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torch.Size([13, 3, 448, 448]) |
question: ['Is a person holding up the crab?'], responses:['no'] |
question: ['How many drawers are on the cabinet?'], responses:['2'] |
[('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']] |
[('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([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many chimneys are in the image?'], responses:['2'] |
question: ['How many binders are in the image?'], responses:['5'] |
[('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']] |
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)] |
[['5', '8', '4', '6', '3', '7', '11', '9']] |
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: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([6.5083e-01, 3.4837e-01, 7.1270e-05, 1.0921e-04, 2.0775e-04, 1.1428e-04, |
2.1671e-04, 8.2972e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.5083e-01, 3.4837e-01, 7.1270e-05, 1.0921e-04, 2.0775e-04, 1.1428e-04, |
2.1671e-04, 8.2972e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3484, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6508, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many pillows are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 6') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([2.8711e-01, 2.7428e-01, 2.3802e-01, 4.6860e-02, 9.9234e-02, 2.8411e-02, |
2.5886e-02, 2.0057e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([2.8711e-01, 2.7428e-01, 2.3802e-01, 4.6860e-02, 9.9234e-02, 2.8411e-02, |
2.5886e-02, 2.0057e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2380, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7620, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many white dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
question: ['How many white dogs are in the image?'], responses:['1'] |
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)] |
[['1', '3', '4', '8', '6', '12', '2', '47']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many pillows are in the image?'], responses:['5'] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)] |
[['5', '8', '4', '6', '3', '7', '11', '9']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([0.2037, 0.0995, 0.1607, 0.1448, 0.1315, 0.1238, 0.0487, 0.0872], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2037, 0.0995, 0.1607, 0.1448, 0.1315, 0.1238, 0.0487, 0.0872], |
device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1315, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8685, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many glass bottles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 0') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([5.7965e-01, 1.4553e-01, 4.1710e-02, 2.1183e-01, 1.4410e-02, 3.1173e-03, |
3.6561e-03, 9.9096e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([5.7965e-01, 1.4553e-01, 4.1710e-02, 2.1183e-01, 1.4410e-02, 3.1173e-03, |
3.6561e-03, 9.9096e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7882, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2118, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='Does the dispenser on the right have a black base?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([5, 3, 448, 448]) |
question: ['Does the dispenser on the right have a black base?'], responses:['yes'] |
tensor([8.8629e-01, 1.5259e-02, 5.4371e-03, 1.4174e-03, 2.3398e-03, 1.3131e-03, |
8.7811e-02, 1.3286e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.8629e-01, 1.5259e-02, 5.4371e-03, 1.4174e-03, 2.3398e-03, 1.3131e-03, |
8.7811e-02, 1.3286e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
[('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']] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1137, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8863, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['How many glass bottles are in the image?'], responses:['many'] |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352 |
[('many', 0.12680051474066337), ('few', 0.12559712123098582), ('several', 0.12545126119006317), ('blinds', 0.12452572291517987), ('moss', 0.12441899466837554), ('rainbow', 0.1244056457460399), ('kite', 0.12440323404357946), ('directions', 0.12439750546511286)] |
[['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions']] |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1355 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353 |
tensor([6.7589e-01, 1.7802e-02, 3.0321e-01, 9.6080e-04, 1.5270e-04, 6.1946e-04, |
5.2785e-05, 1.3136e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.7589e-01, 1.7802e-02, 3.0321e-01, 9.6080e-04, 1.5270e-04, 6.1946e-04, |
5.2785e-05, 1.3136e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6759, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.3032, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0209, device='cuda:0', grad_fn=<SubBackward0>)} |
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