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question: ['How many people are in the image?'], responses:['0'] |
question: ['Is there at least one person standing in front of and staring ahead at a row of vending machines?'], responses:['no'] |
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)] |
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']] |
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)] |
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1873 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1873 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874 |
question: ['Are there people in a shop in the image?'], responses:['yes'] |
question: ['Does the sink have a double basin?'], responses:['yes'] |
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)] |
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']] |
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)] |
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1873 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1873 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874 |
tensor([9.8792e-01, 1.8201e-03, 1.5642e-03, 3.7907e-04, 1.8117e-03, 3.1656e-04, |
1.4374e-03, 4.7510e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.8792e-01, 1.8201e-03, 1.5642e-03, 3.7907e-04, 1.8117e-03, 3.1656e-04, |
1.4374e-03, 4.7510e-03], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([7.6428e-01, 2.3442e-01, 6.4069e-05, 1.3744e-04, 6.4146e-04, 6.3322e-05, |
2.6377e-04, 1.3252e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.6428e-01, 2.3442e-01, 6.4069e-05, 1.3744e-04, 6.4146e-04, 6.3322e-05, |
2.6377e-04, 1.3252e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0.9879, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0121, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2344, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.7643, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many dogs are in the image?'], responses:['2'] |
question: ['How many dogs are in the image?'], responses:['3'] |
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)] |
[['2', '3', '4', '1', '5', '8', '7', '29']] |
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)] |
[['3', '4', '1', '5', '8', '2', '6', '12']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([7.7298e-01, 1.6567e-02, 2.0804e-01, 1.3079e-03, 8.2454e-05, 3.0950e-04, |
7.9619e-05, 6.2719e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.7298e-01, 1.6567e-02, 2.0804e-01, 1.3079e-03, 8.2454e-05, 3.0950e-04, |
7.9619e-05, 6.2719e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
tensor([8.2071e-01, 1.4926e-02, 1.6125e-01, 1.6153e-03, 9.8795e-05, 3.9250e-04, |
6.2436e-05, 9.4274e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.2071e-01, 1.4926e-02, 1.6125e-01, 1.6153e-03, 9.8795e-05, 3.9250e-04, |
6.2436e-05, 9.4274e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7730, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.2080, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0190, device='cuda:3', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8207, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1613, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0180, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many mountain goats are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many birds are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
question: ['How many mountain goats are in the image?'], responses:['3'] |
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)] |
[['3', '4', '1', '5', '8', '2', '6', '12']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
question: ['How many birds are in the image?'], responses:['2'] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)] |
[['2', '3', '4', '1', '5', '8', '7', '29']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([9.5740e-01, 9.9912e-03, 2.2295e-03, 2.8913e-02, 7.2351e-04, 3.9766e-04, |
3.1033e-04, 3.4222e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.5740e-01, 9.9912e-03, 2.2295e-03, 2.8913e-02, 7.2351e-04, 3.9766e-04, |
3.1033e-04, 3.4222e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0100, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9900, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([8.8235e-01, 6.0042e-02, 1.7747e-02, 6.9507e-03, 7.0949e-04, 2.9260e-02, |
2.0545e-03, 8.8184e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([8.8235e-01, 6.0042e-02, 1.7747e-02, 6.9507e-03, 7.0949e-04, 2.9260e-02, |
2.0545e-03, 8.8184e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8824, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1176, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
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