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move model to onnx format

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  1. examples/AFRICAN OYESTER CATCHER.jpg +0 -0
  2. examples/HOUSE SPARROW.jpg +0 -0
  3. examples/PEACOCK.jpg +0 -0
  4. examples/VERMILION FLYCATCHER.jpg +0 -0
  5. examples/WILD TURKEY.jpg +0 -0
  6. model/birds_name_mapping.json +452 -0
  7. notebooks/onnx-testing.ipynb +0 -0
  8. notebooks/pytorch-birds-resnet34.ipynb +0 -0
  9. notebooks/torch-to-onnx.ipynb +389 -0
examples/AFRICAN OYESTER CATCHER.jpg ADDED
examples/HOUSE SPARROW.jpg ADDED
examples/PEACOCK.jpg ADDED
examples/VERMILION FLYCATCHER.jpg ADDED
examples/WILD TURKEY.jpg ADDED
model/birds_name_mapping.json ADDED
@@ -0,0 +1,452 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "0": "ABBOTTS BABBLER",
3
+ "1": "ABBOTTS BOOBY",
4
+ "2": "ABYSSINIAN GROUND HORNBILL",
5
+ "3": "AFRICAN CROWNED CRANE",
6
+ "4": "AFRICAN EMERALD CUCKOO",
7
+ "5": "AFRICAN FIREFINCH",
8
+ "6": "AFRICAN OYSTER CATCHER",
9
+ "7": "AFRICAN PIED HORNBILL",
10
+ "8": "ALBATROSS",
11
+ "9": "ALBERTS TOWHEE",
12
+ "10": "ALEXANDRINE PARAKEET",
13
+ "11": "ALPINE CHOUGH",
14
+ "12": "ALTAMIRA YELLOWTHROAT",
15
+ "13": "AMERICAN AVOCET",
16
+ "14": "AMERICAN BITTERN",
17
+ "15": "AMERICAN COOT",
18
+ "16": "AMERICAN FLAMINGO",
19
+ "17": "AMERICAN GOLDFINCH",
20
+ "18": "AMERICAN KESTREL",
21
+ "19": "AMERICAN PIPIT",
22
+ "20": "AMERICAN REDSTART",
23
+ "21": "AMERICAN WIGEON",
24
+ "22": "AMETHYST WOODSTAR",
25
+ "23": "ANDEAN GOOSE",
26
+ "24": "ANDEAN LAPWING",
27
+ "25": "ANDEAN SISKIN",
28
+ "26": "ANHINGA",
29
+ "27": "ANIANIAU",
30
+ "28": "ANNAS HUMMINGBIRD",
31
+ "29": "ANTBIRD",
32
+ "30": "ANTILLEAN EUPHONIA",
33
+ "31": "APAPANE",
34
+ "32": "APOSTLEBIRD",
35
+ "33": "ARARIPE MANAKIN",
36
+ "34": "ASHY STORM PETREL",
37
+ "35": "ASHY THRUSHBIRD",
38
+ "36": "ASIAN CRESTED IBIS",
39
+ "37": "ASIAN DOLLARD BIRD",
40
+ "38": "AUCKLAND SHAQ",
41
+ "39": "AUSTRAL CANASTERO",
42
+ "40": "AUSTRALASIAN FIGBIRD",
43
+ "41": "AVADAVAT",
44
+ "42": "AZARAS SPINETAIL",
45
+ "43": "AZURE BREASTED PITTA",
46
+ "44": "AZURE JAY",
47
+ "45": "AZURE TANAGER",
48
+ "46": "AZURE TIT",
49
+ "47": "BAIKAL TEAL",
50
+ "48": "BALD EAGLE",
51
+ "49": "BALD IBIS",
52
+ "50": "BALI STARLING",
53
+ "51": "BALTIMORE ORIOLE",
54
+ "52": "BANANAQUIT",
55
+ "53": "BAND TAILED GUAN",
56
+ "54": "BANDED BROADBILL",
57
+ "55": "BANDED PITA",
58
+ "56": "BANDED STILT",
59
+ "57": "BAR-TAILED GODWIT",
60
+ "58": "BARN OWL",
61
+ "59": "BARN SWALLOW",
62
+ "60": "BARRED PUFFBIRD",
63
+ "61": "BARROWS GOLDENEYE",
64
+ "62": "BAY-BREASTED WARBLER",
65
+ "63": "BEARDED BARBET",
66
+ "64": "BEARDED BELLBIRD",
67
+ "65": "BEARDED REEDLING",
68
+ "66": "BELTED KINGFISHER",
69
+ "67": "BIRD OF PARADISE",
70
+ "68": "BLACK & YELLOW BROADBILL",
71
+ "69": "BLACK BAZA",
72
+ "70": "BLACK COCKATO",
73
+ "71": "BLACK FRANCOLIN",
74
+ "72": "BLACK SKIMMER",
75
+ "73": "BLACK SWAN",
76
+ "74": "BLACK TAIL CRAKE",
77
+ "75": "BLACK THROATED BUSHTIT",
78
+ "76": "BLACK THROATED WARBLER",
79
+ "77": "BLACK VENTED SHEARWATER",
80
+ "78": "BLACK VULTURE",
81
+ "79": "BLACK-CAPPED CHICKADEE",
82
+ "80": "BLACK-NECKED GREBE",
83
+ "81": "BLACK-THROATED SPARROW",
84
+ "82": "BLACKBURNIAM WARBLER",
85
+ "83": "BLONDE CRESTED WOODPECKER",
86
+ "84": "BLOOD PHEASANT",
87
+ "85": "BLUE COAU",
88
+ "86": "BLUE DACNIS",
89
+ "87": "BLUE GROUSE",
90
+ "88": "BLUE HERON",
91
+ "89": "BLUE MALKOHA",
92
+ "90": "BLUE THROATED TOUCANET",
93
+ "91": "BOBOLINK",
94
+ "92": "BORNEAN BRISTLEHEAD",
95
+ "93": "BORNEAN LEAFBIRD",
96
+ "94": "BORNEAN PHEASANT",
97
+ "95": "BRANDT CORMARANT",
98
+ "96": "BREWERS BLACKBIRD",
99
+ "97": "BROWN CREPPER",
100
+ "98": "BROWN NOODY",
101
+ "99": "BROWN THRASHER",
102
+ "100": "BUFFLEHEAD",
103
+ "101": "BULWERS PHEASANT",
104
+ "102": "BURCHELLS COURSER",
105
+ "103": "BUSH TURKEY",
106
+ "104": "CAATINGA CACHOLOTE",
107
+ "105": "CACTUS WREN",
108
+ "106": "CALIFORNIA CONDOR",
109
+ "107": "CALIFORNIA GULL",
110
+ "108": "CALIFORNIA QUAIL",
111
+ "109": "CAMPO FLICKER",
112
+ "110": "CANARY",
113
+ "111": "CAPE GLOSSY STARLING",
114
+ "112": "CAPE LONGCLAW",
115
+ "113": "CAPE MAY WARBLER",
116
+ "114": "CAPE ROCK THRUSH",
117
+ "115": "CAPPED HERON",
118
+ "116": "CAPUCHINBIRD",
119
+ "117": "CARMINE BEE-EATER",
120
+ "118": "CASPIAN TERN",
121
+ "119": "CASSOWARY",
122
+ "120": "CEDAR WAXWING",
123
+ "121": "CERULEAN WARBLER",
124
+ "122": "CHARA DE COLLAR",
125
+ "123": "CHATTERING LORY",
126
+ "124": "CHESTNET BELLIED EUPHONIA",
127
+ "125": "CHINESE BAMBOO PARTRIDGE",
128
+ "126": "CHINESE POND HERON",
129
+ "127": "CHIPPING SPARROW",
130
+ "128": "CHUCAO TAPACULO",
131
+ "129": "CHUKAR PARTRIDGE",
132
+ "130": "CINNAMON ATTILA",
133
+ "131": "CINNAMON FLYCATCHER",
134
+ "132": "CINNAMON TEAL",
135
+ "133": "CLARKS NUTCRACKER",
136
+ "134": "COCK OF THE ROCK",
137
+ "135": "COCKATOO",
138
+ "136": "COLLARED ARACARI",
139
+ "137": "COMMON FIRECREST",
140
+ "138": "COMMON GRACKLE",
141
+ "139": "COMMON HOUSE MARTIN",
142
+ "140": "COMMON IORA",
143
+ "141": "COMMON LOON",
144
+ "142": "COMMON POORWILL",
145
+ "143": "COMMON STARLING",
146
+ "144": "COPPERY TAILED COUCAL",
147
+ "145": "CRAB PLOVER",
148
+ "146": "CRANE HAWK",
149
+ "147": "CREAM COLORED WOODPECKER",
150
+ "148": "CRESTED AUKLET",
151
+ "149": "CRESTED CARACARA",
152
+ "150": "CRESTED COUA",
153
+ "151": "CRESTED FIREBACK",
154
+ "152": "CRESTED KINGFISHER",
155
+ "153": "CRESTED NUTHATCH",
156
+ "154": "CRESTED OROPENDOLA",
157
+ "155": "CRESTED SHRIKETIT",
158
+ "156": "CRIMSON CHAT",
159
+ "157": "CRIMSON SUNBIRD",
160
+ "158": "CROW",
161
+ "159": "CROWNED PIGEON",
162
+ "160": "CUBAN TODY",
163
+ "161": "CUBAN TROGON",
164
+ "162": "CURL CRESTED ARACURI",
165
+ "163": "D-ARNAUDS BARBET",
166
+ "164": "DALMATIAN PELICAN",
167
+ "165": "DARJEELING WOODPECKER",
168
+ "166": "DARK EYED JUNCO",
169
+ "167": "DARWINS FLYCATCHER",
170
+ "168": "DAURIAN REDSTART",
171
+ "169": "DEMOISELLE CRANE",
172
+ "170": "DOUBLE BARRED FINCH",
173
+ "171": "DOUBLE BRESTED CORMARANT",
174
+ "172": "DOUBLE EYED FIG PARROT",
175
+ "173": "DOWNY WOODPECKER",
176
+ "174": "DUSKY LORY",
177
+ "175": "DUSKY ROBIN",
178
+ "176": "EARED PITA",
179
+ "177": "EASTERN BLUEBIRD",
180
+ "178": "EASTERN BLUEBONNET",
181
+ "179": "EASTERN GOLDEN WEAVER",
182
+ "180": "EASTERN MEADOWLARK",
183
+ "181": "EASTERN ROSELLA",
184
+ "182": "EASTERN TOWEE",
185
+ "183": "EASTERN WIP POOR WILL",
186
+ "184": "ECUADORIAN HILLSTAR",
187
+ "185": "EGYPTIAN GOOSE",
188
+ "186": "ELEGANT TROGON",
189
+ "187": "ELLIOTS PHEASANT",
190
+ "188": "EMERALD TANAGER",
191
+ "189": "EMPEROR PENGUIN",
192
+ "190": "EMU",
193
+ "191": "ENGGANO MYNA",
194
+ "192": "EURASIAN BULLFINCH",
195
+ "193": "EURASIAN GOLDEN ORIOLE",
196
+ "194": "EURASIAN MAGPIE",
197
+ "195": "EUROPEAN GOLDFINCH",
198
+ "196": "EUROPEAN TURTLE DOVE",
199
+ "197": "EVENING GROSBEAK",
200
+ "198": "FAIRY BLUEBIRD",
201
+ "199": "FAIRY PENGUIN",
202
+ "200": "FAIRY TERN",
203
+ "201": "FAN TAILED WIDOW",
204
+ "202": "FASCIATED WREN",
205
+ "203": "FIERY MINIVET",
206
+ "204": "FIORDLAND PENGUIN",
207
+ "205": "FIRE TAILLED MYZORNIS",
208
+ "206": "FLAME BOWERBIRD",
209
+ "207": "FLAME TANAGER",
210
+ "208": "FRIGATE",
211
+ "209": "GAMBELS QUAIL",
212
+ "210": "GANG GANG COCKATOO",
213
+ "211": "GILA WOODPECKER",
214
+ "212": "GILDED FLICKER",
215
+ "213": "GLOSSY IBIS",
216
+ "214": "GO AWAY BIRD",
217
+ "215": "GOLD WING WARBLER",
218
+ "216": "GOLDEN BOWER BIRD",
219
+ "217": "GOLDEN CHEEKED WARBLER",
220
+ "218": "GOLDEN CHLOROPHONIA",
221
+ "219": "GOLDEN EAGLE",
222
+ "220": "GOLDEN PARAKEET",
223
+ "221": "GOLDEN PHEASANT",
224
+ "222": "GOLDEN PIPIT",
225
+ "223": "GOULDIAN FINCH",
226
+ "224": "GRANDALA",
227
+ "225": "GRAY CATBIRD",
228
+ "226": "GRAY KINGBIRD",
229
+ "227": "GRAY PARTRIDGE",
230
+ "228": "GREAT GRAY OWL",
231
+ "229": "GREAT JACAMAR",
232
+ "230": "GREAT KISKADEE",
233
+ "231": "GREAT POTOO",
234
+ "232": "GREAT TINAMOU",
235
+ "233": "GREAT XENOPS",
236
+ "234": "GREATER PEWEE",
237
+ "235": "GREATOR SAGE GROUSE",
238
+ "236": "GREEN BROADBILL",
239
+ "237": "GREEN JAY",
240
+ "238": "GREEN MAGPIE",
241
+ "239": "GREY CUCKOOSHRIKE",
242
+ "240": "GREY PLOVER",
243
+ "241": "GROVED BILLED ANI",
244
+ "242": "GUINEA TURACO",
245
+ "243": "GUINEAFOWL",
246
+ "244": "GURNEYS PITTA",
247
+ "245": "GYRFALCON",
248
+ "246": "HAMERKOP",
249
+ "247": "HARLEQUIN DUCK",
250
+ "248": "HARLEQUIN QUAIL",
251
+ "249": "HARPY EAGLE",
252
+ "250": "HAWAIIAN GOOSE",
253
+ "251": "HAWFINCH",
254
+ "252": "HELMET VANGA",
255
+ "253": "HEPATIC TANAGER",
256
+ "254": "HIMALAYAN BLUETAIL",
257
+ "255": "HIMALAYAN MONAL",
258
+ "256": "HOATZIN",
259
+ "257": "HOODED MERGANSER",
260
+ "258": "HOOPOES",
261
+ "259": "HORNED GUAN",
262
+ "260": "HORNED LARK",
263
+ "261": "HORNED SUNGEM",
264
+ "262": "HOUSE FINCH",
265
+ "263": "HOUSE SPARROW",
266
+ "264": "HYACINTH MACAW",
267
+ "265": "IBERIAN MAGPIE",
268
+ "266": "IBISBILL",
269
+ "267": "IMPERIAL SHAQ",
270
+ "268": "INCA TERN",
271
+ "269": "INDIAN BUSTARD",
272
+ "270": "INDIAN PITTA",
273
+ "271": "INDIAN ROLLER",
274
+ "272": "INDIAN VULTURE",
275
+ "273": "INDIGO BUNTING",
276
+ "274": "INDIGO FLYCATCHER",
277
+ "275": "INLAND DOTTEREL",
278
+ "276": "IVORY BILLED ARACARI",
279
+ "277": "IVORY GULL",
280
+ "278": "IWI",
281
+ "279": "JABIRU",
282
+ "280": "JACK SNIPE",
283
+ "281": "JANDAYA PARAKEET",
284
+ "282": "JAPANESE ROBIN",
285
+ "283": "JAVA SPARROW",
286
+ "284": "JOCOTOCO ANTPITTA",
287
+ "285": "KAGU",
288
+ "286": "KAKAPO",
289
+ "287": "KILLDEAR",
290
+ "288": "KING EIDER",
291
+ "289": "KING VULTURE",
292
+ "290": "KIWI",
293
+ "291": "KOOKABURRA",
294
+ "292": "LARK BUNTING",
295
+ "293": "LAZULI BUNTING",
296
+ "294": "LESSER ADJUTANT",
297
+ "295": "LILAC ROLLER",
298
+ "296": "LITTLE AUK",
299
+ "297": "LOGGERHEAD SHRIKE",
300
+ "298": "LONG-EARED OWL",
301
+ "299": "MAGPIE GOOSE",
302
+ "300": "MALABAR HORNBILL",
303
+ "301": "MALACHITE KINGFISHER",
304
+ "302": "MALAGASY WHITE EYE",
305
+ "303": "MALEO",
306
+ "304": "MALLARD DUCK",
307
+ "305": "MANDRIN DUCK",
308
+ "306": "MANGROVE CUCKOO",
309
+ "307": "MARABOU STORK",
310
+ "308": "MASKED BOOBY",
311
+ "309": "MASKED LAPWING",
312
+ "310": "MCKAYS BUNTING",
313
+ "311": "MIKADO PHEASANT",
314
+ "312": "MOURNING DOVE",
315
+ "313": "MYNA",
316
+ "314": "NICOBAR PIGEON",
317
+ "315": "NOISY FRIARBIRD",
318
+ "316": "NORTHERN BEARDLESS TYRANNULET",
319
+ "317": "NORTHERN CARDINAL",
320
+ "318": "NORTHERN FLICKER",
321
+ "319": "NORTHERN FULMAR",
322
+ "320": "NORTHERN GANNET",
323
+ "321": "NORTHERN GOSHAWK",
324
+ "322": "NORTHERN JACANA",
325
+ "323": "NORTHERN MOCKINGBIRD",
326
+ "324": "NORTHERN PARULA",
327
+ "325": "NORTHERN RED BISHOP",
328
+ "326": "NORTHERN SHOVELER",
329
+ "327": "OCELLATED TURKEY",
330
+ "328": "OKINAWA RAIL",
331
+ "329": "ORANGE BRESTED BUNTING",
332
+ "330": "ORIENTAL BAY OWL",
333
+ "331": "OSPREY",
334
+ "332": "OSTRICH",
335
+ "333": "OVENBIRD",
336
+ "334": "OYSTER CATCHER",
337
+ "335": "PAINTED BUNTING",
338
+ "336": "PALILA",
339
+ "337": "PARADISE TANAGER",
340
+ "338": "PARAKETT AKULET",
341
+ "339": "PARUS MAJOR",
342
+ "340": "PATAGONIAN SIERRA FINCH",
343
+ "341": "PEACOCK",
344
+ "342": "PEREGRINE FALCON",
345
+ "343": "PHILIPPINE EAGLE",
346
+ "344": "PINK ROBIN",
347
+ "345": "POMARINE JAEGER",
348
+ "346": "PUFFIN",
349
+ "347": "PURPLE FINCH",
350
+ "348": "PURPLE GALLINULE",
351
+ "349": "PURPLE MARTIN",
352
+ "350": "PURPLE SWAMPHEN",
353
+ "351": "PYGMY KINGFISHER",
354
+ "352": "QUETZAL",
355
+ "353": "RAINBOW LORIKEET",
356
+ "354": "RAZORBILL",
357
+ "355": "RED BEARDED BEE EATER",
358
+ "356": "RED BELLIED PITTA",
359
+ "357": "RED BROWED FINCH",
360
+ "358": "RED FACED CORMORANT",
361
+ "359": "RED FACED WARBLER",
362
+ "360": "RED FODY",
363
+ "361": "RED HEADED DUCK",
364
+ "362": "RED HEADED WOODPECKER",
365
+ "363": "RED HONEY CREEPER",
366
+ "364": "RED NAPED TROGON",
367
+ "365": "RED TAILED HAWK",
368
+ "366": "RED TAILED THRUSH",
369
+ "367": "RED WINGED BLACKBIRD",
370
+ "368": "RED WISKERED BULBUL",
371
+ "369": "REGENT BOWERBIRD",
372
+ "370": "RING-NECKED PHEASANT",
373
+ "371": "ROADRUNNER",
374
+ "372": "ROBIN",
375
+ "373": "ROCK DOVE",
376
+ "374": "ROSY FACED LOVEBIRD",
377
+ "375": "ROUGH LEG BUZZARD",
378
+ "376": "ROYAL FLYCATCHER",
379
+ "377": "RUBY THROATED HUMMINGBIRD",
380
+ "378": "RUDY KINGFISHER",
381
+ "379": "RUFOUS KINGFISHER",
382
+ "380": "RUFUOS MOTMOT",
383
+ "381": "SAMATRAN THRUSH",
384
+ "382": "SAND MARTIN",
385
+ "383": "SANDHILL CRANE",
386
+ "384": "SATYR TRAGOPAN",
387
+ "385": "SCARLET CROWNED FRUIT DOVE",
388
+ "386": "SCARLET IBIS",
389
+ "387": "SCARLET MACAW",
390
+ "388": "SCARLET TANAGER",
391
+ "389": "SHOEBILL",
392
+ "390": "SHORT BILLED DOWITCHER",
393
+ "391": "SKUA",
394
+ "392": "SMITHS LONGSPUR",
395
+ "393": "SNOWY EGRET",
396
+ "394": "SNOWY OWL",
397
+ "395": "SNOWY PLOVER",
398
+ "396": "SORA",
399
+ "397": "SPANGLED COTINGA",
400
+ "398": "SPLENDID WREN",
401
+ "399": "SPOON BILED SANDPIPER",
402
+ "400": "SPOONBILL",
403
+ "401": "SPOTTED CATBIRD",
404
+ "402": "SRI LANKA BLUE MAGPIE",
405
+ "403": "STEAMER DUCK",
406
+ "404": "STORK BILLED KINGFISHER",
407
+ "405": "STRAWBERRY FINCH",
408
+ "406": "STRIPED OWL",
409
+ "407": "STRIPPED MANAKIN",
410
+ "408": "STRIPPED SWALLOW",
411
+ "409": "SUPERB STARLING",
412
+ "410": "SWINHOES PHEASANT",
413
+ "411": "TAILORBIRD",
414
+ "412": "TAIWAN MAGPIE",
415
+ "413": "TAKAHE",
416
+ "414": "TASMANIAN HEN",
417
+ "415": "TEAL DUCK",
418
+ "416": "TIT MOUSE",
419
+ "417": "TOUCHAN",
420
+ "418": "TOWNSENDS WARBLER",
421
+ "419": "TREE SWALLOW",
422
+ "420": "TRICOLORED BLACKBIRD",
423
+ "421": "TROPICAL KINGBIRD",
424
+ "422": "TRUMPTER SWAN",
425
+ "423": "TURKEY VULTURE",
426
+ "424": "TURQUOISE MOTMOT",
427
+ "425": "UMBRELLA BIRD",
428
+ "426": "VARIED THRUSH",
429
+ "427": "VEERY",
430
+ "428": "VENEZUELIAN TROUPIAL",
431
+ "429": "VERMILION FLYCATHER",
432
+ "430": "VICTORIA CROWNED PIGEON",
433
+ "431": "VIOLET GREEN SWALLOW",
434
+ "432": "VIOLET TURACO",
435
+ "433": "VULTURINE GUINEAFOWL",
436
+ "434": "WALL CREAPER",
437
+ "435": "WATTLED CURASSOW",
438
+ "436": "WATTLED LAPWING",
439
+ "437": "WHIMBREL",
440
+ "438": "WHITE BROWED CRAKE",
441
+ "439": "WHITE CHEEKED TURACO",
442
+ "440": "WHITE CRESTED HORNBILL",
443
+ "441": "WHITE NECKED RAVEN",
444
+ "442": "WHITE TAILED TROPIC",
445
+ "443": "WHITE THROATED BEE EATER",
446
+ "444": "WILD TURKEY",
447
+ "445": "WILSONS BIRD OF PARADISE",
448
+ "446": "WOOD DUCK",
449
+ "447": "YELLOW BELLIED FLOWERPECKER",
450
+ "448": "YELLOW CACIQUE",
451
+ "449": "YELLOW HEADED BLACKBIRD"
452
+ }
notebooks/onnx-testing.ipynb ADDED
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1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {},
6
+ "source": [
7
+ "# Converting PyTorch to ONNX"
8
+ ]
9
+ },
10
+ {
11
+ "cell_type": "code",
12
+ "execution_count": 1,
13
+ "metadata": {},
14
+ "outputs": [
15
+ {
16
+ "name": "stderr",
17
+ "output_type": "stream",
18
+ "text": [
19
+ "/home/gautham/.local/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
20
+ " from .autonotebook import tqdm as notebook_tqdm\n"
21
+ ]
22
+ }
23
+ ],
24
+ "source": [
25
+ "import torch\n",
26
+ "from torch import nn\n",
27
+ "from torch.nn import functional as F"
28
+ ]
29
+ },
30
+ {
31
+ "cell_type": "markdown",
32
+ "metadata": {},
33
+ "source": [
34
+ "## Defining the model"
35
+ ]
36
+ },
37
+ {
38
+ "cell_type": "code",
39
+ "execution_count": 2,
40
+ "metadata": {},
41
+ "outputs": [],
42
+ "source": [
43
+ "class DoubleConv(nn.Module):\n",
44
+ " def __init__(self, in_channels, out_channels):\n",
45
+ " super().__init__()\n",
46
+ " self.conv = nn.Sequential(\n",
47
+ " nn.Conv2d(in_channels, out_channels, 3, padding=1),\n",
48
+ " nn.BatchNorm2d(out_channels),\n",
49
+ " nn.ReLU(inplace=True),\n",
50
+ " nn.Conv2d(out_channels, out_channels, 3, padding=1),\n",
51
+ " nn.BatchNorm2d(out_channels),\n",
52
+ " nn.ReLU(inplace=True)\n",
53
+ " )\n",
54
+ " def forward(self, x):\n",
55
+ " return self.conv(x)\n",
56
+ "\n",
57
+ "class Down(nn.Module):\n",
58
+ " def __init__(self, in_channels, out_channels):\n",
59
+ " super().__init__()\n",
60
+ " self.down = nn.Sequential(\n",
61
+ " nn.MaxPool2d(2),\n",
62
+ " DoubleConv(in_channels, out_channels)\n",
63
+ " )\n",
64
+ " def forward(self, x):\n",
65
+ " return self.down(x)\n",
66
+ "\n",
67
+ "class Up(nn.Module):\n",
68
+ " def __init__(self, in_channels, out_channels, bilinear=False):\n",
69
+ " super().__init__()\n",
70
+ " if bilinear:\n",
71
+ " self.up = nn.Sequential(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),\n",
72
+ " nn.Conv2d(in_channels, in_channels // 2, 1))\n",
73
+ " else:\n",
74
+ " self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, 2, stride=2)\n",
75
+ " \n",
76
+ " self.conv = DoubleConv(in_channels, out_channels)\n",
77
+ "\n",
78
+ " def forward(self, x1, x2):\n",
79
+ " x1 = self.up(x1)\n",
80
+ " diffY = x2.size()[2] - x1.size()[2]\n",
81
+ " diffX = x2.size()[3] - x1.size()[3]\n",
82
+ " x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2])\n",
83
+ " x = torch.cat([x2, x1], dim=1)\n",
84
+ " return self.conv(x)\n",
85
+ "\n",
86
+ "class OutConv(nn.Module):\n",
87
+ " def __init__(self, in_channels, out_channels):\n",
88
+ " super().__init__()\n",
89
+ " self.conv = nn.Conv2d(in_channels, out_channels, 1)\n",
90
+ " self.sigmoid = nn.Sigmoid()\n",
91
+ "\n",
92
+ " def forward(self, x):\n",
93
+ " return self.sigmoid(self.conv(x))\n",
94
+ "\n",
95
+ "class UNet(nn.Module):\n",
96
+ " def __init__(self, n_channels, n_classes):\n",
97
+ " super().__init__()\n",
98
+ " self.inc = DoubleConv(n_channels, 64)\n",
99
+ " self.down1 = Down(64, 128)\n",
100
+ " self.down2 = Down(128, 256)\n",
101
+ " self.down3 = Down(256, 512)\n",
102
+ " self.down4 = Down(512, 1024)\n",
103
+ " self.up1 = Up(1024, 512)\n",
104
+ " self.up2 = Up(512, 256)\n",
105
+ " self.up3 = Up(256, 128)\n",
106
+ " self.up4 = Up(128, 64)\n",
107
+ " self.outc = OutConv(64, n_classes)\n",
108
+ "\n",
109
+ " def forward(self, x):\n",
110
+ " x1 = self.inc(x)\n",
111
+ " x2 = self.down1(x1)\n",
112
+ " x3 = self.down2(x2)\n",
113
+ " x4 = self.down3(x3)\n",
114
+ " x5 = self.down4(x4)\n",
115
+ " x = self.up1(x5, x4)\n",
116
+ " x = self.up2(x4, x3)\n",
117
+ " x = self.up3(x3, x2)\n",
118
+ " x = self.up4(x2, x1)\n",
119
+ " logits = self.outc(x)\n",
120
+ " return logits"
121
+ ]
122
+ },
123
+ {
124
+ "cell_type": "markdown",
125
+ "metadata": {},
126
+ "source": [
127
+ "## Loading the model"
128
+ ]
129
+ },
130
+ {
131
+ "cell_type": "code",
132
+ "execution_count": 3,
133
+ "metadata": {},
134
+ "outputs": [
135
+ {
136
+ "ename": "RuntimeError",
137
+ "evalue": "Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.",
138
+ "output_type": "error",
139
+ "traceback": [
140
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
141
+ "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
142
+ "Cell \u001b[0;32mIn [3], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m model \u001b[39m=\u001b[39m UNet(n_channels\u001b[39m=\u001b[39m\u001b[39m3\u001b[39m, n_classes\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m model\u001b[39m.\u001b[39mload_state_dict(torch\u001b[39m.\u001b[39;49mload(\u001b[39m'\u001b[39;49m\u001b[39m../weights/water_bodies_model.pth\u001b[39;49m\u001b[39m'\u001b[39;49m))\n\u001b[1;32m 3\u001b[0m model\u001b[39m.\u001b[39mto(\u001b[39m'\u001b[39m\u001b[39mcpu\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 5\u001b[0m dummy_input \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mrandn(\u001b[39m1\u001b[39m, \u001b[39m3\u001b[39m, \u001b[39m256\u001b[39m, \u001b[39m256\u001b[39m)\n",
143
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/serialization.py:789\u001b[0m, in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, weights_only, **pickle_load_args)\u001b[0m\n\u001b[1;32m 787\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 788\u001b[0m \u001b[39mraise\u001b[39;00m pickle\u001b[39m.\u001b[39mUnpicklingError(UNSAFE_MESSAGE \u001b[39m+\u001b[39m \u001b[39mstr\u001b[39m(e)) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[0;32m--> 789\u001b[0m \u001b[39mreturn\u001b[39;00m _load(opened_zipfile, map_location, pickle_module, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mpickle_load_args)\n\u001b[1;32m 790\u001b[0m \u001b[39mif\u001b[39;00m weights_only:\n\u001b[1;32m 791\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
144
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/serialization.py:1131\u001b[0m, in \u001b[0;36m_load\u001b[0;34m(zip_file, map_location, pickle_module, pickle_file, **pickle_load_args)\u001b[0m\n\u001b[1;32m 1129\u001b[0m unpickler \u001b[39m=\u001b[39m UnpicklerWrapper(data_file, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mpickle_load_args)\n\u001b[1;32m 1130\u001b[0m unpickler\u001b[39m.\u001b[39mpersistent_load \u001b[39m=\u001b[39m persistent_load\n\u001b[0;32m-> 1131\u001b[0m result \u001b[39m=\u001b[39m unpickler\u001b[39m.\u001b[39;49mload()\n\u001b[1;32m 1133\u001b[0m torch\u001b[39m.\u001b[39m_utils\u001b[39m.\u001b[39m_validate_loaded_sparse_tensors()\n\u001b[1;32m 1135\u001b[0m \u001b[39mreturn\u001b[39;00m result\n",
145
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/serialization.py:1101\u001b[0m, in \u001b[0;36m_load.<locals>.persistent_load\u001b[0;34m(saved_id)\u001b[0m\n\u001b[1;32m 1099\u001b[0m \u001b[39mif\u001b[39;00m key \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m loaded_storages:\n\u001b[1;32m 1100\u001b[0m nbytes \u001b[39m=\u001b[39m numel \u001b[39m*\u001b[39m torch\u001b[39m.\u001b[39m_utils\u001b[39m.\u001b[39m_element_size(dtype)\n\u001b[0;32m-> 1101\u001b[0m load_tensor(dtype, nbytes, key, _maybe_decode_ascii(location))\n\u001b[1;32m 1103\u001b[0m \u001b[39mreturn\u001b[39;00m loaded_storages[key]\n",
146
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/serialization.py:1083\u001b[0m, in \u001b[0;36m_load.<locals>.load_tensor\u001b[0;34m(dtype, numel, key, location)\u001b[0m\n\u001b[1;32m 1079\u001b[0m storage \u001b[39m=\u001b[39m zip_file\u001b[39m.\u001b[39mget_storage_from_record(name, numel, torch\u001b[39m.\u001b[39mUntypedStorage)\u001b[39m.\u001b[39mstorage()\u001b[39m.\u001b[39muntyped()\n\u001b[1;32m 1080\u001b[0m \u001b[39m# TODO: Once we decide to break serialization FC, we can\u001b[39;00m\n\u001b[1;32m 1081\u001b[0m \u001b[39m# stop wrapping with TypedStorage\u001b[39;00m\n\u001b[1;32m 1082\u001b[0m loaded_storages[key] \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mstorage\u001b[39m.\u001b[39mTypedStorage(\n\u001b[0;32m-> 1083\u001b[0m wrap_storage\u001b[39m=\u001b[39mrestore_location(storage, location),\n\u001b[1;32m 1084\u001b[0m dtype\u001b[39m=\u001b[39mdtype)\n",
147
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/serialization.py:215\u001b[0m, in \u001b[0;36mdefault_restore_location\u001b[0;34m(storage, location)\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdefault_restore_location\u001b[39m(storage, location):\n\u001b[1;32m 214\u001b[0m \u001b[39mfor\u001b[39;00m _, _, fn \u001b[39min\u001b[39;00m _package_registry:\n\u001b[0;32m--> 215\u001b[0m result \u001b[39m=\u001b[39m fn(storage, location)\n\u001b[1;32m 216\u001b[0m \u001b[39mif\u001b[39;00m result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 217\u001b[0m \u001b[39mreturn\u001b[39;00m result\n",
148
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/serialization.py:182\u001b[0m, in \u001b[0;36m_cuda_deserialize\u001b[0;34m(obj, location)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_cuda_deserialize\u001b[39m(obj, location):\n\u001b[1;32m 181\u001b[0m \u001b[39mif\u001b[39;00m location\u001b[39m.\u001b[39mstartswith(\u001b[39m'\u001b[39m\u001b[39mcuda\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[0;32m--> 182\u001b[0m device \u001b[39m=\u001b[39m validate_cuda_device(location)\n\u001b[1;32m 183\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(obj, \u001b[39m\"\u001b[39m\u001b[39m_torch_load_uninitialized\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFalse\u001b[39;00m):\n\u001b[1;32m 184\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39mdevice(device):\n",
149
+ "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/serialization.py:166\u001b[0m, in \u001b[0;36mvalidate_cuda_device\u001b[0;34m(location)\u001b[0m\n\u001b[1;32m 163\u001b[0m device \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39m_utils\u001b[39m.\u001b[39m_get_device_index(location, \u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m 165\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m torch\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39mis_available():\n\u001b[0;32m--> 166\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mAttempting to deserialize object on a CUDA \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 167\u001b[0m \u001b[39m'\u001b[39m\u001b[39mdevice but torch.cuda.is_available() is False. \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 168\u001b[0m \u001b[39m'\u001b[39m\u001b[39mIf you are running on a CPU-only machine, \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 169\u001b[0m \u001b[39m'\u001b[39m\u001b[39mplease use torch.load with map_location=torch.device(\u001b[39m\u001b[39m\\'\u001b[39;00m\u001b[39mcpu\u001b[39m\u001b[39m\\'\u001b[39;00m\u001b[39m) \u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 170\u001b[0m \u001b[39m'\u001b[39m\u001b[39mto map your storages to the CPU.\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 171\u001b[0m device_count \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39mdevice_count()\n\u001b[1;32m 172\u001b[0m \u001b[39mif\u001b[39;00m device \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m device_count:\n",
150
+ "\u001b[0;31mRuntimeError\u001b[0m: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU."
151
+ ]
152
+ }
153
+ ],
154
+ "source": [
155
+ "model = UNet(n_channels=3, n_classes=1)\n",
156
+ "model.load_state_dict(torch.load('../weights/water_bodies_model.pth'))\n",
157
+ "\n",
158
+ "dummy_input = torch.randn(1, 3, 256, 256)\n",
159
+ "\n",
160
+ "model.eval()\n",
161
+ "torch_out = model(dummy_input)"
162
+ ]
163
+ },
164
+ {
165
+ "cell_type": "markdown",
166
+ "metadata": {},
167
+ "source": [
168
+ "## Converting to ONNX"
169
+ ]
170
+ },
171
+ {
172
+ "cell_type": "code",
173
+ "execution_count": null,
174
+ "metadata": {},
175
+ "outputs": [
176
+ {
177
+ "name": "stdout",
178
+ "output_type": "stream",
179
+ "text": [
180
+ "Exported graph: graph(%input : Float(*, 3, 256, 256, strides=[196608, 65536, 256, 1], requires_grad=0, device=cpu),\n",
181
+ " %up4.up.weight : Float(128, 64, 2, 2, strides=[256, 4, 2, 1], requires_grad=1, device=cpu),\n",
182
+ " %up4.up.bias : Float(64, strides=[1], requires_grad=1, device=cpu),\n",
183
+ " %outc.conv.weight : Float(1, 64, 1, 1, strides=[64, 1, 1, 1], requires_grad=1, device=cpu),\n",
184
+ " %outc.conv.bias : Float(1, strides=[1], requires_grad=1, device=cpu),\n",
185
+ " %onnx::Conv_226 : Float(64, 3, 3, 3, strides=[27, 9, 3, 1], requires_grad=0, device=cpu),\n",
186
+ " %onnx::Conv_227 : Float(64, strides=[1], requires_grad=0, device=cpu),\n",
187
+ " %onnx::Conv_229 : Float(64, 64, 3, 3, strides=[576, 9, 3, 1], requires_grad=0, device=cpu),\n",
188
+ " %onnx::Conv_230 : Float(64, strides=[1], requires_grad=0, device=cpu),\n",
189
+ " %onnx::Conv_232 : Float(128, 64, 3, 3, strides=[576, 9, 3, 1], requires_grad=0, device=cpu),\n",
190
+ " %onnx::Conv_233 : Float(128, strides=[1], requires_grad=0, device=cpu),\n",
191
+ " %onnx::Conv_235 : Float(128, 128, 3, 3, strides=[1152, 9, 3, 1], requires_grad=0, device=cpu),\n",
192
+ " %onnx::Conv_236 : Float(128, strides=[1], requires_grad=0, device=cpu),\n",
193
+ " %onnx::Conv_238 : Float(64, 128, 3, 3, strides=[1152, 9, 3, 1], requires_grad=0, device=cpu),\n",
194
+ " %onnx::Conv_239 : Float(64, strides=[1], requires_grad=0, device=cpu),\n",
195
+ " %onnx::Conv_241 : Float(64, 64, 3, 3, strides=[576, 9, 3, 1], requires_grad=0, device=cpu),\n",
196
+ " %onnx::Conv_242 : Float(64, strides=[1], requires_grad=0, device=cpu)):\n",
197
+ " %/inc/conv/conv.0/Conv_output_0 : Float(*, 64, 256, 256, strides=[4194304, 65536, 256, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/inc/conv/conv.0/Conv\"](%input, %onnx::Conv_226, %onnx::Conv_227), scope: __main__.UNet::/__main__.DoubleConv::inc/torch.nn.modules.container.Sequential::conv/torch.nn.modules.conv.Conv2d::conv.0 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
198
+ " %/inc/conv/conv.2/Relu_output_0 : Float(*, 64, 256, 256, strides=[4194304, 65536, 256, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/inc/conv/conv.2/Relu\"](%/inc/conv/conv.0/Conv_output_0), scope: __main__.UNet::/__main__.DoubleConv::inc/torch.nn.modules.container.Sequential::conv/torch.nn.modules.activation.ReLU::conv.2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
199
+ " %/inc/conv/conv.3/Conv_output_0 : Float(*, 64, 256, 256, strides=[4194304, 65536, 256, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/inc/conv/conv.3/Conv\"](%/inc/conv/conv.2/Relu_output_0, %onnx::Conv_229, %onnx::Conv_230), scope: __main__.UNet::/__main__.DoubleConv::inc/torch.nn.modules.container.Sequential::conv/torch.nn.modules.conv.Conv2d::conv.3 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
200
+ " %/inc/conv/conv.5/Relu_output_0 : Float(*, 64, 256, 256, strides=[4194304, 65536, 256, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/inc/conv/conv.5/Relu\"](%/inc/conv/conv.3/Conv_output_0), scope: __main__.UNet::/__main__.DoubleConv::inc/torch.nn.modules.container.Sequential::conv/torch.nn.modules.activation.ReLU::conv.5 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
201
+ " %/down1/down/down.0/MaxPool_output_0 : Float(*, 64, 128, 128, strides=[1048576, 16384, 128, 1], requires_grad=1, device=cpu) = onnx::MaxPool[ceil_mode=0, kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2], onnx_name=\"/down1/down/down.0/MaxPool\"](%/inc/conv/conv.5/Relu_output_0), scope: __main__.UNet::/__main__.Down::down1/torch.nn.modules.container.Sequential::down/torch.nn.modules.pooling.MaxPool2d::down.0 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:780:0\n",
202
+ " %/down1/down/down.1/conv/conv.0/Conv_output_0 : Float(*, 128, 128, 128, strides=[2097152, 16384, 128, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/down1/down/down.1/conv/conv.0/Conv\"](%/down1/down/down.0/MaxPool_output_0, %onnx::Conv_232, %onnx::Conv_233), scope: __main__.UNet::/__main__.Down::down1/torch.nn.modules.container.Sequential::down/__main__.DoubleConv::down.1/torch.nn.modules.container.Sequential::conv/torch.nn.modules.conv.Conv2d::conv.0 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
203
+ " %/down1/down/down.1/conv/conv.2/Relu_output_0 : Float(*, 128, 128, 128, strides=[2097152, 16384, 128, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/down1/down/down.1/conv/conv.2/Relu\"](%/down1/down/down.1/conv/conv.0/Conv_output_0), scope: __main__.UNet::/__main__.Down::down1/torch.nn.modules.container.Sequential::down/__main__.DoubleConv::down.1/torch.nn.modules.container.Sequential::conv/torch.nn.modules.activation.ReLU::conv.2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
204
+ " %/down1/down/down.1/conv/conv.3/Conv_output_0 : Float(*, 128, 128, 128, strides=[2097152, 16384, 128, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/down1/down/down.1/conv/conv.3/Conv\"](%/down1/down/down.1/conv/conv.2/Relu_output_0, %onnx::Conv_235, %onnx::Conv_236), scope: __main__.UNet::/__main__.Down::down1/torch.nn.modules.container.Sequential::down/__main__.DoubleConv::down.1/torch.nn.modules.container.Sequential::conv/torch.nn.modules.conv.Conv2d::conv.3 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
205
+ " %/down1/down/down.1/conv/conv.5/Relu_output_0 : Float(*, 128, 128, 128, strides=[2097152, 16384, 128, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/down1/down/down.1/conv/conv.5/Relu\"](%/down1/down/down.1/conv/conv.3/Conv_output_0), scope: __main__.UNet::/__main__.Down::down1/torch.nn.modules.container.Sequential::down/__main__.DoubleConv::down.1/torch.nn.modules.container.Sequential::conv/torch.nn.modules.activation.ReLU::conv.5 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
206
+ " %/up4/up/ConvTranspose_output_0 : Float(*, 64, 256, 256, strides=[4194304, 65536, 256, 1], requires_grad=0, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2], onnx_name=\"/up4/up/ConvTranspose\"](%/down1/down/down.1/conv/conv.5/Relu_output_0, %up4.up.weight, %up4.up.bias), scope: __main__.UNet::/__main__.Up::up4/torch.nn.modules.conv.ConvTranspose2d::up # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:953:0\n",
207
+ " %/up4/Shape_output_0 : Long(4, strides=[1], device=cpu) = onnx::Shape[onnx_name=\"/up4/Shape\"](%/inc/conv/conv.5/Relu_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:37:0\n",
208
+ " %/up4/Constant_output_0 : Long(device=cpu) = onnx::Constant[value={2}, onnx_name=\"/up4/Constant\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:37:0\n",
209
+ " %/up4/Gather_output_0 : Long(device=cpu) = onnx::Gather[axis=0, onnx_name=\"/up4/Gather\"](%/up4/Shape_output_0, %/up4/Constant_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:37:0\n",
210
+ " %/up4/Shape_1_output_0 : Long(4, strides=[1], device=cpu) = onnx::Shape[onnx_name=\"/up4/Shape_1\"](%/up4/up/ConvTranspose_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:38:0\n",
211
+ " %/up4/Constant_1_output_0 : Long(device=cpu) = onnx::Constant[value={2}, onnx_name=\"/up4/Constant_1\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:38:0\n",
212
+ " %/up4/Gather_1_output_0 : Long(device=cpu) = onnx::Gather[axis=0, onnx_name=\"/up4/Gather_1\"](%/up4/Shape_1_output_0, %/up4/Constant_1_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:38:0\n",
213
+ " %/up4/Sub_output_0 : Long(requires_grad=0, device=cpu) = onnx::Sub[onnx_name=\"/up4/Sub\"](%/up4/Gather_output_0, %/up4/Gather_1_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:38:0\n",
214
+ " %/up4/Shape_2_output_0 : Long(4, strides=[1], device=cpu) = onnx::Shape[onnx_name=\"/up4/Shape_2\"](%/inc/conv/conv.5/Relu_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:38:0\n",
215
+ " %/up4/Constant_2_output_0 : Long(device=cpu) = onnx::Constant[value={3}, onnx_name=\"/up4/Constant_2\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:38:0\n",
216
+ " %/up4/Gather_2_output_0 : Long(device=cpu) = onnx::Gather[axis=0, onnx_name=\"/up4/Gather_2\"](%/up4/Shape_2_output_0, %/up4/Constant_2_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:38:0\n",
217
+ " %/up4/Shape_3_output_0 : Long(4, strides=[1], device=cpu) = onnx::Shape[onnx_name=\"/up4/Shape_3\"](%/up4/up/ConvTranspose_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:38:0\n",
218
+ " %/up4/Constant_3_output_0 : Long(device=cpu) = onnx::Constant[value={3}, onnx_name=\"/up4/Constant_3\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:38:0\n",
219
+ " %/up4/Gather_3_output_0 : Long(device=cpu) = onnx::Gather[axis=0, onnx_name=\"/up4/Gather_3\"](%/up4/Shape_3_output_0, %/up4/Constant_3_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:38:0\n",
220
+ " %/up4/Sub_1_output_0 : Long(requires_grad=0, device=cpu) = onnx::Sub[onnx_name=\"/up4/Sub_1\"](%/up4/Gather_2_output_0, %/up4/Gather_3_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:38:0\n",
221
+ " %/up4/Constant_4_output_0 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}, onnx_name=\"/up4/Constant_4\"](), scope: __main__.UNet::/__main__.Up::up4 # /home/gautham/.local/lib/python3.10/site-packages/torch/_tensor.py:867:0\n",
222
+ " %/up4/Div_output_0 : Long(device=cpu) = onnx::Div[onnx_name=\"/up4/Div\"](%/up4/Sub_1_output_0, %/up4/Constant_4_output_0), scope: __main__.UNet::/__main__.Up::up4 # /home/gautham/.local/lib/python3.10/site-packages/torch/_tensor.py:867:0\n",
223
+ " %/up4/Cast_output_0 : Long(device=cpu) = onnx::Cast[to=7, onnx_name=\"/up4/Cast\"](%/up4/Div_output_0), scope: __main__.UNet::/__main__.Up::up4 # /home/gautham/.local/lib/python3.10/site-packages/torch/_tensor.py:867:0\n",
224
+ " %/up4/Cast_1_output_0 : Long(requires_grad=0, device=cpu) = onnx::Cast[to=7, onnx_name=\"/up4/Cast_1\"](%/up4/Cast_output_0), scope: __main__.UNet::/__main__.Up::up4 # /home/gautham/.local/lib/python3.10/site-packages/torch/_tensor.py:867:0\n",
225
+ " %/up4/Sub_2_output_0 : Long(requires_grad=0, device=cpu) = onnx::Sub[onnx_name=\"/up4/Sub_2\"](%/up4/Sub_1_output_0, %/up4/Cast_1_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
226
+ " %/up4/Constant_5_output_0 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={2}, onnx_name=\"/up4/Constant_5\"](), scope: __main__.UNet::/__main__.Up::up4 # /home/gautham/.local/lib/python3.10/site-packages/torch/_tensor.py:867:0\n",
227
+ " %/up4/Div_1_output_0 : Long(device=cpu) = onnx::Div[onnx_name=\"/up4/Div_1\"](%/up4/Sub_output_0, %/up4/Constant_5_output_0), scope: __main__.UNet::/__main__.Up::up4 # /home/gautham/.local/lib/python3.10/site-packages/torch/_tensor.py:867:0\n",
228
+ " %/up4/Cast_2_output_0 : Long(device=cpu) = onnx::Cast[to=7, onnx_name=\"/up4/Cast_2\"](%/up4/Div_1_output_0), scope: __main__.UNet::/__main__.Up::up4 # /home/gautham/.local/lib/python3.10/site-packages/torch/_tensor.py:867:0\n",
229
+ " %/up4/Cast_3_output_0 : Long(requires_grad=0, device=cpu) = onnx::Cast[to=7, onnx_name=\"/up4/Cast_3\"](%/up4/Cast_2_output_0), scope: __main__.UNet::/__main__.Up::up4 # /home/gautham/.local/lib/python3.10/site-packages/torch/_tensor.py:867:0\n",
230
+ " %/up4/Sub_3_output_0 : Long(requires_grad=0, device=cpu) = onnx::Sub[onnx_name=\"/up4/Sub_3\"](%/up4/Sub_output_0, %/up4/Cast_3_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
231
+ " %onnx::Unsqueeze_175 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()\n",
232
+ " %/up4/Unsqueeze_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name=\"/up4/Unsqueeze\"](%/up4/Cast_1_output_0, %onnx::Unsqueeze_175), scope: __main__.UNet::/__main__.Up::up4\n",
233
+ " %onnx::Unsqueeze_177 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()\n",
234
+ " %/up4/Unsqueeze_1_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name=\"/up4/Unsqueeze_1\"](%/up4/Sub_2_output_0, %onnx::Unsqueeze_177), scope: __main__.UNet::/__main__.Up::up4\n",
235
+ " %onnx::Unsqueeze_179 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()\n",
236
+ " %/up4/Unsqueeze_2_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name=\"/up4/Unsqueeze_2\"](%/up4/Cast_3_output_0, %onnx::Unsqueeze_179), scope: __main__.UNet::/__main__.Up::up4\n",
237
+ " %onnx::Unsqueeze_181 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()\n",
238
+ " %/up4/Unsqueeze_3_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name=\"/up4/Unsqueeze_3\"](%/up4/Sub_3_output_0, %onnx::Unsqueeze_181), scope: __main__.UNet::/__main__.Up::up4\n",
239
+ " %/up4/Concat_output_0 : Long(4, strides=[1], device=cpu) = onnx::Concat[axis=0, onnx_name=\"/up4/Concat\"](%/up4/Unsqueeze_output_0, %/up4/Unsqueeze_1_output_0, %/up4/Unsqueeze_2_output_0, %/up4/Unsqueeze_3_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
240
+ " %onnx::Unsqueeze_184 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()\n",
241
+ " %/up4/Unsqueeze_4_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name=\"/up4/Unsqueeze_4\"](%/up4/Cast_1_output_0, %onnx::Unsqueeze_184), scope: __main__.UNet::/__main__.Up::up4\n",
242
+ " %onnx::Unsqueeze_186 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()\n",
243
+ " %/up4/Unsqueeze_5_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name=\"/up4/Unsqueeze_5\"](%/up4/Sub_2_output_0, %onnx::Unsqueeze_186), scope: __main__.UNet::/__main__.Up::up4\n",
244
+ " %onnx::Unsqueeze_188 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()\n",
245
+ " %/up4/Unsqueeze_6_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name=\"/up4/Unsqueeze_6\"](%/up4/Cast_3_output_0, %onnx::Unsqueeze_188), scope: __main__.UNet::/__main__.Up::up4\n",
246
+ " %onnx::Unsqueeze_190 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()\n",
247
+ " %/up4/Unsqueeze_7_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name=\"/up4/Unsqueeze_7\"](%/up4/Sub_3_output_0, %onnx::Unsqueeze_190), scope: __main__.UNet::/__main__.Up::up4\n",
248
+ " %/up4/Concat_1_output_0 : Long(4, strides=[1], device=cpu) = onnx::Concat[axis=0, onnx_name=\"/up4/Concat_1\"](%/up4/Unsqueeze_4_output_0, %/up4/Unsqueeze_5_output_0, %/up4/Unsqueeze_6_output_0, %/up4/Unsqueeze_7_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
249
+ " %onnx::Pad_193 : NoneType = prim::Constant(), scope: __main__.UNet::/__main__.Up::up4\n",
250
+ " %/up4/Constant_6_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}, onnx_name=\"/up4/Constant_6\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
251
+ " %/up4/Shape_4_output_0 : Long(1, strides=[1], device=cpu) = onnx::Shape[onnx_name=\"/up4/Shape_4\"](%/up4/Concat_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
252
+ " %/up4/Gather_4_output_0 : Long(1, strides=[1], device=cpu) = onnx::Gather[axis=0, onnx_name=\"/up4/Gather_4\"](%/up4/Shape_4_output_0, %/up4/Constant_6_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
253
+ " %/up4/Constant_7_output_0 : Long(requires_grad=0, device=cpu) = onnx::Constant[value={8}, onnx_name=\"/up4/Constant_7\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
254
+ " %/up4/Sub_4_output_0 : Long(1, strides=[1], device=cpu) = onnx::Sub[onnx_name=\"/up4/Sub_4\"](%/up4/Constant_7_output_0, %/up4/Gather_4_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
255
+ " %/up4/Cast_4_output_0 : Long(4, strides=[1], device=cpu) = onnx::Cast[to=7, onnx_name=\"/up4/Cast_4\"](%/up4/Concat_1_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
256
+ " %/up4/ConstantOfShape_output_0 : Long(4, strides=[1], device=cpu) = onnx::ConstantOfShape[value={0}, onnx_name=\"/up4/ConstantOfShape\"](%/up4/Sub_4_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
257
+ " %/up4/Concat_2_output_0 : Long(8, strides=[1], device=cpu) = onnx::Concat[axis=0, onnx_name=\"/up4/Concat_2\"](%/up4/Cast_4_output_0, %/up4/ConstantOfShape_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
258
+ " %/up4/Constant_8_output_0 : Long(2, strides=[1], device=cpu) = onnx::Constant[value=-1 2 [ CPULongType{2} ], onnx_name=\"/up4/Constant_8\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
259
+ " %/up4/Reshape_output_0 : Long(4, 2, strides=[2, 1], device=cpu) = onnx::Reshape[allowzero=0, onnx_name=\"/up4/Reshape\"](%/up4/Concat_2_output_0, %/up4/Constant_8_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
260
+ " %/up4/Constant_9_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}, onnx_name=\"/up4/Constant_9\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
261
+ " %/up4/Constant_10_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={-1}, onnx_name=\"/up4/Constant_10\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
262
+ " %/up4/Constant_11_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={-9223372036854775807}, onnx_name=\"/up4/Constant_11\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
263
+ " %/up4/Constant_12_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={-1}, onnx_name=\"/up4/Constant_12\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
264
+ " %/up4/Slice_output_0 : Long(4, 2, strides=[2, 1], device=cpu) = onnx::Slice[onnx_name=\"/up4/Slice\"](%/up4/Reshape_output_0, %/up4/Constant_10_output_0, %/up4/Constant_11_output_0, %/up4/Constant_9_output_0, %/up4/Constant_12_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
265
+ " %/up4/Transpose_output_0 : Long(2, 4, strides=[4, 1], device=cpu) = onnx::Transpose[perm=[1, 0], onnx_name=\"/up4/Transpose\"](%/up4/Slice_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
266
+ " %/up4/Constant_13_output_0 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={-1}, onnx_name=\"/up4/Constant_13\"](), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
267
+ " %/up4/Reshape_1_output_0 : Long(8, strides=[1], device=cpu) = onnx::Reshape[allowzero=0, onnx_name=\"/up4/Reshape_1\"](%/up4/Transpose_output_0, %/up4/Constant_13_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
268
+ " %/up4/Cast_5_output_0 : Long(8, strides=[1], device=cpu) = onnx::Cast[to=7, onnx_name=\"/up4/Cast_5\"](%/up4/Reshape_1_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
269
+ " %/up4/Pad_output_0 : Float(*, *, *, *, strides=[4194304, 65536, 256, 1], requires_grad=1, device=cpu) = onnx::Pad[mode=\"constant\", onnx_name=\"/up4/Pad\"](%/up4/up/ConvTranspose_output_0, %/up4/Cast_5_output_0, %onnx::Pad_193), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:39:0\n",
270
+ " %/up4/Concat_3_output_0 : Float(*, *, 256, 256, strides=[8388608, 65536, 256, 1], requires_grad=1, device=cpu) = onnx::Concat[axis=1, onnx_name=\"/up4/Concat_3\"](%/inc/conv/conv.5/Relu_output_0, %/up4/Pad_output_0), scope: __main__.UNet::/__main__.Up::up4 # /tmp/ipykernel_19565/1651252862.py:40:0\n",
271
+ " %/up4/conv/conv/conv.0/Conv_output_0 : Float(*, 64, 256, 256, strides=[4194304, 65536, 256, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/up4/conv/conv/conv.0/Conv\"](%/up4/Concat_3_output_0, %onnx::Conv_238, %onnx::Conv_239), scope: __main__.UNet::/__main__.Up::up4/__main__.DoubleConv::conv/torch.nn.modules.container.Sequential::conv/torch.nn.modules.conv.Conv2d::conv.0 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
272
+ " %/up4/conv/conv/conv.2/Relu_output_0 : Float(*, 64, 256, 256, strides=[4194304, 65536, 256, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/up4/conv/conv/conv.2/Relu\"](%/up4/conv/conv/conv.0/Conv_output_0), scope: __main__.UNet::/__main__.Up::up4/__main__.DoubleConv::conv/torch.nn.modules.container.Sequential::conv/torch.nn.modules.activation.ReLU::conv.2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
273
+ " %/up4/conv/conv/conv.3/Conv_output_0 : Float(*, 64, 256, 256, strides=[4194304, 65536, 256, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/up4/conv/conv/conv.3/Conv\"](%/up4/conv/conv/conv.2/Relu_output_0, %onnx::Conv_241, %onnx::Conv_242), scope: __main__.UNet::/__main__.Up::up4/__main__.DoubleConv::conv/torch.nn.modules.container.Sequential::conv/torch.nn.modules.conv.Conv2d::conv.3 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
274
+ " %/up4/conv/conv/conv.5/Relu_output_0 : Float(*, 64, 256, 256, strides=[4194304, 65536, 256, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/up4/conv/conv/conv.5/Relu\"](%/up4/conv/conv/conv.3/Conv_output_0), scope: __main__.UNet::/__main__.Up::up4/__main__.DoubleConv::conv/torch.nn.modules.container.Sequential::conv/torch.nn.modules.activation.ReLU::conv.5 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
275
+ " %/outc/conv/Conv_output_0 : Float(*, 1, 256, 256, strides=[65536, 65536, 256, 1], requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1], onnx_name=\"/outc/conv/Conv\"](%/up4/conv/conv/conv.5/Relu_output_0, %outc.conv.weight, %outc.conv.bias), scope: __main__.UNet::/__main__.OutConv::outc/torch.nn.modules.conv.Conv2d::conv # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
276
+ " %output : Float(*, 1, 256, 256, strides=[65536, 65536, 256, 1], requires_grad=1, device=cpu) = onnx::Sigmoid[onnx_name=\"/outc/sigmoid/Sigmoid\"](%/outc/conv/Conv_output_0), scope: __main__.UNet::/__main__.OutConv::outc/torch.nn.modules.activation.Sigmoid::sigmoid # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/activation.py:294:0\n",
277
+ " return (%output)\n",
278
+ "\n"
279
+ ]
280
+ },
281
+ {
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+ "name": "stderr",
283
+ "output_type": "stream",
284
+ "text": [
285
+ "/home/gautham/.local/lib/python3.10/site-packages/torch/onnx/utils.py:687: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)\n",
286
+ " _C._jit_pass_onnx_graph_shape_type_inference(\n",
287
+ "/home/gautham/.local/lib/python3.10/site-packages/torch/onnx/utils.py:1178: UserWarning: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)\n",
288
+ " _C._jit_pass_onnx_graph_shape_type_inference(\n"
289
+ ]
290
+ }
291
+ ],
292
+ "source": [
293
+ "onnx_path = '../weights/model.onnx'\n",
294
+ "\n",
295
+ "torch.onnx.export(model,\n",
296
+ " dummy_input,\n",
297
+ " onnx_path,\n",
298
+ " verbose=True,\n",
299
+ " input_names = ['input'], # the model's input names\n",
300
+ " output_names = ['output'], # the model's output names\n",
301
+ " dynamic_axes={'input' : {0 : 'batch_size'}, # variable length axes\n",
302
+ " 'output' : {0 : 'batch_size'}})"
303
+ ]
304
+ },
305
+ {
306
+ "cell_type": "markdown",
307
+ "metadata": {},
308
+ "source": [
309
+ "## Verifying the ONNX model"
310
+ ]
311
+ },
312
+ {
313
+ "cell_type": "code",
314
+ "execution_count": null,
315
+ "metadata": {},
316
+ "outputs": [],
317
+ "source": [
318
+ "import onnx\n",
319
+ "\n",
320
+ "onnx_model = onnx.load(onnx_path)\n",
321
+ "onnx.checker.check_model(onnx_model)"
322
+ ]
323
+ },
324
+ {
325
+ "cell_type": "markdown",
326
+ "metadata": {},
327
+ "source": [
328
+ "## Comparing ONNX Runtime and PyTorch results"
329
+ ]
330
+ },
331
+ {
332
+ "cell_type": "code",
333
+ "execution_count": null,
334
+ "metadata": {},
335
+ "outputs": [
336
+ {
337
+ "name": "stdout",
338
+ "output_type": "stream",
339
+ "text": [
340
+ "Exported model has been tested with ONNXRuntime, and the result looks good!\n"
341
+ ]
342
+ }
343
+ ],
344
+ "source": [
345
+ "import onnxruntime\n",
346
+ "import numpy as np\n",
347
+ "\n",
348
+ "ort_session = onnxruntime.InferenceSession(onnx_path)\n",
349
+ "\n",
350
+ "def to_numpy(tensor):\n",
351
+ " return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()\n",
352
+ "\n",
353
+ "ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(dummy_input)}\n",
354
+ "ort_outs = ort_session.run(None, ort_inputs)\n",
355
+ "\n",
356
+ "np.testing.assert_allclose(to_numpy(torch_out), ort_outs[0], rtol=1e-03, atol=1e-05)\n",
357
+ "\n",
358
+ "print(\"Exported model has been tested with ONNXRuntime, and the result looks good!\")\n"
359
+ ]
360
+ }
361
+ ],
362
+ "metadata": {
363
+ "kernelspec": {
364
+ "display_name": "Python 3.10.6 64-bit",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.10.6"
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+ },
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+ "orig_nbformat": 4,
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+ "vscode": {
382
+ "interpreter": {
383
+ "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
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+ }
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 2
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+ }