Existing resnet50 model increased to four existing output nodes via onnx_graphsurgeon
graph torch-jit-export (
%inputs[FLOAT, 1x3x224x224]
) initializers (
%497[FLOAT, 64x3x7x7]
%498[FLOAT, 64]
%500[FLOAT, 64x64x1x1]
%501[FLOAT, 64]
%503[FLOAT, 64x64x3x3]
%504[FLOAT, 64]
%506[FLOAT, 256x64x1x1]
%507[FLOAT, 256]
%509[FLOAT, 256x64x1x1]
%510[FLOAT, 256]
%512[FLOAT, 64x256x1x1]
%513[FLOAT, 64]
%515[FLOAT, 64x64x3x3]
%516[FLOAT, 64]
%518[FLOAT, 256x64x1x1]
%519[FLOAT, 256]
%521[FLOAT, 64x256x1x1]
%522[FLOAT, 64]
%524[FLOAT, 64x64x3x3]
%525[FLOAT, 64]
%527[FLOAT, 256x64x1x1]
%528[FLOAT, 256]
%530[FLOAT, 128x256x1x1]
%531[FLOAT, 128]
%533[FLOAT, 128x128x3x3]
%534[FLOAT, 128]
%536[FLOAT, 512x128x1x1]
%537[FLOAT, 512]
%539[FLOAT, 512x256x1x1]
%540[FLOAT, 512]
%542[FLOAT, 128x512x1x1]
%543[FLOAT, 128]
%545[FLOAT, 128x128x3x3]
%546[FLOAT, 128]
%548[FLOAT, 512x128x1x1]
%549[FLOAT, 512]
%551[FLOAT, 128x512x1x1]
%552[FLOAT, 128]
%554[FLOAT, 128x128x3x3]
%555[FLOAT, 128]
%557[FLOAT, 512x128x1x1]
%558[FLOAT, 512]
%560[FLOAT, 128x512x1x1]
%561[FLOAT, 128]
%563[FLOAT, 128x128x3x3]
%564[FLOAT, 128]
%566[FLOAT, 512x128x1x1]
%567[FLOAT, 512]
%569[FLOAT, 256x512x1x1]
%570[FLOAT, 256]
%572[FLOAT, 256x256x3x3]
%573[FLOAT, 256]
%575[FLOAT, 1024x256x1x1]
%576[FLOAT, 1024]
%578[FLOAT, 1024x512x1x1]
%579[FLOAT, 1024]
%581[FLOAT, 256x1024x1x1]
%582[FLOAT, 256]
%584[FLOAT, 256x256x3x3]
%585[FLOAT, 256]
%587[FLOAT, 1024x256x1x1]
%588[FLOAT, 1024]
%590[FLOAT, 256x1024x1x1]
%591[FLOAT, 256]
%593[FLOAT, 256x256x3x3]
%594[FLOAT, 256]
%596[FLOAT, 1024x256x1x1]
%597[FLOAT, 1024]
%599[FLOAT, 256x1024x1x1]
%600[FLOAT, 256]
%602[FLOAT, 256x256x3x3]
%603[FLOAT, 256]
%605[FLOAT, 1024x256x1x1]
%606[FLOAT, 1024]
%608[FLOAT, 256x1024x1x1]
%609[FLOAT, 256]
%611[FLOAT, 256x256x3x3]
%612[FLOAT, 256]
%614[FLOAT, 1024x256x1x1]
%615[FLOAT, 1024]
%617[FLOAT, 256x1024x1x1]
%618[FLOAT, 256]
%620[FLOAT, 256x256x3x3]
%621[FLOAT, 256]
%623[FLOAT, 1024x256x1x1]
%624[FLOAT, 1024]
%626[FLOAT, 512x1024x1x1]
%627[FLOAT, 512]
%629[FLOAT, 512x512x3x3]
%630[FLOAT, 512]
%632[FLOAT, 2048x512x1x1]
%633[FLOAT, 2048]
%635[FLOAT, 2048x1024x1x1]
%636[FLOAT, 2048]
%638[FLOAT, 512x2048x1x1]
%639[FLOAT, 512]
%641[FLOAT, 512x512x3x3]
%642[FLOAT, 512]
%644[FLOAT, 2048x512x1x1]
%645[FLOAT, 2048]
%647[FLOAT, 512x2048x1x1]
%648[FLOAT, 512]
%650[FLOAT, 512x512x3x3]
%651[FLOAT, 512]
%653[FLOAT, 2048x512x1x1]
%654[FLOAT, 2048]
%fc.weight[FLOAT, 3x2048]
%fc.bias[FLOAT, 3]
) {
%496 = Conv[dilations = [1, 1], group = 1, kernel_shape = [7, 7], pads = [3, 3, 3, 3], strides = [2, 2]](%inputs, %497, %498)
%323 = Relu(%496)
%324 = MaxPool[kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%323)
%499 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%324, %500, %501)
%327 = Relu(%499)
%502 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%327, %503, %504)
%330 = Relu(%502)
%505 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%330, %506, %507)
%508 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%324, %509, %510)
%335 = Add(%505, %508)
%336 = Relu(%335)
%511 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%336, %512, %513)
%339 = Relu(%511)
%514 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%339, %515, %516)
%342 = Relu(%514)
%517 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%342, %518, %519)
%345 = Add(%517, %336)
%346 = Relu(%345)
%520 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%346, %521, %522)
%349 = Relu(%520)
%523 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%349, %524, %525)
%352 = Relu(%523)
%526 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%352, %527, %528)
%355 = Add(%526, %346)
%356 = Relu(%355)
%529 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%356, %530, %531)
%359 = Relu(%529)
%532 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%359, %533, %534)
%362 = Relu(%532)
%535 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%362, %536, %537)
%538 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%356, %539, %540)
%367 = Add(%535, %538)
%368 = Relu(%367)
%541 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%368, %542, %543)
%371 = Relu(%541)
%544 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%371, %545, %546)
%374 = Relu(%544)
%547 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%374, %548, %549)
%377 = Add(%547, %368)
%378 = Relu(%377)
%550 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%378, %551, %552)
%381 = Relu(%550)
%553 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%381, %554, %555)
%384 = Relu(%553)
%556 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%384, %557, %558)
%387 = Add(%556, %378)
%388 = Relu(%387)
%559 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%388, %560, %561)
%391 = Relu(%559)
%562 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%391, %563, %564)
%394 = Relu(%562)
%565 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%394, %566, %567)
%397 = Add(%565, %388)
%398 = Relu(%397)
%568 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%398, %569, %570)
%401 = Relu(%568)
%571 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%401, %572, %573)
%404 = Relu(%571)
%574 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%404, %575, %576)
%577 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%398, %578, %579)
%409 = Add(%574, %577)
%410 = Relu(%409)
%580 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%410, %581, %582)
%413 = Relu(%580)
%583 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%413, %584, %585)
%416 = Relu(%583)
%586 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%416, %587, %588)
%419 = Add(%586, %410)
%420 = Relu(%419)
%589 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%420, %590, %591)
%423 = Relu(%589)
%592 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%423, %593, %594)
%426 = Relu(%592)
%595 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%426, %596, %597)
%429 = Add(%595, %420)
%430 = Relu(%429)
%598 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%430, %599, %600)
%433 = Relu(%598)
%601 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%433, %602, %603)
%436 = Relu(%601)
%604 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%436, %605, %606)
%439 = Add(%604, %430)
%440 = Relu(%439)
%607 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%440, %608, %609)
%443 = Relu(%607)
%610 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%443, %611, %612)
%446 = Relu(%610)
%613 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%446, %614, %615)
%449 = Add(%613, %440)
%450 = Relu(%449)
%616 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%450, %617, %618)
%453 = Relu(%616)
%619 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%453, %620, %621)
%456 = Relu(%619)
%622 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%456, %623, %624)
%459 = Add(%622, %450)
%460 = Relu(%459)
%625 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%460, %626, %627)
%463 = Relu(%625)
%628 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [2, 2]](%463, %629, %630)
%466 = Relu(%628)
%631 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%466, %632, %633)
%634 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [2, 2]](%460, %635, %636)
%471 = Add(%631, %634)
%472 = Relu(%471)
%637 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%472, %638, %639)
%475 = Relu(%637)
%640 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%475, %641, %642)
%478 = Relu(%640)
%643 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%478, %644, %645)
%481 = Add(%643, %472)
%482 = Relu(%481)
%646 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%482, %647, %648)
%485 = Relu(%646)
%649 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], pads = [1, 1, 1, 1], strides = [1, 1]](%485, %650, %651)
%488 = Relu(%649)
%652 = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%488, %653, %654)
%491 = Add(%652, %482)
%492 = Relu(%491)
%493 = GlobalAveragePool(%492)
%494 = Flatten[axis = 1](%493)
%outputs = Gemm[alpha = 1, beta = 1, transB = 1](%494, %fc.weight, %fc.bias)
return %outputs, %356, %398, %460
}
I perform build_engine on tx2 with this model, is tx2 converted to fp32 + fp16?