layer1.1/relu/Relu_output_0">
594
- <dim>-1</dim>
595
- <dim>64</dim>
596
- <dim>56</dim>
597
- <dim>56</dim>
598
- </port>
599
- </output>
600
- </layer>
601
- <layer id="31" name="onnx::Conv_205_compressed" type="Const" version="opset1">
602
- <data element_type="f16" shape="64, 64, 3, 3" offset="240512" size="73728" />
603
- <output>
604
- <port id="0" precision="FP16" names="onnx::Conv_205">
605
- <dim>64</dim>
606
- <dim>64</dim>
607
- <dim>3</dim>
608
- <dim>3</dim>
609
- </port>
610
- </output>
611
- </layer>
612
- <layer id="32" name="onnx::Conv_205" type="Convert" version="opset1">
613
- <data destination_type="f32" />
614
- <rt_info>
615
- <attribute name="decompression" version="0" />
616
- </rt_info>
617
- <input>
618
- <port id="0" precision="FP16">
619
- <dim>64</dim>
620
- <dim>64</dim>
621
- <dim>3</dim>
622
- <dim>3</dim>
623
- </port>
624
- </input>
625
- <output>
626
- <port id="1" precision="FP32">
627
- <dim>64</dim>
628
- <dim>64</dim>
629
- <dim>3</dim>
630
- <dim>3</dim>
631
- </port>
632
- </output>
633
- </layer>
634
- <layer id="33" name="/layer1/layer1.1/conv2/Conv/WithoutBiases" type="Convolution" version="opset1">
635
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
636
- <input>
637
- <port id="0" precision="FP32">
638
- <dim>-1</dim>
639
- <dim>64</dim>
640
- <dim>56</dim>
641
- <dim>56</dim>
642
- </port>
643
- <port id="1" precision="FP32">
644
- <dim>64</dim>
645
- <dim>64</dim>
646
- <dim>3</dim>
647
- <dim>3</dim>
648
- </port>
649
- </input>
650
- <output>
651
- <port id="2" precision="FP32">
652
- <dim>-1</dim>
653
- <dim>64</dim>
654
- <dim>56</dim>
655
- <dim>56</dim>
656
- </port>
657
- </output>
658
- </layer>
659
- <layer id="34" name="Reshape_81_compressed" type="Const" version="opset1">
660
- <data element_type="f16" shape="1, 64, 1, 1" offset="314240" size="128" />
661
- <output>
662
- <port id="0" precision="FP16">
663
- <dim>1</dim>
664
- <dim>64</dim>
665
- <dim>1</dim>
666
- <dim>1</dim>
667
- </port>
668
- </output>
669
- </layer>
670
- <layer id="35" name="Reshape_81" type="Convert" version="opset1">
671
- <data destination_type="f32" />
672
- <rt_info>
673
- <attribute name="decompression" version="0" />
674
- </rt_info>
675
- <input>
676
- <port id="0" precision="FP16">
677
- <dim>1</dim>
678
- <dim>64</dim>
679
- <dim>1</dim>
680
- <dim>1</dim>
681
- </port>
682
- </input>
683
- <output>
684
- <port id="1" precision="FP32">
685
- <dim>1</dim>
686
- <dim>64</dim>
687
- <dim>1</dim>
688
- <dim>1</dim>
689
- </port>
690
- </output>
691
- </layer>
692
- <layer id="36" name="/layer1/layer1.1/conv2/Conv" type="Add" version="opset1">
693
- <data auto_broadcast="numpy" />
694
- <input>
695
- <port id="0" precision="FP32">
696
- <dim>-1</dim>
697
- <dim>64</dim>
698
- <dim>56</dim>
699
- <dim>56</dim>
700
- </port>
701
- <port id="1" precision="FP32">
702
- <dim>1</dim>
703
- <dim>64</dim>
704
- <dim>1</dim>
705
- <dim>1</dim>
706
- </port>
707
- </input>
708
- <output>
709
- <port id="2" precision="FP32" names="/layer1/layer1.1/conv2/Conv_output_0">
710
- <dim>-1</dim>
711
- <dim>64</dim>
712
- <dim>56</dim>
713
- <dim>56</dim>
714
- </port>
715
- </output>
716
- </layer>
717
- <layer id="37" name="/layer1/layer1.1/Add" type="Add" version="opset1">
718
- <data auto_broadcast="numpy" />
719
- <input>
720
- <port id="0" precision="FP32">
721
- <dim>-1</dim>
722
- <dim>64</dim>
723
- <dim>56</dim>
724
- <dim>56</dim>
725
- </port>
726
- <port id="1" precision="FP32">
727
- <dim>-1</dim>
728
- <dim>64</dim>
729
- <dim>56</dim>
730
- <dim>56</dim>
731
- </port>
732
- </input>
733
- <output>
734
- <port id="2" precision="FP32" names="/layer1/layer1.1/Add_output_0">
735
- <dim>-1</dim>
736
- <dim>64</dim>
737
- <dim>56</dim>
738
- <dim>56</dim>
739
- </port>
740
- </output>
741
- </layer>
742
- <layer id="38" name="/layer1/layer1.1/relu_1/Relu" type="ReLU" version="opset1">
743
- <input>
744
- <port id="0" precision="FP32">
745
- <dim>-1</dim>
746
- <dim>64</dim>
747
- <dim>56</dim>
748
- <dim>56</dim>
749
- </port>
750
- </input>
751
- <output>
752
- <port id="1" precision="FP32" names="/layer1/layer1.1/relu_1/Relu_output_0">
753
- <dim>-1</dim>
754
- <dim>64</dim>
755
- <dim>56</dim>
756
- <dim>56</dim>
757
- </port>
758
- </output>
759
- </layer>
760
- <layer id="39" name="onnx::Conv_208_compressed" type="Const" version="opset1">
761
- <data element_type="f16" shape="128, 64, 3, 3" offset="314368" size="147456" />
762
- <output>
763
- <port id="0" precision="FP16" names="onnx::Conv_208">
764
- <dim>128</dim>
765
- <dim>64</dim>
766
- <dim>3</dim>
767
- <dim>3</dim>
768
- </port>
769
- </output>
770
- </layer>
771
- <layer id="40" name="onnx::Conv_208" type="Convert" version="opset1">
772
- <data destination_type="f32" />
773
- <rt_info>
774
- <attribute name="decompression" version="0" />
775
- </rt_info>
776
- <input>
777
- <port id="0" precision="FP16">
778
- <dim>128</dim>
779
- <dim>64</dim>
780
- <dim>3</dim>
781
- <dim>3</dim>
782
- </port>
783
- </input>
784
- <output>
785
- <port id="1" precision="FP32">
786
- <dim>128</dim>
787
- <dim>64</dim>
788
- <dim>3</dim>
789
- <dim>3</dim>
790
- </port>
791
- </output>
792
- </layer>
793
- <layer id="41" name="/layer2/layer2.0/conv1/Conv/WithoutBiases" type="Convolution" version="opset1">
794
- <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
795
- <input>
796
- <port id="0" precision="FP32">
797
- <dim>-1</dim>
798
- <dim>64</dim>
799
- <dim>56</dim>
800
- <dim>56</dim>
801
- </port>
802
- <port id="1" precision="FP32">
803
- <dim>128</dim>
804
- <dim>64</dim>
805
- <dim>3</dim>
806
- <dim>3</dim>
807
- </port>
808
- </input>
809
- <output>
810
- <port id="2" precision="FP32">
811
- <dim>-1</dim>
812
- <dim>128</dim>
813
- <dim>28</dim>
814
- <dim>28</dim>
815
- </port>
816
- </output>
817
- </layer>
818
- <layer id="42" name="Reshape_98_compressed" type="Const" version="opset1">
819
- <data element_type="f16" shape="1, 128, 1, 1" offset="461824" size="256" />
820
- <output>
821
- <port id="0" precision="FP16">
822
- <dim>1</dim>
823
- <dim>128</dim>
824
- <dim>1</dim>
825
- <dim>1</dim>
826
- </port>
827
- </output>
828
- </layer>
829
- <layer id="43" name="Reshape_98" type="Convert" version="opset1">
830
- <data destination_type="f32" />
831
- <rt_info>
832
- <attribute name="decompression" version="0" />
833
- </rt_info>
834
- <input>
835
- <port id="0" precision="FP16">
836
- <dim>1</dim>
837
- <dim>128</dim>
838
- <dim>1</dim>
839
- <dim>1</dim>
840
- </port>
841
- </input>
842
- <output>
843
- <port id="1" precision="FP32">
844
- <dim>1</dim>
845
- <dim>128</dim>
846
- <dim>1</dim>
847
- <dim>1</dim>
848
- </port>
849
- </output>
850
- </layer>
851
- <layer id="44" name="/layer2/layer2.0/conv1/Conv" type="Add" version="opset1">
852
- <data auto_broadcast="numpy" />
853
- <input>
854
- <port id="0" precision="FP32">
855
- <dim>-1</dim>
856
- <dim>128</dim>
857
- <dim>28</dim>
858
- <dim>28</dim>
859
- </port>
860
- <port id="1" precision="FP32">
861
- <dim>1</dim>
862
- <dim>128</dim>
863
- <dim>1</dim>
864
- <dim>1</dim>
865
- </port>
866
- </input>
867
- <output>
868
- <port id="2" precision="FP32" names="/layer2/layer2.0/conv1/Conv_output_0">
869
- <dim>-1</dim>
870
- <dim>128</dim>
871
- <dim>28</dim>
872
- <dim>28</dim>
873
- </port>
874
- </output>
875
- </layer>
876
- <layer id="45" name="/layer2/layer2.0/relu/Relu" type="ReLU" version="opset1">
877
- <input>
878
- <port id="0" precision="FP32">
879
- <dim>-1</dim>
880
- <dim>128</dim>
881
- <dim>28</dim>
882
- <dim>28</dim>
883
- </port>
884
- </input>
885
- <output>
886
- <port id="1" precision="FP32" names="/layer2/layer2.0/relu/Relu_output_0">
887
- <dim>-1</dim>
888
- <dim>128</dim>
889
- <dim>28</dim>
890
- <dim>28</dim>
891
- </port>
892
- </output>
893
- </layer>
894
- <layer id="46" name="onnx::Conv_211_compressed" type="Const" version="opset1">
895
- <data element_type="f16" shape="128, 128, 3, 3" offset="462080" size="294912" />
896
- <output>
897
- <port id="0" precision="FP16" names="onnx::Conv_211">
898
- <dim>128</dim>
899
- <dim>128</dim>
900
- <dim>3</dim>
901
- <dim>3</dim>
902
- </port>
903
- </output>
904
- </layer>
905
- <layer id="47" name="onnx::Conv_211" type="Convert" version="opset1">
906
- <data destination_type="f32" />
907
- <rt_info>
908
- <attribute name="decompression" version="0" />
909
- </rt_info>
910
- <input>
911
- <port id="0" precision="FP16">
912
- <dim>128</dim>
913
- <dim>128</dim>
914
- <dim>3</dim>
915
- <dim>3</dim>
916
- </port>
917
- </input>
918
- <output>
919
- <port id="1" precision="FP32">
920
- <dim>128</dim>
921
- <dim>128</dim>
922
- <dim>3</dim>
923
- <dim>3</dim>
924
- </port>
925
- </output>
926
- </layer>
927
- <layer id="48" name="/layer2/layer2.0/conv2/Conv/WithoutBiases" type="Convolution" version="opset1">
928
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
929
- <input>
930
- <port id="0" precision="FP32">
931
- <dim>-1</dim>
932
- <dim>128</dim>
933
- <dim>28</dim>
934
- <dim>28</dim>
935
- </port>
936
- <port id="1" precision="FP32">
937
- <dim>128</dim>
938
- <dim>128</dim>
939
- <dim>3</dim>
940
- <dim>3</dim>
941
- </port>
942
- </input>
943
- <output>
944
- <port id="2" precision="FP32">
945
- <dim>-1</dim>
946
- <dim>128</dim>
947
- <dim>28</dim>
948
- <dim>28</dim>
949
- </port>
950
- </output>
951
- </layer>
952
- <layer id="49" name="Reshape_129_compressed" type="Const" version="opset1">
953
- <data element_type="f16" shape="1, 128, 1, 1" offset="756992" size="256" />
954
- <output>
955
- <port id="0" precision="FP16">
956
- <dim>1</dim>
957
- <dim>128</dim>
958
- <dim>1</dim>
959
- <dim>1</dim>
960
- </port>
961
- </output>
962
- </layer>
963
- <layer id="50" name="Reshape_129" type="Convert" version="opset1">
964
- <data destination_type="f32" />
965
- <rt_info>
966
- <attribute name="decompression" version="0" />
967
- </rt_info>
968
- <input>
969
- <port id="0" precision="FP16">
970
- <dim>1</dim>
971
- <dim>128</dim>
972
- <dim>1</dim>
973
- <dim>1</dim>
974
- </port>
975
- </input>
976
- <output>
977
- <port id="1" precision="FP32">
978
- <dim>1</dim>
979
- <dim>128</dim>
980
- <dim>1</dim>
981
- <dim>1</dim>
982
- </port>
983
- </output>
984
- </layer>
985
- <layer id="51" name="/layer2/layer2.0/conv2/Conv" type="Add" version="opset1">
986
- <data auto_broadcast="numpy" />
987
- <input>
988
- <port id="0" precision="FP32">
989
- <dim>-1</dim>
990
- <dim>128</dim>
991
- <dim>28</dim>
992
- <dim>28</dim>
993
- </port>
994
- <port id="1" precision="FP32">
995
- <dim>1</dim>
996
- <dim>128</dim>
997
- <dim>1</dim>
998
- <dim>1</dim>
999
- </port>
1000
- </input>
1001
- <output>
1002
- <port id="2" precision="FP32" names="/layer2/layer2.0/conv2/Conv_output_0">
1003
- <dim>-1</dim>
1004
- <dim>128</dim>
1005
- <dim>28</dim>
1006
- <dim>28</dim>
1007
- </port>
1008
- </output>
1009
- </layer>
1010
- <layer id="52" name="onnx::Conv_214_compressed" type="Const" version="opset1">
1011
- <data element_type="f16" shape="128, 64, 1, 1" offset="757248" size="16384" />
1012
- <output>
1013
- <port id="0" precision="FP16" names="onnx::Conv_214">
1014
- <dim>128</dim>
1015
- <dim>64</dim>
1016
- <dim>1</dim>
1017
- <dim>1</dim>
1018
- </port>
1019
- </output>
1020
- </layer>
1021
- <layer id="53" name="onnx::Conv_214" type="Convert" version="opset1">
1022
- <data destination_type="f32" />
1023
- <rt_info>
1024
- <attribute name="decompression" version="0" />
1025
- </rt_info>
1026
- <input>
1027
- <port id="0" precision="FP16">
1028
- <dim>128</dim>
1029
- <dim>64</dim>
1030
- <dim>1</dim>
1031
- <dim>1</dim>
1032
- </port>
1033
- </input>
1034
- <output>
1035
- <port id="1" precision="FP32">
1036
- <dim>128</dim>
1037
- <dim>64</dim>
1038
- <dim>1</dim>
1039
- <dim>1</dim>
1040
- </port>
1041
- </output>
1042
- </layer>
1043
- <layer id="54" name="/layer2/layer2.0/downsample/downsample.0/Conv/WithoutBiases" type="Convolution" version="opset1">
1044
- <data strides="2, 2" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
1045
- <input>
1046
- <port id="0" precision="FP32">
1047
- <dim>-1</dim>
1048
- <dim>64</dim>
1049
- <dim>56</dim>
1050
- <dim>56</dim>
1051
- </port>
1052
- <port id="1" precision="FP32">
1053
- <dim>128</dim>
1054
- <dim>64</dim>
1055
- <dim>1</dim>
1056
- <dim>1</dim>
1057
- </port>
1058
- </input>
1059
- <output>
1060
- <port id="2" precision="FP32">
1061
- <dim>-1</dim>
1062
- <dim>128</dim>
1063
- <dim>28</dim>
1064
- <dim>28</dim>
1065
- </port>
1066
- </output>
1067
- </layer>
1068
- <layer id="55" name="Reshape_113_compressed" type="Const" version="opset1">
1069
- <data element_type="f16" shape="1, 128, 1, 1" offset="773632" size="256" />
1070
- <output>
1071
- <port id="0" precision="FP16">
1072
- <dim>1</dim>
1073
- <dim>128</dim>
1074
- <dim>1</dim>
1075
- <dim>1</dim>
1076
- </port>
1077
- </output>
1078
- </layer>
1079
- <layer id="56" name="Reshape_113" type="Convert" version="opset1">
1080
- <data destination_type="f32" />
1081
- <rt_info>
1082
- <attribute name="decompression" version="0" />
1083
- </rt_info>
1084
- <input>
1085
- <port id="0" precision="FP16">
1086
- <dim>1</dim>
1087
- <dim>128</dim>
1088
- <dim>1</dim>
1089
- <dim>1</dim>
1090
- </port>
1091
- </input>
1092
- <output>
1093
- <port id="1" precision="FP32">
1094
- <dim>1</dim>
1095
- <dim>128</dim>
1096
- <dim>1</dim>
1097
- <dim>1</dim>
1098
- </port>
1099
- </output>
1100
- </layer>
1101
- <layer id="57" name="/layer2/layer2.0/downsample/downsample.0/Conv" type="Add" version="opset1">
1102
- <data auto_broadcast="numpy" />
1103
- <input>
1104
- <port id="0" precision="FP32">
1105
- <dim>-1</dim>
1106
- <dim>128</dim>
1107
- <dim>28</dim>
1108
- <dim>28</dim>
1109
- </port>
1110
- <port id="1" precision="FP32">
1111
- <dim>1</dim>
1112
- <dim>128</dim>
1113
- <dim>1</dim>
1114
- <dim>1</dim>
1115
- </port>
1116
- </input>
1117
- <output>
1118
- <port id="2" precision="FP32" names="/layer2/layer2.0/downsample/downsample.0/Conv_output_0">
1119
- <dim>-1</dim>
1120
- <dim>128</dim>
1121
- <dim>28</dim>
1122
- <dim>28</dim>
1123
- </port>
1124
- </output>
1125
- </layer>
1126
- <layer id="58" name="/layer2/layer2.0/Add" type="Add" version="opset1">
1127
- <data auto_broadcast="numpy" />
1128
- <input>
1129
- <port id="0" precision="FP32">
1130
- <dim>-1</dim>
1131
- <dim>128</dim>
1132
- <dim>28</dim>
1133
- <dim>28</dim>
1134
- </port>
1135
- <port id="1" precision="FP32">
1136
- <dim>-1</dim>
1137
- <dim>128</dim>
1138
- <dim>28</dim>
1139
- <dim>28</dim>
1140
- </port>
1141
- </input>
1142
- <output>
1143
- <port id="2" precision="FP32" names="/layer2/layer2.0/Add_output_0">
1144
- <dim>-1</dim>
1145
- <dim>128</dim>
1146
- <dim>28</dim>
1147
- <dim>28</dim>
1148
- </port>
1149
- </output>
1150
- </layer>
1151
- <layer id="59" name="/layer2/layer2.0/relu_1/Relu" type="ReLU" version="opset1">
1152
- <input>
1153
- <port id="0" precision="FP32">
1154
- <dim>-1</dim>
1155
- <dim>128</dim>
1156
- <dim>28</dim>
1157
- <dim>28</dim>
1158
- </port>
1159
- </input>
1160
- <output>
1161
- <port id="1" precision="FP32" names="/layer2/layer2.0/relu_1/Relu_output_0">
1162
- <dim>-1</dim>
1163
- <dim>128</dim>
1164
- <dim>28</dim>
1165
- <dim>28</dim>
1166
- </port>
1167
- </output>
1168
- </layer>
1169
- <layer id="60" name="onnx::Conv_217_compressed" type="Const" version="opset1">
1170
- <data element_type="f16" shape="128, 128, 3, 3" offset="773888" size="294912" />
1171
- <output>
1172
- <port id="0" precision="FP16" names="onnx::Conv_217">
1173
- <dim>128</dim>
1174
- <dim>128</dim>
1175
- <dim>3</dim>
1176
- <dim>3</dim>
1177
- </port>
1178
- </output>
1179
- </layer>
1180
- <layer id="61" name="onnx::Conv_217" type="Convert" version="opset1">
1181
- <data destination_type="f32" />
1182
- <rt_info>
1183
- <attribute name="decompression" version="0" />
1184
- </rt_info>
1185
- <input>
1186
- <port id="0" precision="FP16">
1187
- <dim>128</dim>
1188
- <dim>128</dim>
1189
- <dim>3</dim>
1190
- <dim>3</dim>
1191
- </port>
1192
- </input>
1193
- <output>
1194
- <port id="1" precision="FP32">
1195
- <dim>128</dim>
1196
- <dim>128</dim>
1197
- <dim>3</dim>
1198
- <dim>3</dim>
1199
- </port>
1200
- </output>
1201
- </layer>
1202
- <layer id="62" name="/layer2/layer2.1/conv1/Conv/WithoutBiases" type="Convolution" version="opset1">
1203
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
1204
- <input>
1205
- <port id="0" precision="FP32">
1206
- <dim>-1</dim>
1207
- <dim>128</dim>
1208
- <dim>28</dim>
1209
- <dim>28</dim>
1210
- </port>
1211
- <port id="1" precision="FP32">
1212
- <dim>128</dim>
1213
- <dim>128</dim>
1214
- <dim>3</dim>
1215
- <dim>3</dim>
1216
- </port>
1217
- </input>
1218
- <output>
1219
- <port id="2" precision="FP32">
1220
- <dim>-1</dim>
1221
- <dim>128</dim>
1222
- <dim>28</dim>
1223
- <dim>28</dim>
1224
- </port>
1225
- </output>
1226
- </layer>
1227
- <layer id="63" name="Reshape_146_compressed" type="Const" version="opset1">
1228
- <data element_type="f16" shape="1, 128, 1, 1" offset="1068800" size="256" />
1229
- <output>
1230
- <port id="0" precision="FP16">
1231
- <dim>1</dim>
1232
- <dim>128</dim>
1233
- <dim>1</dim>
1234
- <dim>1</dim>
1235
- </port>
1236
- </output>
1237
- </layer>
1238
- <layer id="64" name="Reshape_146" type="Convert" version="opset1">
1239
- <data destination_type="f32" />
1240
- <rt_info>
1241
- <attribute name="decompression" version="0" />
1242
- </rt_info>
1243
- <input>
1244
- <port id="0" precision="FP16">
1245
- <dim>1</dim>
1246
- <dim>128</dim>
1247
- <dim>1</dim>
1248
- <dim>1</dim>
1249
- </port>
1250
- </input>
1251
- <output>
1252
- <port id="1" precision="FP32">
1253
- <dim>1</dim>
1254
- <dim>128</dim>
1255
- <dim>1</dim>
1256
- <dim>1</dim>
1257
- </port>
1258
- </output>
1259
- </layer>
1260
- <layer id="65" name="/layer2/layer2.1/conv1/Conv" type="Add" version="opset1">
1261
- <data auto_broadcast="numpy" />
1262
- <input>
1263
- <port id="0" precision="FP32">
1264
- <dim>-1</dim>
1265
- <dim>128</dim>
1266
- <dim>28</dim>
1267
- <dim>28</dim>
1268
- </port>
1269
- <port id="1" precision="FP32">
1270
- <dim>1</dim>
1271
- <dim>128</dim>
1272
- <dim>1</dim>
1273
- <dim>1</dim>
1274
- </port>
1275
- </input>
1276
- <output>
1277
- <port id="2" precision="FP32" names="/layer2/layer2.1/conv1/Conv_output_0">
1278
- <dim>-1</dim>
1279
- <dim>128</dim>
1280
- <dim>28</dim>
1281
- <dim>28</dim>
1282
- </port>
1283
- </output>
1284
- </layer>
1285
- <layer id="66" name="/layer2/layer2.1/relu/Relu" type="ReLU" version="opset1">
1286
- <input>
1287
- <port id="0" precision="FP32">
1288
- <dim>-1</dim>
1289
- <dim>128</dim>
1290
- <dim>28</dim>
1291
- <dim>28</dim>
1292
- </port>
1293
- </input>
1294
- <output>
1295
- <port id="1" precision="FP32" names="/layer2/layer2.1/relu/Relu_output_0">
1296
- <dim>-1</dim>
1297
- <dim>128</dim>
1298
- <dim>28</dim>
1299
- <dim>28</dim>
1300
- </port>
1301
- </output>
1302
- </layer>
1303
- <layer id="67" name="onnx::Conv_220_compressed" type="Const" version="opset1">
1304
- <data element_type="f16" shape="128, 128, 3, 3" offset="1069056" size="294912" />
1305
- <output>
1306
- <port id="0" precision="FP16" names="onnx::Conv_220">
1307
- <dim>128</dim>
1308
- <dim>128</dim>
1309
- <dim>3</dim>
1310
- <dim>3</dim>
1311
- </port>
1312
- </output>
1313
- </layer>
1314
- <layer id="68" name="onnx::Conv_220" type="Convert" version="opset1">
1315
- <data destination_type="f32" />
1316
- <rt_info>
1317
- <attribute name="decompression" version="0" />
1318
- </rt_info>
1319
- <input>
1320
- <port id="0" precision="FP16">
1321
- <dim>128</dim>
1322
- <dim>128</dim>
1323
- <dim>3</dim>
1324
- <dim>3</dim>
1325
- </port>
1326
- </input>
1327
- <output>
1328
- <port id="1" precision="FP32">
1329
- <dim>128</dim>
1330
- <dim>128</dim>
1331
- <dim>3</dim>
1332
- <dim>3</dim>
1333
- </port>
1334
- </output>
1335
- </layer>
1336
- <layer id="69" name="/layer2/layer2.1/conv2/Conv/WithoutBiases" type="Convolution" version="opset1">
1337
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
1338
- <input>
1339
- <port id="0" precision="FP32">
1340
- <dim>-1</dim>
1341
- <dim>128</dim>
1342
- <dim>28</dim>
1343
- <dim>28</dim>
1344
- </port>
1345
- <port id="1" precision="FP32">
1346
- <dim>128</dim>
1347
- <dim>128</dim>
1348
- <dim>3</dim>
1349
- <dim>3</dim>
1350
- </port>
1351
- </input>
1352
- <output>
1353
- <port id="2" precision="FP32">
1354
- <dim>-1</dim>
1355
- <dim>128</dim>
1356
- <dim>28</dim>
1357
- <dim>28</dim>
1358
- </port>
1359
- </output>
1360
- </layer>
1361
- <layer id="70" name="Reshape_162_compressed" type="Const" version="opset1">
1362
- <data element_type="f16" shape="1, 128, 1, 1" offset="1363968" size="256" />
1363
- <output>
1364
- <port id="0" precision="FP16">
1365
- <dim>1</dim>
1366
- <dim>128</dim>
1367
- <dim>1</dim>
1368
- <dim>1</dim>
1369
- </port>
1370
- </output>
1371
- </layer>
1372
- <layer id="71" name="Reshape_162" type="Convert" version="opset1">
1373
- <data destination_type="f32" />
1374
- <rt_info>
1375
- <attribute name="decompression" version="0" />
1376
- </rt_info>
1377
- <input>
1378
- <port id="0" precision="FP16">
1379
- <dim>1</dim>
1380
- <dim>128</dim>
1381
- <dim>1</dim>
1382
- <dim>1</dim>
1383
- </port>
1384
- </input>
1385
- <output>
1386
- <port id="1" precision="FP32">
1387
- <dim>1</dim>
1388
- <dim>128</dim>
1389
- <dim>1</dim>
1390
- <dim>1</dim>
1391
- </port>
1392
- </output>
1393
- </layer>
1394
- <layer id="72" name="/layer2/layer2.1/conv2/Conv" type="Add" version="opset1">
1395
- <data auto_broadcast="numpy" />
1396
- <input>
1397
- <port id="0" precision="FP32">
1398
- <dim>-1</dim>
1399
- <dim>128</dim>
1400
- <dim>28</dim>
1401
- <dim>28</dim>
1402
- </port>
1403
- <port id="1" precision="FP32">
1404
- <dim>1</dim>
1405
- <dim>128</dim>
1406
- <dim>1</dim>
1407
- <dim>1</dim>
1408
- </port>
1409
- </input>
1410
- <output>
1411
- <port id="2" precision="FP32" names="/layer2/layer2.1/conv2/Conv_output_0">
1412
- <dim>-1</dim>
1413
- <dim>128</dim>
1414
- <dim>28</dim>
1415
- <dim>28</dim>
1416
- </port>
1417
- </output>
1418
- </layer>
1419
- <layer id="73" name="/layer2/layer2.1/Add" type="Add" version="opset1">
1420
- <data auto_broadcast="numpy" />
1421
- <input>
1422
- <port id="0" precision="FP32">
1423
- <dim>-1</dim>
1424
- <dim>128</dim>
1425
- <dim>28</dim>
1426
- <dim>28</dim>
1427
- </port>
1428
- <port id="1" precision="FP32">
1429
- <dim>-1</dim>
1430
- <dim>128</dim>
1431
- <dim>28</dim>
1432
- <dim>28</dim>
1433
- </port>
1434
- </input>
1435
- <output>
1436
- <port id="2" precision="FP32" names="/layer2/layer2.1/Add_output_0">
1437
- <dim>-1</dim>
1438
- <dim>128</dim>
1439
- <dim>28</dim>
1440
- <dim>28</dim>
1441
- </port>
1442
- </output>
1443
- </layer>
1444
- <layer id="74" name="/layer2/layer2.1/relu_1/Relu" type="ReLU" version="opset1">
1445
- <input>
1446
- <port id="0" precision="FP32">
1447
- <dim>-1</dim>
1448
- <dim>128</dim>
1449
- <dim>28</dim>
1450
- <dim>28</dim>
1451
- </port>
1452
- </input>
1453
- <output>
1454
- <port id="1" precision="FP32" names="/layer2/layer2.1/relu_1/Relu_output_0">
1455
- <dim>-1</dim>
1456
- <dim>128</dim>
1457
- <dim>28</dim>
1458
- <dim>28</dim>
1459
- </port>
1460
- </output>
1461
- </layer>
1462
- <layer id="75" name="onnx::Conv_223_compressed" type="Const" version="opset1">
1463
- <data element_type="f16" shape="256, 128, 3, 3" offset="1364224" size="589824" />
1464
- <output>
1465
- <port id="0" precision="FP16" names="onnx::Conv_223">
1466
- <dim>256</dim>
1467
- <dim>128</dim>
1468
- <dim>3</dim>
1469
- <dim>3</dim>
1470
- </port>
1471
- </output>
1472
- </layer>
1473
- <layer id="76" name="onnx::Conv_223" type="Convert" version="opset1">
1474
- <data destination_type="f32" />
1475
- <rt_info>
1476
- <attribute name="decompression" version="0" />
1477
- </rt_info>
1478
- <input>
1479
- <port id="0" precision="FP16">
1480
- <dim>256</dim>
1481
- <dim>128</dim>
1482
- <dim>3</dim>
1483
- <dim>3</dim>
1484
- </port>
1485
- </input>
1486
- <output>
1487
- <port id="1" precision="FP32">
1488
- <dim>256</dim>
1489
- <dim>128</dim>
1490
- <dim>3</dim>
1491
- <dim>3</dim>
1492
- </port>
1493
- </output>
1494
- </layer>
1495
- <layer id="77" name="/layer3/layer3.0/conv1/Conv/WithoutBiases" type="Convolution" version="opset1">
1496
- <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
1497
- <input>
1498
- <port id="0" precision="FP32">
1499
- <dim>-1</dim>
1500
- <dim>128</dim>
1501
- <dim>28</dim>
1502
- <dim>28</dim>
1503
- </port>
1504
- <port id="1" precision="FP32">
1505
- <dim>256</dim>
1506
- <dim>128</dim>
1507
- <dim>3</dim>
1508
- <dim>3</dim>
1509
- </port>
1510
- </input>
1511
- <output>
1512
- <port id="2" precision="FP32">
1513
- <dim>-1</dim>
1514
- <dim>256</dim>
1515
- <dim>14</dim>
1516
- <dim>14</dim>
1517
- </port>
1518
- </output>
1519
- </layer>
1520
- <layer id="78" name="Reshape_179_compressed" type="Const" version="opset1">
1521
- <data element_type="f16" shape="1, 256, 1, 1" offset="1954048" size="512" />
1522
- <output>
1523
- <port id="0" precision="FP16">
1524
- <dim>1</dim>
1525
- <dim>256</dim>
1526
- <dim>1</dim>
1527
- <dim>1</dim>
1528
- </port>
1529
- </output>
1530
- </layer>
1531
- <layer id="79" name="Reshape_179" type="Convert" version="opset1">
1532
- <data destination_type="f32" />
1533
- <rt_info>
1534
- <attribute name="decompression" version="0" />
1535
- </rt_info>
1536
- <input>
1537
- <port id="0" precision="FP16">
1538
- <dim>1</dim>
1539
- <dim>256</dim>
1540
- <dim>1</dim>
1541
- <dim>1</dim>
1542
- </port>
1543
- </input>
1544
- <output>
1545
- <port id="1" precision="FP32">
1546
- <dim>1</dim>
1547
- <dim>256</dim>
1548
- <dim>1</dim>
1549
- <dim>1</dim>
1550
- </port>
1551
- </output>
1552
- </layer>
1553
- <layer id="80" name="/layer3/layer3.0/conv1/Conv" type="Add" version="opset1">
1554
- <data auto_broadcast="numpy" />
1555
- <input>
1556
- <port id="0" precision="FP32">
1557
- <dim>-1</dim>
1558
- <dim>256</dim>
1559
- <dim>14</dim>
1560
- <dim>14</dim>
1561
- </port>
1562
- <port id="1" precision="FP32">
1563
- <dim>1</dim>
1564
- <dim>256</dim>
1565
- <dim>1</dim>
1566
- <dim>1</dim>
1567
- </port>
1568
- </input>
1569
- <output>
1570
- <port id="2" precision="FP32" names="/layer3/layer3.0/conv1/Conv_output_0">
1571
- <dim>-1</dim>
1572
- <dim>256</dim>
1573
- <dim>14</dim>
1574
- <dim>14</dim>
1575
- </port>
1576
- </output>
1577
- </layer>
1578
- <layer id="81" name="/layer3/layer3.0/relu/Relu" type="ReLU" version="opset1">
1579
- <input>
1580
- <port id="0" precision="FP32">
1581
- <dim>-1</dim>
1582
- <dim>256</dim>
1583
- <dim>14</dim>
1584
- <dim>14</dim>
1585
- </port>
1586
- </input>
1587
- <output>
1588
- <port id="1" precision="FP32" names="/layer3/layer3.0/relu/Relu_output_0">
1589
- <dim>-1</dim>
1590
- <dim>256</dim>
1591
- <dim>14</dim>
1592
- <dim>14</dim>
1593
- </port>
1594
- </output>
1595
- </layer>
1596
- <layer id="82" name="onnx::Conv_226_compressed" type="Const" version="opset1">
1597
- <data element_type="f16" shape="256, 256, 3, 3" offset="1954560" size="1179648" />
1598
- <output>
1599
- <port id="0" precision="FP16" names="onnx::Conv_226">
1600
- <dim>256</dim>
1601
- <dim>256</dim>
1602
- <dim>3</dim>
1603
- <dim>3</dim>
1604
- </port>
1605
- </output>
1606
- </layer>
1607
- <layer id="83" name="onnx::Conv_226" type="Convert" version="opset1">
1608
- <data destination_type="f32" />
1609
- <rt_info>
1610
- <attribute name="decompression" version="0" />
1611
- </rt_info>
1612
- <input>
1613
- <port id="0" precision="FP16">
1614
- <dim>256</dim>
1615
- <dim>256</dim>
1616
- <dim>3</dim>
1617
- <dim>3</dim>
1618
- </port>
1619
- </input>
1620
- <output>
1621
- <port id="1" precision="FP32">
1622
- <dim>256</dim>
1623
- <dim>256</dim>
1624
- <dim>3</dim>
1625
- <dim>3</dim>
1626
- </port>
1627
- </output>
1628
- </layer>
1629
- <layer id="84" name="/layer3/layer3.0/conv2/Conv/WithoutBiases" type="Convolution" version="opset1">
1630
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
1631
- <input>
1632
- <port id="0" precision="FP32">
1633
- <dim>-1</dim>
1634
- <dim>256</dim>
1635
- <dim>14</dim>
1636
- <dim>14</dim>
1637
- </port>
1638
- <port id="1" precision="FP32">
1639
- <dim>256</dim>
1640
- <dim>256</dim>
1641
- <dim>3</dim>
1642
- <dim>3</dim>
1643
- </port>
1644
- </input>
1645
- <output>
1646
- <port id="2" precision="FP32">
1647
- <dim>-1</dim>
1648
- <dim>256</dim>
1649
- <dim>14</dim>
1650
- <dim>14</dim>
1651
- </port>
1652
- </output>
1653
- </layer>
1654
- <layer id="85" name="Reshape_210_compressed" type="Const" version="opset1">
1655
- <data element_type="f16" shape="1, 256, 1, 1" offset="3134208" size="512" />
1656
- <output>
1657
- <port id="0" precision="FP16">
1658
- <dim>1</dim>
1659
- <dim>256</dim>
1660
- <dim>1</dim>
1661
- <dim>1</dim>
1662
- </port>
1663
- </output>
1664
- </layer>
1665
- <layer id="86" name="Reshape_210" type="Convert" version="opset1">
1666
- <data destination_type="f32" />
1667
- <rt_info>
1668
- <attribute name="decompression" version="0" />
1669
- </rt_info>
1670
- <input>
1671
- <port id="0" precision="FP16">
1672
- <dim>1</dim>
1673
- <dim>256</dim>
1674
- <dim>1</dim>
1675
- <dim>1</dim>
1676
- </port>
1677
- </input>
1678
- <output>
1679
- <port id="1" precision="FP32">
1680
- <dim>1</dim>
1681
- <dim>256</dim>
1682
- <dim>1</dim>
1683
- <dim>1</dim>
1684
- </port>
1685
- </output>
1686
- </layer>
1687
- <layer id="87" name="/layer3/layer3.0/conv2/Conv" type="Add" version="opset1">
1688
- <data auto_broadcast="numpy" />
1689
- <input>
1690
- <port id="0" precision="FP32">
1691
- <dim>-1</dim>
1692
- <dim>256</dim>
1693
- <dim>14</dim>
1694
- <dim>14</dim>
1695
- </port>
1696
- <port id="1" precision="FP32">
1697
- <dim>1</dim>
1698
- <dim>256</dim>
1699
- <dim>1</dim>
1700
- <dim>1</dim>
1701
- </port>
1702
- </input>
1703
- <output>
1704
- <port id="2" precision="FP32" names="/layer3/layer3.0/conv2/Conv_output_0">
1705
- <dim>-1</dim>
1706
- <dim>256</dim>
1707
- <dim>14</dim>
1708
- <dim>14</dim>
1709
- </port>
1710
- </output>
1711
- </layer>
1712
- <layer id="88" name="onnx::Conv_229_compressed" type="Const" version="opset1">
1713
- <data element_type="f16" shape="256, 128, 1, 1" offset="3134720" size="65536" />
1714
- <output>
1715
- <port id="0" precision="FP16" names="onnx::Conv_229">
1716
- <dim>256</dim>
1717
- <dim>128</dim>
1718
- <dim>1</dim>
1719
- <dim>1</dim>
1720
- </port>
1721
- </output>
1722
- </layer>
1723
- <layer id="89" name="onnx::Conv_229" type="Convert" version="opset1">
1724
- <data destination_type="f32" />
1725
- <rt_info>
1726
- <attribute name="decompression" version="0" />
1727
- </rt_info>
1728
- <input>
1729
- <port id="0" precision="FP16">
1730
- <dim>256</dim>
1731
- <dim>128</dim>
1732
- <dim>1</dim>
1733
- <dim>1</dim>
1734
- </port>
1735
- </input>
1736
- <output>
1737
- <port id="1" precision="FP32">
1738
- <dim>256</dim>
1739
- <dim>128</dim>
1740
- <dim>1</dim>
1741
- <dim>1</dim>
1742
- </port>
1743
- </output>
1744
- </layer>
1745
- <layer id="90" name="/layer3/layer3.0/downsample/downsample.0/Conv/WithoutBiases" type="Convolution" version="opset1">
1746
- <data strides="2, 2" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
1747
- <input>
1748
- <port id="0" precision="FP32">
1749
- <dim>-1</dim>
1750
- <dim>128</dim>
1751
- <dim>28</dim>
1752
- <dim>28</dim>
1753
- </port>
1754
- <port id="1" precision="FP32">
1755
- <dim>256</dim>
1756
- <dim>128</dim>
1757
- <dim>1</dim>
1758
- <dim>1</dim>
1759
- </port>
1760
- </input>
1761
- <output>
1762
- <port id="2" precision="FP32">
1763
- <dim>-1</dim>
1764
- <dim>256</dim>
1765
- <dim>14</dim>
1766
- <dim>14</dim>
1767
- </port>
1768
- </output>
1769
- </layer>
1770
- <layer id="91" name="Reshape_194_compressed" type="Const" version="opset1">
1771
- <data element_type="f16" shape="1, 256, 1, 1" offset="3200256" size="512" />
1772
- <output>
1773
- <port id="0" precision="FP16">
1774
- <dim>1</dim>
1775
- <dim>256</dim>
1776
- <dim>1</dim>
1777
- <dim>1</dim>
1778
- </port>
1779
- </output>
1780
- </layer>
1781
- <layer id="92" name="Reshape_194" type="Convert" version="opset1">
1782
- <data destination_type="f32" />
1783
- <rt_info>
1784
- <attribute name="decompression" version="0" />
1785
- </rt_info>
1786
- <input>
1787
- <port id="0" precision="FP16">
1788
- <dim>1</dim>
1789
- <dim>256</dim>
1790
- <dim>1</dim>
1791
- <dim>1</dim>
1792
- </port>
1793
- </input>
1794
- <output>
1795
- <port id="1" precision="FP32">
1796
- <dim>1</dim>
1797
- <dim>256</dim>
1798
- <dim>1</dim>
1799
- <dim>1</dim>
1800
- </port>
1801
- </output>
1802
- </layer>
1803
- <layer id="93" name="/layer3/layer3.0/downsample/downsample.0/Conv" type="Add" version="opset1">
1804
- <data auto_broadcast="numpy" />
1805
- <input>
1806
- <port id="0" precision="FP32">
1807
- <dim>-1</dim>
1808
- <dim>256</dim>
1809
- <dim>14</dim>
1810
- <dim>14</dim>
1811
- </port>
1812
- <port id="1" precision="FP32">
1813
- <dim>1</dim>
1814
- <dim>256</dim>
1815
- <dim>1</dim>
1816
- <dim>1</dim>
1817
- </port>
1818
- </input>
1819
- <output>
1820
- <port id="2" precision="FP32" names="/layer3/layer3.0/downsample/downsample.0/Conv_output_0">
1821
- <dim>-1</dim>
1822
- <dim>256</dim>
1823
- <dim>14</dim>
1824
- <dim>14</dim>
1825
- </port>
1826
- </output>
1827
- </layer>
1828
- <layer id="94" name="/layer3/layer3.0/Add" type="Add" version="opset1">
1829
- <data auto_broadcast="numpy" />
1830
- <input>
1831
- <port id="0" precision="FP32">
1832
- <dim>-1</dim>
1833
- <dim>256</dim>
1834
- <dim>14</dim>
1835
- <dim>14</dim>
1836
- </port>
1837
- <port id="1" precision="FP32">
1838
- <dim>-1</dim>
1839
- <dim>256</dim>
1840
- <dim>14</dim>
1841
- <dim>14</dim>
1842
- </port>
1843
- </input>
1844
- <output>
1845
- <port id="2" precision="FP32" names="/layer3/layer3.0/Add_output_0">
1846
- <dim>-1</dim>
1847
- <dim>256</dim>
1848
- <dim>14</dim>
1849
- <dim>14</dim>
1850
- </port>
1851
- </output>
1852
- </layer>
1853
- <layer id="95" name="/layer3/layer3.0/relu_1/Relu" type="ReLU" version="opset1">
1854
- <input>
1855
- <port id="0" precision="FP32">
1856
- <dim>-1</dim>
1857
- <dim>256</dim>
1858
- <dim>14</dim>
1859
- <dim>14</dim>
1860
- </port>
1861
- </input>
1862
- <output>
1863
- <port id="1" precision="FP32" names="/layer3/layer3.0/relu_1/Relu_output_0">
1864
- <dim>-1</dim>
1865
- <dim>256</dim>
1866
- <dim>14</dim>
1867
- <dim>14</dim>
1868
- </port>
1869
- </output>
1870
- </layer>
1871
- <layer id="96" name="onnx::Conv_232_compressed" type="Const" version="opset1">
1872
- <data element_type="f16" shape="256, 256, 3, 3" offset="3200768" size="1179648" />
1873
- <output>
1874
- <port id="0" precision="FP16" names="onnx::Conv_232">
1875
- <dim>256</dim>
1876
- <dim>256</dim>
1877
- <dim>3</dim>
1878
- <dim>3</dim>
1879
- </port>
1880
- </output>
1881
- </layer>
1882
- <layer id="97" name="onnx::Conv_232" type="Convert" version="opset1">
1883
- <data destination_type="f32" />
1884
- <rt_info>
1885
- <attribute name="decompression" version="0" />
1886
- </rt_info>
1887
- <input>
1888
- <port id="0" precision="FP16">
1889
- <dim>256</dim>
1890
- <dim>256</dim>
1891
- <dim>3</dim>
1892
- <dim>3</dim>
1893
- </port>
1894
- </input>
1895
- <output>
1896
- <port id="1" precision="FP32">
1897
- <dim>256</dim>
1898
- <dim>256</dim>
1899
- <dim>3</dim>
1900
- <dim>3</dim>
1901
- </port>
1902
- </output>
1903
- </layer>
1904
- <layer id="98" name="/layer3/layer3.1/conv1/Conv/WithoutBiases" type="Convolution" version="opset1">
1905
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
1906
- <input>
1907
- <port id="0" precision="FP32">
1908
- <dim>-1</dim>
1909
- <dim>256</dim>
1910
- <dim>14</dim>
1911
- <dim>14</dim>
1912
- </port>
1913
- <port id="1" precision="FP32">
1914
- <dim>256</dim>
1915
- <dim>256</dim>
1916
- <dim>3</dim>
1917
- <dim>3</dim>
1918
- </port>
1919
- </input>
1920
- <output>
1921
- <port id="2" precision="FP32">
1922
- <dim>-1</dim>
1923
- <dim>256</dim>
1924
- <dim>14</dim>
1925
- <dim>14</dim>
1926
- </port>
1927
- </output>
1928
- </layer>
1929
- <layer id="99" name="Reshape_227_compressed" type="Const" version="opset1">
1930
- <data element_type="f16" shape="1, 256, 1, 1" offset="4380416" size="512" />
1931
- <output>
1932
- <port id="0" precision="FP16">
1933
- <dim>1</dim>
1934
- <dim>256</dim>
1935
- <dim>1</dim>
1936
- <dim>1</dim>
1937
- </port>
1938
- </output>
1939
- </layer>
1940
- <layer id="100" name="Reshape_227" type="Convert" version="opset1">
1941
- <data destination_type="f32" />
1942
- <rt_info>
1943
- <attribute name="decompression" version="0" />
1944
- </rt_info>
1945
- <input>
1946
- <port id="0" precision="FP16">
1947
- <dim>1</dim>
1948
- <dim>256</dim>
1949
- <dim>1</dim>
1950
- <dim>1</dim>
1951
- </port>
1952
- </input>
1953
- <output>
1954
- <port id="1" precision="FP32">
1955
- <dim>1</dim>
1956
- <dim>256</dim>
1957
- <dim>1</dim>
1958
- <dim>1</dim>
1959
- </port>
1960
- </output>
1961
- </layer>
1962
- <layer id="101" name="/layer3/layer3.1/conv1/Conv" type="Add" version="opset1">
1963
- <data auto_broadcast="numpy" />
1964
- <input>
1965
- <port id="0" precision="FP32">
1966
- <dim>-1</dim>
1967
- <dim>256</dim>
1968
- <dim>14</dim>
1969
- <dim>14</dim>
1970
- </port>
1971
- <port id="1" precision="FP32">
1972
- <dim>1</dim>
1973
- <dim>256</dim>
1974
- <dim>1</dim>
1975
- <dim>1</dim>
1976
- </port>
1977
- </input>
1978
- <output>
1979
- <port id="2" precision="FP32" names="/layer3/layer3.1/conv1/Conv_output_0">
1980
- <dim>-1</dim>
1981
- <dim>256</dim>
1982
- <dim>14</dim>
1983
- <dim>14</dim>
1984
- </port>
1985
- </output>
1986
- </layer>
1987
- <layer id="102" name="/layer3/layer3.1/relu/Relu" type="ReLU" version="opset1">
1988
- <input>
1989
- <port id="0" precision="FP32">
1990
- <dim>-1</dim>
1991
- <dim>256</dim>
1992
- <dim>14</dim>
1993
- <dim>14</dim>
1994
- </port>
1995
- </input>
1996
- <output>
1997
- <port id="1" precision="FP32" names="/layer3/layer3.1/relu/Relu_output_0">
1998
- <dim>-1</dim>
1999
- <dim>256</dim>
2000
- <dim>14</dim>
2001
- <dim>14</dim>
2002
- </port>
2003
- </output>
2004
- </layer>
2005
- <layer id="103" name="onnx::Conv_235_compressed" type="Const" version="opset1">
2006
- <data element_type="f16" shape="256, 256, 3, 3" offset="4380928" size="1179648" />
2007
- <output>
2008
- <port id="0" precision="FP16" names="onnx::Conv_235">
2009
- <dim>256</dim>
2010
- <dim>256</dim>
2011
- <dim>3</dim>
2012
- <dim>3</dim>
2013
- </port>
2014
- </output>
2015
- </layer>
2016
- <layer id="104" name="onnx::Conv_235" type="Convert" version="opset1">
2017
- <data destination_type="f32" />
2018
- <rt_info>
2019
- <attribute name="decompression" version="0" />
2020
- </rt_info>
2021
- <input>
2022
- <port id="0" precision="FP16">
2023
- <dim>256</dim>
2024
- <dim>256</dim>
2025
- <dim>3</dim>
2026
- <dim>3</dim>
2027
- </port>
2028
- </input>
2029
- <output>
2030
- <port id="1" precision="FP32">
2031
- <dim>256</dim>
2032
- <dim>256</dim>
2033
- <dim>3</dim>
2034
- <dim>3</dim>
2035
- </port>
2036
- </output>
2037
- </layer>
2038
- <layer id="105" name="/layer3/layer3.1/conv2/Conv/WithoutBiases" type="Convolution" version="opset1">
2039
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
2040
- <input>
2041
- <port id="0" precision="FP32">
2042
- <dim>-1</dim>
2043
- <dim>256</dim>
2044
- <dim>14</dim>
2045
- <dim>14</dim>
2046
- </port>
2047
- <port id="1" precision="FP32">
2048
- <dim>256</dim>
2049
- <dim>256</dim>
2050
- <dim>3</dim>
2051
- <dim>3</dim>
2052
- </port>
2053
- </input>
2054
- <output>
2055
- <port id="2" precision="FP32">
2056
- <dim>-1</dim>
2057
- <dim>256</dim>
2058
- <dim>14</dim>
2059
- <dim>14</dim>
2060
- </port>
2061
- </output>
2062
- </layer>
2063
- <layer id="106" name="Reshape_243_compressed" type="Const" version="opset1">
2064
- <data element_type="f16" shape="1, 256, 1, 1" offset="5560576" size="512" />
2065
- <output>
2066
- <port id="0" precision="FP16">
2067
- <dim>1</dim>
2068
- <dim>256</dim>
2069
- <dim>1</dim>
2070
- <dim>1</dim>
2071
- </port>
2072
- </output>
2073
- </layer>
2074
- <layer id="107" name="Reshape_243" type="Convert" version="opset1">
2075
- <data destination_type="f32" />
2076
- <rt_info>
2077
- <attribute name="decompression" version="0" />
2078
- </rt_info>
2079
- <input>
2080
- <port id="0" precision="FP16">
2081
- <dim>1</dim>
2082
- <dim>256</dim>
2083
- <dim>1</dim>
2084
- <dim>1</dim>
2085
- </port>
2086
- </input>
2087
- <output>
2088
- <port id="1" precision="FP32">
2089
- <dim>1</dim>
2090
- <dim>256</dim>
2091
- <dim>1</dim>
2092
- <dim>1</dim>
2093
- </port>
2094
- </output>
2095
- </layer>
2096
- <layer id="108" name="/layer3/layer3.1/conv2/Conv" type="Add" version="opset1">
2097
- <data auto_broadcast="numpy" />
2098
- <input>
2099
- <port id="0" precision="FP32">
2100
- <dim>-1</dim>
2101
- <dim>256</dim>
2102
- <dim>14</dim>
2103
- <dim>14</dim>
2104
- </port>
2105
- <port id="1" precision="FP32">
2106
- <dim>1</dim>
2107
- <dim>256</dim>
2108
- <dim>1</dim>
2109
- <dim>1</dim>
2110
- </port>
2111
- </input>
2112
- <output>
2113
- <port id="2" precision="FP32" names="/layer3/layer3.1/conv2/Conv_output_0">
2114
- <dim>-1</dim>
2115
- <dim>256</dim>
2116
- <dim>14</dim>
2117
- <dim>14</dim>
2118
- </port>
2119
- </output>
2120
- </layer>
2121
- <layer id="109" name="/layer3/layer3.1/Add" type="Add" version="opset1">
2122
- <data auto_broadcast="numpy" />
2123
- <input>
2124
- <port id="0" precision="FP32">
2125
- <dim>-1</dim>
2126
- <dim>256</dim>
2127
- <dim>14</dim>
2128
- <dim>14</dim>
2129
- </port>
2130
- <port id="1" precision="FP32">
2131
- <dim>-1</dim>
2132
- <dim>256</dim>
2133
- <dim>14</dim>
2134
- <dim>14</dim>
2135
- </port>
2136
- </input>
2137
- <output>
2138
- <port id="2" precision="FP32" names="/layer3/layer3.1/Add_output_0">
2139
- <dim>-1</dim>
2140
- <dim>256</dim>
2141
- <dim>14</dim>
2142
- <dim>14</dim>
2143
- </port>
2144
- </output>
2145
- </layer>
2146
- <layer id="110" name="/layer3/layer3.1/relu_1/Relu" type="ReLU" version="opset1">
2147
- <input>
2148
- <port id="0" precision="FP32">
2149
- <dim>-1</dim>
2150
- <dim>256</dim>
2151
- <dim>14</dim>
2152
- <dim>14</dim>
2153
- </port>
2154
- </input>
2155
- <output>
2156
- <port id="1" precision="FP32" names="/layer3/layer3.1/relu_1/Relu_output_0">
2157
- <dim>-1</dim>
2158
- <dim>256</dim>
2159
- <dim>14</dim>
2160
- <dim>14</dim>
2161
- </port>
2162
- </output>
2163
- </layer>
2164
- <layer id="111" name="onnx::Conv_238_compressed" type="Const" version="opset1">
2165
- <data element_type="f16" shape="512, 256, 3, 3" offset="5561088" size="2359296" />
2166
- <output>
2167
- <port id="0" precision="FP16" names="onnx::Conv_238">
2168
- <dim>512</dim>
2169
- <dim>256</dim>
2170
- <dim>3</dim>
2171
- <dim>3</dim>
2172
- </port>
2173
- </output>
2174
- </layer>
2175
- <layer id="112" name="onnx::Conv_238" type="Convert" version="opset1">
2176
- <data destination_type="f32" />
2177
- <rt_info>
2178
- <attribute name="decompression" version="0" />
2179
- </rt_info>
2180
- <input>
2181
- <port id="0" precision="FP16">
2182
- <dim>512</dim>
2183
- <dim>256</dim>
2184
- <dim>3</dim>
2185
- <dim>3</dim>
2186
- </port>
2187
- </input>
2188
- <output>
2189
- <port id="1" precision="FP32">
2190
- <dim>512</dim>
2191
- <dim>256</dim>
2192
- <dim>3</dim>
2193
- <dim>3</dim>
2194
- </port>
2195
- </output>
2196
- </layer>
2197
- <layer id="113" name="/layer4/layer4.0/conv1/Conv/WithoutBiases" type="Convolution" version="opset1">
2198
- <data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
2199
- <input>
2200
- <port id="0" precision="FP32">
2201
- <dim>-1</dim>
2202
- <dim>256</dim>
2203
- <dim>14</dim>
2204
- <dim>14</dim>
2205
- </port>
2206
- <port id="1" precision="FP32">
2207
- <dim>512</dim>
2208
- <dim>256</dim>
2209
- <dim>3</dim>
2210
- <dim>3</dim>
2211
- </port>
2212
- </input>
2213
- <output>
2214
- <port id="2" precision="FP32">
2215
- <dim>-1</dim>
2216
- <dim>512</dim>
2217
- <dim>7</dim>
2218
- <dim>7</dim>
2219
- </port>
2220
- </output>
2221
- </layer>
2222
- <layer id="114" name="Reshape_260_compressed" type="Const" version="opset1">
2223
- <data element_type="f16" shape="1, 512, 1, 1" offset="7920384" size="1024" />
2224
- <output>
2225
- <port id="0" precision="FP16">
2226
- <dim>1</dim>
2227
- <dim>512</dim>
2228
- <dim>1</dim>
2229
- <dim>1</dim>
2230
- </port>
2231
- </output>
2232
- </layer>
2233
- <layer id="115" name="Reshape_260" type="Convert" version="opset1">
2234
- <data destination_type="f32" />
2235
- <rt_info>
2236
- <attribute name="decompression" version="0" />
2237
- </rt_info>
2238
- <input>
2239
- <port id="0" precision="FP16">
2240
- <dim>1</dim>
2241
- <dim>512</dim>
2242
- <dim>1</dim>
2243
- <dim>1</dim>
2244
- </port>
2245
- </input>
2246
- <output>
2247
- <port id="1" precision="FP32">
2248
- <dim>1</dim>
2249
- <dim>512</dim>
2250
- <dim>1</dim>
2251
- <dim>1</dim>
2252
- </port>
2253
- </output>
2254
- </layer>
2255
- <layer id="116" name="/layer4/layer4.0/conv1/Conv" type="Add" version="opset1">
2256
- <data auto_broadcast="numpy" />
2257
- <input>
2258
- <port id="0" precision="FP32">
2259
- <dim>-1</dim>
2260
- <dim>512</dim>
2261
- <dim>7</dim>
2262
- <dim>7</dim>
2263
- </port>
2264
- <port id="1" precision="FP32">
2265
- <dim>1</dim>
2266
- <dim>512</dim>
2267
- <dim>1</dim>
2268
- <dim>1</dim>
2269
- </port>
2270
- </input>
2271
- <output>
2272
- <port id="2" precision="FP32" names="/layer4/layer4.0/conv1/Conv_output_0">
2273
- <dim>-1</dim>
2274
- <dim>512</dim>
2275
- <dim>7</dim>
2276
- <dim>7</dim>
2277
- </port>
2278
- </output>
2279
- </layer>
2280
- <layer id="117" name="/layer4/layer4.0/relu/Relu" type="ReLU" version="opset1">
2281
- <input>
2282
- <port id="0" precision="FP32">
2283
- <dim>-1</dim>
2284
- <dim>512</dim>
2285
- <dim>7</dim>
2286
- <dim>7</dim>
2287
- </port>
2288
- </input>
2289
- <output>
2290
- <port id="1" precision="FP32" names="/layer4/layer4.0/relu/Relu_output_0">
2291
- <dim>-1</dim>
2292
- <dim>512</dim>
2293
- <dim>7</dim>
2294
- <dim>7</dim>
2295
- </port>
2296
- </output>
2297
- </layer>
2298
- <layer id="118" name="onnx::Conv_241_compressed" type="Const" version="opset1">
2299
- <data element_type="f16" shape="512, 512, 3, 3" offset="7921408" size="4718592" />
2300
- <output>
2301
- <port id="0" precision="FP16" names="onnx::Conv_241">
2302
- <dim>512</dim>
2303
- <dim>512</dim>
2304
- <dim>3</dim>
2305
- <dim>3</dim>
2306
- </port>
2307
- </output>
2308
- </layer>
2309
- <layer id="119" name="onnx::Conv_241" type="Convert" version="opset1">
2310
- <data destination_type="f32" />
2311
- <rt_info>
2312
- <attribute name="decompression" version="0" />
2313
- </rt_info>
2314
- <input>
2315
- <port id="0" precision="FP16">
2316
- <dim>512</dim>
2317
- <dim>512</dim>
2318
- <dim>3</dim>
2319
- <dim>3</dim>
2320
- </port>
2321
- </input>
2322
- <output>
2323
- <port id="1" precision="FP32">
2324
- <dim>512</dim>
2325
- <dim>512</dim>
2326
- <dim>3</dim>
2327
- <dim>3</dim>
2328
- </port>
2329
- </output>
2330
- </layer>
2331
- <layer id="120" name="/layer4/layer4.0/conv2/Conv/WithoutBiases" type="Convolution" version="opset1">
2332
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
2333
- <input>
2334
- <port id="0" precision="FP32">
2335
- <dim>-1</dim>
2336
- <dim>512</dim>
2337
- <dim>7</dim>
2338
- <dim>7</dim>
2339
- </port>
2340
- <port id="1" precision="FP32">
2341
- <dim>512</dim>
2342
- <dim>512</dim>
2343
- <dim>3</dim>
2344
- <dim>3</dim>
2345
- </port>
2346
- </input>
2347
- <output>
2348
- <port id="2" precision="FP32">
2349
- <dim>-1</dim>
2350
- <dim>512</dim>
2351
- <dim>7</dim>
2352
- <dim>7</dim>
2353
- </port>
2354
- </output>
2355
- </layer>
2356
- <layer id="121" name="Reshape_291_compressed" type="Const" version="opset1">
2357
- <data element_type="f16" shape="1, 512, 1, 1" offset="12640000" size="1024" />
2358
- <output>
2359
- <port id="0" precision="FP16">
2360
- <dim>1</dim>
2361
- <dim>512</dim>
2362
- <dim>1</dim>
2363
- <dim>1</dim>
2364
- </port>
2365
- </output>
2366
- </layer>
2367
- <layer id="122" name="Reshape_291" type="Convert" version="opset1">
2368
- <data destination_type="f32" />
2369
- <rt_info>
2370
- <attribute name="decompression" version="0" />
2371
- </rt_info>
2372
- <input>
2373
- <port id="0" precision="FP16">
2374
- <dim>1</dim>
2375
- <dim>512</dim>
2376
- <dim>1</dim>
2377
- <dim>1</dim>
2378
- </port>
2379
- </input>
2380
- <output>
2381
- <port id="1" precision="FP32">
2382
- <dim>1</dim>
2383
- <dim>512</dim>
2384
- <dim>1</dim>
2385
- <dim>1</dim>
2386
- </port>
2387
- </output>
2388
- </layer>
2389
- <layer id="123" name="/layer4/layer4.0/conv2/Conv" type="Add" version="opset1">
2390
- <data auto_broadcast="numpy" />
2391
- <input>
2392
- <port id="0" precision="FP32">
2393
- <dim>-1</dim>
2394
- <dim>512</dim>
2395
- <dim>7</dim>
2396
- <dim>7</dim>
2397
- </port>
2398
- <port id="1" precision="FP32">
2399
- <dim>1</dim>
2400
- <dim>512</dim>
2401
- <dim>1</dim>
2402
- <dim>1</dim>
2403
- </port>
2404
- </input>
2405
- <output>
2406
- <port id="2" precision="FP32" names="/layer4/layer4.0/conv2/Conv_output_0">
2407
- <dim>-1</dim>
2408
- <dim>512</dim>
2409
- <dim>7</dim>
2410
- <dim>7</dim>
2411
- </port>
2412
- </output>
2413
- </layer>
2414
- <layer id="124" name="onnx::Conv_244_compressed" type="Const" version="opset1">
2415
- <data element_type="f16" shape="512, 256, 1, 1" offset="12641024" size="262144" />
2416
- <output>
2417
- <port id="0" precision="FP16" names="onnx::Conv_244">
2418
- <dim>512</dim>
2419
- <dim>256</dim>
2420
- <dim>1</dim>
2421
- <dim>1</dim>
2422
- </port>
2423
- </output>
2424
- </layer>
2425
- <layer id="125" name="onnx::Conv_244" type="Convert" version="opset1">
2426
- <data destination_type="f32" />
2427
- <rt_info>
2428
- <attribute name="decompression" version="0" />
2429
- </rt_info>
2430
- <input>
2431
- <port id="0" precision="FP16">
2432
- <dim>512</dim>
2433
- <dim>256</dim>
2434
- <dim>1</dim>
2435
- <dim>1</dim>
2436
- </port>
2437
- </input>
2438
- <output>
2439
- <port id="1" precision="FP32">
2440
- <dim>512</dim>
2441
- <dim>256</dim>
2442
- <dim>1</dim>
2443
- <dim>1</dim>
2444
- </port>
2445
- </output>
2446
- </layer>
2447
- <layer id="126" name="/layer4/layer4.0/downsample/downsample.0/Conv/WithoutBiases" type="Convolution" version="opset1">
2448
- <data strides="2, 2" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
2449
- <input>
2450
- <port id="0" precision="FP32">
2451
- <dim>-1</dim>
2452
- <dim>256</dim>
2453
- <dim>14</dim>
2454
- <dim>14</dim>
2455
- </port>
2456
- <port id="1" precision="FP32">
2457
- <dim>512</dim>
2458
- <dim>256</dim>
2459
- <dim>1</dim>
2460
- <dim>1</dim>
2461
- </port>
2462
- </input>
2463
- <output>
2464
- <port id="2" precision="FP32">
2465
- <dim>-1</dim>
2466
- <dim>512</dim>
2467
- <dim>7</dim>
2468
- <dim>7</dim>
2469
- </port>
2470
- </output>
2471
- </layer>
2472
- <layer id="127" name="Reshape_275_compressed" type="Const" version="opset1">
2473
- <data element_type="f16" shape="1, 512, 1, 1" offset="12903168" size="1024" />
2474
- <output>
2475
- <port id="0" precision="FP16">
2476
- <dim>1</dim>
2477
- <dim>512</dim>
2478
- <dim>1</dim>
2479
- <dim>1</dim>
2480
- </port>
2481
- </output>
2482
- </layer>
2483
- <layer id="128" name="Reshape_275" type="Convert" version="opset1">
2484
- <data destination_type="f32" />
2485
- <rt_info>
2486
- <attribute name="decompression" version="0" />
2487
- </rt_info>
2488
- <input>
2489
- <port id="0" precision="FP16">
2490
- <dim>1</dim>
2491
- <dim>512</dim>
2492
- <dim>1</dim>
2493
- <dim>1</dim>
2494
- </port>
2495
- </input>
2496
- <output>
2497
- <port id="1" precision="FP32">
2498
- <dim>1</dim>
2499
- <dim>512</dim>
2500
- <dim>1</dim>
2501
- <dim>1</dim>
2502
- </port>
2503
- </output>
2504
- </layer>
2505
- <layer id="129" name="/layer4/layer4.0/downsample/downsample.0/Conv" type="Add" version="opset1">
2506
- <data auto_broadcast="numpy" />
2507
- <input>
2508
- <port id="0" precision="FP32">
2509
- <dim>-1</dim>
2510
- <dim>512</dim>
2511
- <dim>7</dim>
2512
- <dim>7</dim>
2513
- </port>
2514
- <port id="1" precision="FP32">
2515
- <dim>1</dim>
2516
- <dim>512</dim>
2517
- <dim>1</dim>
2518
- <dim>1</dim>
2519
- </port>
2520
- </input>
2521
- <output>
2522
- <port id="2" precision="FP32" names="/layer4/layer4.0/downsample/downsample.0/Conv_output_0">
2523
- <dim>-1</dim>
2524
- <dim>512</dim>
2525
- <dim>7</dim>
2526
- <dim>7</dim>
2527
- </port>
2528
- </output>
2529
- </layer>
2530
- <layer id="130" name="/layer4/layer4.0/Add" type="Add" version="opset1">
2531
- <data auto_broadcast="numpy" />
2532
- <input>
2533
- <port id="0" precision="FP32">
2534
- <dim>-1</dim>
2535
- <dim>512</dim>
2536
- <dim>7</dim>
2537
- <dim>7</dim>
2538
- </port>
2539
- <port id="1" precision="FP32">
2540
- <dim>-1</dim>
2541
- <dim>512</dim>
2542
- <dim>7</dim>
2543
- <dim>7</dim>
2544
- </port>
2545
- </input>
2546
- <output>
2547
- <port id="2" precision="FP32" names="/layer4/layer4.0/Add_output_0">
2548
- <dim>-1</dim>
2549
- <dim>512</dim>
2550
- <dim>7</dim>
2551
- <dim>7</dim>
2552
- </port>
2553
- </output>
2554
- </layer>
2555
- <layer id="131" name="/layer4/layer4.0/relu_1/Relu" type="ReLU" version="opset1">
2556
- <input>
2557
- <port id="0" precision="FP32">
2558
- <dim>-1</dim>
2559
- <dim>512</dim>
2560
- <dim>7</dim>
2561
- <dim>7</dim>
2562
- </port>
2563
- </input>
2564
- <output>
2565
- <port id="1" precision="FP32" names="/layer4/layer4.0/relu_1/Relu_output_0">
2566
- <dim>-1</dim>
2567
- <dim>512</dim>
2568
- <dim>7</dim>
2569
- <dim>7</dim>
2570
- </port>
2571
- </output>
2572
- </layer>
2573
- <layer id="132" name="onnx::Conv_247_compressed" type="Const" version="opset1">
2574
- <data element_type="f16" shape="512, 512, 3, 3" offset="12904192" size="4718592" />
2575
- <output>
2576
- <port id="0" precision="FP16" names="onnx::Conv_247">
2577
- <dim>512</dim>
2578
- <dim>512</dim>
2579
- <dim>3</dim>
2580
- <dim>3</dim>
2581
- </port>
2582
- </output>
2583
- </layer>
2584
- <layer id="133" name="onnx::Conv_247" type="Convert" version="opset1">
2585
- <data destination_type="f32" />
2586
- <rt_info>
2587
- <attribute name="decompression" version="0" />
2588
- </rt_info>
2589
- <input>
2590
- <port id="0" precision="FP16">
2591
- <dim>512</dim>
2592
- <dim>512</dim>
2593
- <dim>3</dim>
2594
- <dim>3</dim>
2595
- </port>
2596
- </input>
2597
- <output>
2598
- <port id="1" precision="FP32">
2599
- <dim>512</dim>
2600
- <dim>512</dim>
2601
- <dim>3</dim>
2602
- <dim>3</dim>
2603
- </port>
2604
- </output>
2605
- </layer>
2606
- <layer id="134" name="/layer4/layer4.1/conv1/Conv/WithoutBiases" type="Convolution" version="opset1">
2607
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
2608
- <input>
2609
- <port id="0" precision="FP32">
2610
- <dim>-1</dim>
2611
- <dim>512</dim>
2612
- <dim>7</dim>
2613
- <dim>7</dim>
2614
- </port>
2615
- <port id="1" precision="FP32">
2616
- <dim>512</dim>
2617
- <dim>512</dim>
2618
- <dim>3</dim>
2619
- <dim>3</dim>
2620
- </port>
2621
- </input>
2622
- <output>
2623
- <port id="2" precision="FP32">
2624
- <dim>-1</dim>
2625
- <dim>512</dim>
2626
- <dim>7</dim>
2627
- <dim>7</dim>
2628
- </port>
2629
- </output>
2630
- </layer>
2631
- <layer id="135" name="Reshape_308_compressed" type="Const" version="opset1">
2632
- <data element_type="f16" shape="1, 512, 1, 1" offset="17622784" size="1024" />
2633
- <output>
2634
- <port id="0" precision="FP16">
2635
- <dim>1</dim>
2636
- <dim>512</dim>
2637
- <dim>1</dim>
2638
- <dim>1</dim>
2639
- </port>
2640
- </output>
2641
- </layer>
2642
- <layer id="136" name="Reshape_308" type="Convert" version="opset1">
2643
- <data destination_type="f32" />
2644
- <rt_info>
2645
- <attribute name="decompression" version="0" />
2646
- </rt_info>
2647
- <input>
2648
- <port id="0" precision="FP16">
2649
- <dim>1</dim>
2650
- <dim>512</dim>
2651
- <dim>1</dim>
2652
- <dim>1</dim>
2653
- </port>
2654
- </input>
2655
- <output>
2656
- <port id="1" precision="FP32">
2657
- <dim>1</dim>
2658
- <dim>512</dim>
2659
- <dim>1</dim>
2660
- <dim>1</dim>
2661
- </port>
2662
- </output>
2663
- </layer>
2664
- <layer id="137" name="/layer4/layer4.1/conv1/Conv" type="Add" version="opset1">
2665
- <data auto_broadcast="numpy" />
2666
- <input>
2667
- <port id="0" precision="FP32">
2668
- <dim>-1</dim>
2669
- <dim>512</dim>
2670
- <dim>7</dim>
2671
- <dim>7</dim>
2672
- </port>
2673
- <port id="1" precision="FP32">
2674
- <dim>1</dim>
2675
- <dim>512</dim>
2676
- <dim>1</dim>
2677
- <dim>1</dim>
2678
- </port>
2679
- </input>
2680
- <output>
2681
- <port id="2" precision="FP32" names="/layer4/layer4.1/conv1/Conv_output_0">
2682
- <dim>-1</dim>
2683
- <dim>512</dim>
2684
- <dim>7</dim>
2685
- <dim>7</dim>
2686
- </port>
2687
- </output>
2688
- </layer>
2689
- <layer id="138" name="/layer4/layer4.1/relu/Relu" type="ReLU" version="opset1">
2690
- <input>
2691
- <port id="0" precision="FP32">
2692
- <dim>-1</dim>
2693
- <dim>512</dim>
2694
- <dim>7</dim>
2695
- <dim>7</dim>
2696
- </port>
2697
- </input>
2698
- <output>
2699
- <port id="1" precision="FP32" names="/layer4/layer4.1/relu/Relu_output_0">
2700
- <dim>-1</dim>
2701
- <dim>512</dim>
2702
- <dim>7</dim>
2703
- <dim>7</dim>
2704
- </port>
2705
- </output>
2706
- </layer>
2707
- <layer id="139" name="onnx::Conv_250_compressed" type="Const" version="opset1">
2708
- <data element_type="f16" shape="512, 512, 3, 3" offset="17623808" size="4718592" />
2709
- <output>
2710
- <port id="0" precision="FP16" names="onnx::Conv_250">
2711
- <dim>512</dim>
2712
- <dim>512</dim>
2713
- <dim>3</dim>
2714
- <dim>3</dim>
2715
- </port>
2716
- </output>
2717
- </layer>
2718
- <layer id="140" name="onnx::Conv_250" type="Convert" version="opset1">
2719
- <data destination_type="f32" />
2720
- <rt_info>
2721
- <attribute name="decompression" version="0" />
2722
- </rt_info>
2723
- <input>
2724
- <port id="0" precision="FP16">
2725
- <dim>512</dim>
2726
- <dim>512</dim>
2727
- <dim>3</dim>
2728
- <dim>3</dim>
2729
- </port>
2730
- </input>
2731
- <output>
2732
- <port id="1" precision="FP32">
2733
- <dim>512</dim>
2734
- <dim>512</dim>
2735
- <dim>3</dim>
2736
- <dim>3</dim>
2737
- </port>
2738
- </output>
2739
- </layer>
2740
- <layer id="141" name="/layer4/layer4.1/conv2/Conv/WithoutBiases" type="Convolution" version="opset1">
2741
- <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
2742
- <input>
2743
- <port id="0" precision="FP32">
2744
- <dim>-1</dim>
2745
- <dim>512</dim>
2746
- <dim>7</dim>
2747
- <dim>7</dim>
2748
- </port>
2749
- <port id="1" precision="FP32">
2750
- <dim>512</dim>
2751
- <dim>512</dim>
2752
- <dim>3</dim>
2753
- <dim>3</dim>
2754
- </port>
2755
- </input>
2756
- <output>
2757
- <port id="2" precision="FP32">
2758
- <dim>-1</dim>
2759
- <dim>512</dim>
2760
- <dim>7</dim>
2761
- <dim>7</dim>
2762
- </port>
2763
- </output>
2764
- </layer>
2765
- <layer id="142" name="Reshape_324_compressed" type="Const" version="opset1">
2766
- <data element_type="f16" shape="1, 512, 1, 1" offset="22342400" size="1024" />
2767
- <output>
2768
- <port id="0" precision="FP16">
2769
- <dim>1</dim>
2770
- <dim>512</dim>
2771
- <dim>1</dim>
2772
- <dim>1</dim>
2773
- </port>
2774
- </output>
2775
- </layer>
2776
- <layer id="143" name="Reshape_324" type="Convert" version="opset1">
2777
- <data destination_type="f32" />
2778
- <rt_info>
2779
- <attribute name="decompression" version="0" />
2780
- </rt_info>
2781
- <input>
2782
- <port id="0" precision="FP16">
2783
- <dim>1</dim>
2784
- <dim>512</dim>
2785
- <dim>1</dim>
2786
- <dim>1</dim>
2787
- </port>
2788
- </input>
2789
- <output>
2790
- <port id="1" precision="FP32">
2791
- <dim>1</dim>
2792
- <dim>512</dim>
2793
- <dim>1</dim>
2794
- <dim>1</dim>
2795
- </port>
2796
- </output>
2797
- </layer>
2798
- <layer id="144" name="/layer4/layer4.1/conv2/Conv" type="Add" version="opset1">
2799
- <data auto_broadcast="numpy" />
2800
- <input>
2801
- <port id="0" precision="FP32">
2802
- <dim>-1</dim>
2803
- <dim>512</dim>
2804
- <dim>7</dim>
2805
- <dim>7</dim>
2806
- </port>
2807
- <port id="1" precision="FP32">
2808
- <dim>1</dim>
2809
- <dim>512</dim>
2810
- <dim>1</dim>
2811
- <dim>1</dim>
2812
- </port>
2813
- </input>
2814
- <output>
2815
- <port id="2" precision="FP32" names="/layer4/layer4.1/conv2/Conv_output_0">
2816
- <dim>-1</dim>
2817
- <dim>512</dim>
2818
- <dim>7</dim>
2819
- <dim>7</dim>
2820
- </port>
2821
- </output>
2822
- </layer>
2823
- <layer id="145" name="/layer4/layer4.1/Add" type="Add" version="opset1">
2824
- <data auto_broadcast="numpy" />
2825
- <input>
2826
- <port id="0" precision="FP32">
2827
- <dim>-1</dim>
2828
- <dim>512</dim>
2829
- <dim>7</dim>
2830
- <dim>7</dim>
2831
- </port>
2832
- <port id="1" precision="FP32">
2833
- <dim>-1</dim>
2834
- <dim>512</dim>
2835
- <dim>7</dim>
2836
- <dim>7</dim>
2837
- </port>
2838
- </input>
2839
- <output>
2840
- <port id="2" precision="FP32" names="/layer4/layer4.1/Add_output_0">
2841
- <dim>-1</dim>
2842
- <dim>512</dim>
2843
- <dim>7</dim>
2844
- <dim>7</dim>
2845
- </port>
2846
- </output>
2847
- </layer>
2848
- <layer id="146" name="/layer4/layer4.1/relu_1/Relu" type="ReLU" version="opset1">
2849
- <input>
2850
- <port id="0" precision="FP32">
2851
- <dim>-1</dim>
2852
- <dim>512</dim>
2853
- <dim>7</dim>
2854
- <dim>7</dim>
2855
- </port>
2856
- </input>
2857
- <output>
2858
- <port id="1" precision="FP32" names="/layer4/layer4.1/relu_1/Relu_output_0">
2859
- <dim>-1</dim>
2860
- <dim>512</dim>
2861
- <dim>7</dim>
2862
- <dim>7</dim>
2863
- </port>
2864
- </output>
2865
- </layer>
2866
- <layer id="147" name="Range_334" type="Const" version="opset1">
2867
- <data element_type="i64" shape="2" offset="22343424" size="16" />
2868
- <output>
2869
- <port id="0" precision="I64">
2870
- <dim>2</dim>
2871
- </port>
2872
- </output>
2873
- </layer>
2874
- <layer id="148" name="/avgpool/GlobalAveragePool" type="ReduceMean" version="opset1">
2875
- <data keep_dims="true" />
2876
- <input>
2877
- <port id="0" precision="FP32">
2878
- <dim>-1</dim>
2879
- <dim>512</dim>
2880
- <dim>7</dim>
2881
- <dim>7</dim>
2882
- </port>
2883
- <port id="1" precision="I64">
2884
- <dim>2</dim>
2885
- </port>
2886
- </input>
2887
- <output>
2888
- <port id="2" precision="FP32" names="/avgpool/GlobalAveragePool_output_0">
2889
- <dim>-1</dim>
2890
- <dim>512</dim>
2891
- <dim>1</dim>
2892
- <dim>1</dim>
2893
- </port>
2894
- </output>
2895
- </layer>
2896
- <layer id="149" name="Concat_751" type="Const" version="opset1">
2897
- <data element_type="i64" shape="2" offset="22343440" size="16" />
2898
- <rt_info>
2899
- <attribute name="precise" version="0" />
2900
- </rt_info>
2901
- <output>
2902
- <port id="0" precision="I64">
2903
- <dim>2</dim>
2904
- </port>
2905
- </output>
2906
- </layer>
2907
- <layer id="150" name="/Flatten" type="Reshape" version="opset1">
2908
- <data special_zero="true" />
2909
- <input>
2910
- <port id="0" precision="FP32">
2911
- <dim>-1</dim>
2912
- <dim>512</dim>
2913
- <dim>1</dim>
2914
- <dim>1</dim>
2915
- </port>
2916
- <port id="1" precision="I64">
2917
- <dim>2</dim>
2918
- </port>
2919
- </input>
2920
- <output>
2921
- <port id="2" precision="FP32" names="/Flatten_output_0">
2922
- <dim>-1</dim>
2923
- <dim>512</dim>
2924
- </port>
2925
- </output>
2926
- </layer>
2927
- <layer id="151" name="fc.weight_compressed" type="Const" version="opset1">
2928
- <data element_type="f16" shape="10, 512" offset="22343456" size="10240" />
2929
- <output>
2930
- <port id="0" precision="FP16" names="fc.weight">
2931
- <dim>10</dim>
2932
- <dim>512</dim>
2933
- </port>
2934
- </output>
2935
- </layer>
2936
- <layer id="152" name="fc.weight" type="Convert" version="opset1">
2937
- <data destination_type="f32" />
2938
- <rt_info>
2939
- <attribute name="decompression" version="0" />
2940
- </rt_info>
2941
- <input>
2942
- <port id="0" precision="FP16">
2943
- <dim>10</dim>
2944
- <dim>512</dim>
2945
- </port>
2946
- </input>
2947
- <output>
2948
- <port id="1" precision="FP32">
2949
- <dim>10</dim>
2950
- <dim>512</dim>
2951
- </port>
2952
- </output>
2953
- </layer>
2954
- <layer id="153" name="/fc/Gemm/WithoutBiases" type="MatMul" version="opset1">
2955
- <data transpose_a="false" transpose_b="true" />
2956
- <input>
2957
- <port id="0" precision="FP32">
2958
- <dim>-1</dim>
2959
- <dim>512</dim>
2960
- </port>
2961
- <port id="1" precision="FP32">
2962
- <dim>10</dim>
2963
- <dim>512</dim>
2964
- </port>
2965
- </input>
2966
- <output>
2967
- <port id="2" precision="FP32">
2968
- <dim>-1</dim>
2969
- <dim>10</dim>
2970
- </port>
2971
- </output>
2972
- </layer>
2973
- <layer id="154" name="Constant_2852" type="Const" version="opset1">
2974
- <data element_type="f32" shape="1, 10" offset="22353696" size="40" />
2975
- <output>
2976
- <port id="0" precision="FP32">
2977
- <dim>1</dim>
2978
- <dim>10</dim>
2979
- </port>
2980
- </output>
2981
- </layer>
2982
- <layer id="155" name="logits" type="Add" version="opset1">
2983
- <data auto_broadcast="numpy" />
2984
- <input>
2985
- <port id="0" precision="FP32">
2986
- <dim>-1</dim>
2987
- <dim>10</dim>
2988
- </port>
2989
- <port id="1" precision="FP32">
2990
- <dim>1</dim>
2991
- <dim>10</dim>
2992
- </port>
2993
- </input>
2994
- <output>
2995
- <port id="2" precision="FP32" names="logits">
2996
- <dim>-1</dim>
2997
- <dim>10</dim>
2998
- </port>
2999
- </output>
3000
- </layer>
3001
- <layer id="156" name="logits/sink_port_0" type="Result" version="opset1" output_names="logits">
3002
- <input>
3003
- <port id="0" precision="FP32">
3004
- <dim>-1</dim>
3005
- <dim>10</dim>
3006
- </port>
3007
- </input>
3008
- </layer>
3009
- </layers>
3010
- <edges>
3011
- <edge from-layer="0" from-port="0" to-layer="3" to-port="0" />
3012
- <edge from-layer="1" from-port="0" to-layer="2" to-port="0" />
3013
- <edge from-layer="2" from-port="1" to-layer="3" to-port="1" />
3014
- <edge from-layer="3" from-port="2" to-layer="6" to-port="0" />
3015
- <edge from-layer="4" from-port="0" to-layer="5" to-port="0" />
3016
- <edge from-layer="5" from-port="1" to-layer="6" to-port="1" />
3017
- <edge from-layer="6" from-port="2" to-layer="7" to-port="0" />
3018
- <edge from-layer="7" from-port="1" to-layer="8" to-port="0" />
3019
- <edge from-layer="8" from-port="1" to-layer="11" to-port="0" />
3020
- <edge from-layer="8" from-port="1" to-layer="22" to-port="1" />
3021
- <edge from-layer="9" from-port="0" to-layer="10" to-port="0" />
3022
- <edge from-layer="10" from-port="1" to-layer="11" to-port="1" />
3023
- <edge from-layer="11" from-port="2" to-layer="14" to-port="0" />
3024
- <edge from-layer="12" from-port="0" to-layer="13" to-port="0" />
3025
- <edge from-layer="13" from-port="1" to-layer="14" to-port="1" />
3026
- <edge from-layer="14" from-port="2" to-layer="15" to-port="0" />
3027
- <edge from-layer="15" from-port="1" to-layer="18" to-port="0" />
3028
- <edge from-layer="16" from-port="0" to-layer="17" to-port="0" />
3029
- <edge from-layer="17" from-port="1" to-layer="18" to-port="1" />
3030
- <edge from-layer="18" from-port="2" to-layer="21" to-port="0" />
3031
- <edge from-layer="19" from-port="0" to-layer="20" to-port="0" />
3032
- <edge from-layer="20" from-port="1" to-layer="21" to-port="1" />
3033
- <edge from-layer="21" from-port="2" to-layer="22" to-port="0" />
3034
- <edge from-layer="22" from-port="2" to-layer="23" to-port="0" />
3035
- <edge from-layer="23" from-port="1" to-layer="26" to-port="0" />
3036
- <edge from-layer="23" from-port="1" to-layer="37" to-port="1" />
3037
- <edge from-layer="24" from-port="0" to-layer="25" to-port="0" />
3038
- <edge from-layer="25" from-port="1" to-layer="26" to-port="1" />
3039
- <edge from-layer="26" from-port="2" to-layer="29" to-port="0" />
3040
- <edge from-layer="27" from-port="0" to-layer="28" to-port="0" />
3041
- <edge from-layer="28" from-port="1" to-layer="29" to-port="1" />
3042
- <edge from-layer="29" from-port="2" to-layer="30" to-port="0" />
3043
- <edge from-layer="30" from-port="1" to-layer="33" to-port="0" />
3044
- <edge from-layer="31" from-port="0" to-layer="32" to-port="0" />
3045
- <edge from-layer="32" from-port="1" to-layer="33" to-port="1" />
3046
- <edge from-layer="33" from-port="2" to-layer="36" to-port="0" />
3047
- <edge from-layer="34" from-port="0" to-layer="35" to-port="0" />
3048
- <edge from-layer="35" from-port="1" to-layer="36" to-port="1" />
3049
- <edge from-layer="36" from-port="2" to-layer="37" to-port="0" />
3050
- <edge from-layer="37" from-port="2" to-layer="38" to-port="0" />
3051
- <edge from-layer="38" from-port="1" to-layer="41" to-port="0" />
3052
- <edge from-layer="38" from-port="1" to-layer="54" to-port="0" />
3053
- <edge from-layer="39" from-port="0" to-layer="40" to-port="0" />
3054
- <edge from-layer="40" from-port="1" to-layer="41" to-port="1" />
3055
- <edge from-layer="41" from-port="2" to-layer="44" to-port="0" />
3056
- <edge from-layer="42" from-port="0" to-layer="43" to-port="0" />
3057
- <edge from-layer="43" from-port="1" to-layer="44" to-port="1" />
3058
- <edge from-layer="44" from-port="2" to-layer="45" to-port="0" />
3059
- <edge from-layer="45" from-port="1" to-layer="48" to-port="0" />
3060
- <edge from-layer="46" from-port="0" to-layer="47" to-port="0" />
3061
- <edge from-layer="47" from-port="1" to-layer="48" to-port="1" />
3062
- <edge from-layer="48" from-port="2" to-layer="51" to-port="0" />
3063
- <edge from-layer="49" from-port="0" to-layer="50" to-port="0" />
3064
- <edge from-layer="50" from-port="1" to-layer="51" to-port="1" />
3065
- <edge from-layer="51" from-port="2" to-layer="58" to-port="0" />
3066
- <edge from-layer="52" from-port="0" to-layer="53" to-port="0" />
3067
- <edge from-layer="53" from-port="1" to-layer="54" to-port="1" />
3068
- <edge from-layer="54" from-port="2" to-layer="57" to-port="0" />
3069
- <edge from-layer="55" from-port="0" to-layer="56" to-port="0" />
3070
- <edge from-layer="56" from-port="1" to-layer="57" to-port="1" />
3071
- <edge from-layer="57" from-port="2" to-layer="58" to-port="1" />
3072
- <edge from-layer="58" from-port="2" to-layer="59" to-port="0" />
3073
- <edge from-layer="59" from-port="1" to-layer="62" to-port="0" />
3074
- <edge from-layer="59" from-port="1" to-layer="73" to-port="1" />
3075
- <edge from-layer="60" from-port="0" to-layer="61" to-port="0" />
3076
- <edge from-layer="61" from-port="1" to-layer="62" to-port="1" />
3077
- <edge from-layer="62" from-port="2" to-layer="65" to-port="0" />
3078
- <edge from-layer="63" from-port="0" to-layer="64" to-port="0" />
3079
- <edge from-layer="64" from-port="1" to-layer="65" to-port="1" />
3080
- <edge from-layer="65" from-port="2" to-layer="66" to-port="0" />
3081
- <edge from-layer="66" from-port="1" to-layer="69" to-port="0" />
3082
- <edge from-layer="67" from-port="0" to-layer="68" to-port="0" />
3083
- <edge from-layer="68" from-port="1" to-layer="69" to-port="1" />
3084
- <edge from-layer="69" from-port="2" to-layer="72" to-port="0" />
3085
- <edge from-layer="70" from-port="0" to-layer="71" to-port="0" />
3086
- <edge from-layer="71" from-port="1" to-layer="72" to-port="1" />
3087
- <edge from-layer="72" from-port="2" to-layer="73" to-port="0" />
3088
- <edge from-layer="73" from-port="2" to-layer="74" to-port="0" />
3089
- <edge from-layer="74" from-port="1" to-layer="77" to-port="0" />
3090
- <edge from-layer="74" from-port="1" to-layer="90" to-port="0" />
3091
- <edge from-layer="75" from-port="0" to-layer="76" to-port="0" />
3092
- <edge from-layer="76" from-port="1" to-layer="77" to-port="1" />
3093
- <edge from-layer="77" from-port="2" to-layer="80" to-port="0" />
3094
- <edge from-layer="78" from-port="0" to-layer="79" to-port="0" />
3095
- <edge from-layer="79" from-port="1" to-layer="80" to-port="1" />
3096
- <edge from-layer="80" from-port="2" to-layer="81" to-port="0" />
3097
- <edge from-layer="81" from-port="1" to-layer="84" to-port="0" />
3098
- <edge from-layer="82" from-port="0" to-layer="83" to-port="0" />
3099
- <edge from-layer="83" from-port="1" to-layer="84" to-port="1" />
3100
- <edge from-layer="84" from-port="2" to-layer="87" to-port="0" />
3101
- <edge from-layer="85" from-port="0" to-layer="86" to-port="0" />
3102
- <edge from-layer="86" from-port="1" to-layer="87" to-port="1" />
3103
- <edge from-layer="87" from-port="2" to-layer="94" to-port="0" />
3104
- <edge from-layer="88" from-port="0" to-layer="89" to-port="0" />
3105
- <edge from-layer="89" from-port="1" to-layer="90" to-port="1" />
3106
- <edge from-layer="90" from-port="2" to-layer="93" to-port="0" />
3107
- <edge from-layer="91" from-port="0" to-layer="92" to-port="0" />
3108
- <edge from-layer="92" from-port="1" to-layer="93" to-port="1" />
3109
- <edge from-layer="93" from-port="2" to-layer="94" to-port="1" />
3110
- <edge from-layer="94" from-port="2" to-layer="95" to-port="0" />
3111
- <edge from-layer="95" from-port="1" to-layer="98" to-port="0" />
3112
- <edge from-layer="95" from-port="1" to-layer="109" to-port="1" />
3113
- <edge from-layer="96" from-port="0" to-layer="97" to-port="0" />
3114
- <edge from-layer="97" from-port="1" to-layer="98" to-port="1" />
3115
- <edge from-layer="98" from-port="2" to-layer="101" to-port="0" />
3116
- <edge from-layer="99" from-port="0" to-layer="100" to-port="0" />
3117
- <edge from-layer="100" from-port="1" to-layer="101" to-port="1" />
3118
- <edge from-layer="101" from-port="2" to-layer="102" to-port="0" />
3119
- <edge from-layer="102" from-port="1" to-layer="105" to-port="0" />
3120
- <edge from-layer="103" from-port="0" to-layer="104" to-port="0" />
3121
- <edge from-layer="104" from-port="1" to-layer="105" to-port="1" />
3122
- <edge from-layer="105" from-port="2" to-layer="108" to-port="0" />
3123
- <edge from-layer="106" from-port="0" to-layer="107" to-port="0" />
3124
- <edge from-layer="107" from-port="1" to-layer="108" to-port="1" />
3125
- <edge from-layer="108" from-port="2" to-layer="109" to-port="0" />
3126
- <edge from-layer="109" from-port="2" to-layer="110" to-port="0" />
3127
- <edge from-layer="110" from-port="1" to-layer="113" to-port="0" />
3128
- <edge from-layer="110" from-port="1" to-layer="126" to-port="0" />
3129
- <edge from-layer="111" from-port="0" to-layer="112" to-port="0" />
3130
- <edge from-layer="112" from-port="1" to-layer="113" to-port="1" />
3131
- <edge from-layer="113" from-port="2" to-layer="116" to-port="0" />
3132
- <edge from-layer="114" from-port="0" to-layer="115" to-port="0" />
3133
- <edge from-layer="115" from-port="1" to-layer="116" to-port="1" />
3134
- <edge from-layer="116" from-port="2" to-layer="117" to-port="0" />
3135
- <edge from-layer="117" from-port="1" to-layer="120" to-port="0" />
3136
- <edge from-layer="118" from-port="0" to-layer="119" to-port="0" />
3137
- <edge from-layer="119" from-port="1" to-layer="120" to-port="1" />
3138
- <edge from-layer="120" from-port="2" to-layer="123" to-port="0" />
3139
- <edge from-layer="121" from-port="0" to-layer="122" to-port="0" />
3140
- <edge from-layer="122" from-port="1" to-layer="123" to-port="1" />
3141
- <edge from-layer="123" from-port="2" to-layer="130" to-port="0" />
3142
- <edge from-layer="124" from-port="0" to-layer="125" to-port="0" />
3143
- <edge from-layer="125" from-port="1" to-layer="126" to-port="1" />
3144
- <edge from-layer="126" from-port="2" to-layer="129" to-port="0" />
3145
- <edge from-layer="127" from-port="0" to-layer="128" to-port="0" />
3146
- <edge from-layer="128" from-port="1" to-layer="129" to-port="1" />
3147
- <edge from-layer="129" from-port="2" to-layer="130" to-port="1" />
3148
- <edge from-layer="130" from-port="2" to-layer="131" to-port="0" />
3149
- <edge from-layer="131" from-port="1" to-layer="134" to-port="0" />
3150
- <edge from-layer="131" from-port="1" to-layer="145" to-port="1" />
3151
- <edge from-layer="132" from-port="0" to-layer="133" to-port="0" />
3152
- <edge from-layer="133" from-port="1" to-layer="134" to-port="1" />
3153
- <edge from-layer="134" from-port="2" to-layer="137" to-port="0" />
3154
- <edge from-layer="135" from-port="0" to-layer="136" to-port="0" />
3155
- <edge from-layer="136" from-port="1" to-layer="137" to-port="1" />
3156
- <edge from-layer="137" from-port="2" to-layer="138" to-port="0" />
3157
- <edge from-layer="138" from-port="1" to-layer="141" to-port="0" />
3158
- <edge from-layer="139" from-port="0" to-layer="140" to-port="0" />
3159
- <edge from-layer="140" from-port="1" to-layer="141" to-port="1" />
3160
- <edge from-layer="141" from-port="2" to-layer="144" to-port="0" />
3161
- <edge from-layer="142" from-port="0" to-layer="143" to-port="0" />
3162
- <edge from-layer="143" from-port="1" to-layer="144" to-port="1" />
3163
- <edge from-layer="144" from-port="2" to-layer="145" to-port="0" />
3164
- <edge from-layer="145" from-port="2" to-layer="146" to-port="0" />
3165
- <edge from-layer="146" from-port="1" to-layer="148" to-port="0" />
3166
- <edge from-layer="147" from-port="0" to-layer="148" to-port="1" />
3167
- <edge from-layer="148" from-port="2" to-layer="150" to-port="0" />
3168
- <edge from-layer="149" from-port="0" to-layer="150" to-port="1" />
3169
- <edge from-layer="150" from-port="2" to-layer="153" to-port="0" />
3170
- <edge from-layer="151" from-port="0" to-layer="152" to-port="0" />
3171
- <edge from-layer="152" from-port="1" to-layer="153" to-port="1" />
3172
- <edge from-layer="153" from-port="2" to-layer="155" to-port="0" />
3173
- <edge from-layer="154" from-port="0" to-layer="155" to-port="1" />
3174
- <edge from-layer="155" from-port="2" to-layer="156" to-port="0" />
3175
- </edges>
3176
- <rt_info>
3177
- <info name="OpenVINO Runtime" value="2026.2.1-21919-ede283a88e3-releases/2026/2" />
3178
- <Runtime_version value="2026.2.1-21919-ede283a88e3-releases/2026/2" />
3179
- <conversion_parameters>
3180
- <input_model value="DIR/animal-cls-v1.onnx" />
3181
- <is_python_object value="False" />
3182
- </conversion_parameters>
3183
- <framework>
3184
- <labels value="[&quot;butterfly&quot;, &quot;cat&quot;, &quot;chicken&quot;, &quot;cow&quot;, &quot;dog&quot;, &quot;elephant&quot;, &quot;horse&quot;, &quot;sheep&quot;, &quot;spider&quot;, &quot;squirrel&quot;]" />
3185
- <preprocess value="imagenet_norm;224;RGB;NCHW" />
3186
- </framework>
3187
- </rt_info>
3188
- </net>