text stringlengths 0 1.16k |
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7 ************* |
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([0.7389, 0.0013, 0.0064, 0.1727, 0.0060, 0.0012, 0.0727, 0.0008], |
device='cuda:3', grad_fn=<SelectBackward0>) |
question: ['Is there a dog standing near a fence in the image?'], responses:['yes'] |
question: ['How many hanging lights are in the image?'], responses:['11'] |
question: ['How many white dogs are in the image?'], responses:['2'] |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, 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 puppies are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
[('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']] |
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)] |
[['11', '10', '12', '9', '8', '13', '7', '14']] |
[('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 |
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: 3400 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403 |
question: ['How many puppies are in the image?'], responses:['ไธ'] |
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)] |
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
tensor([2.7950e-04, 4.6073e-03, 7.8434e-02, 6.1226e-01, 2.2734e-01, 2.6431e-02, |
2.4927e-03, 4.8164e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
bulldog ************* |
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([2.7950e-04, 4.6073e-03, 7.8434e-02, 6.1226e-01, 2.2734e-01, 2.6431e-02, |
2.4927e-03, 4.8164e-02], device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
tensor([8.7823e-01, 1.0287e-03, 3.4066e-02, 4.6010e-03, 6.1717e-05, 6.3644e-02, |
8.6100e-03, 9.7576e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
11 ************* |
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([8.7823e-01, 1.0287e-03, 3.4066e-02, 4.6010e-03, 6.1717e-05, 6.3644e-02, |
8.6100e-03, 9.7576e-03], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 2.6426e-06, 2.3405e-08, 1.0408e-07, 1.6052e-09, 1.2697e-09, |
2.3447e-09, 2.9857e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.6426e-06, 2.3405e-08, 1.0408e-07, 1.6052e-09, 1.2697e-09, |
2.3447e-09, 2.9857e-09], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 5.5432e-09, 1.5285e-10, 2.7054e-08, 4.5708e-11, 4.8956e-10, |
7.5545e-11, 8.7389e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.5432e-09, 1.5285e-10, 2.7054e-08, 4.5708e-11, 4.8956e-10, |
7.5545e-11, 8.7389e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0408e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.5285e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.5285e-10, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many mittens are shown in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many perfume bottles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='Is the dog sitting on a wood surface?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Is the dog sitting on a wood surface?'], responses:['no'] |
question: ['How many mittens are shown in the image?'], responses:['ไธ'] |
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)] |
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']] |
[('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: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
question: ['How many perfume bottles are in the image?'], responses:['7'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)] |
[['7', '8', '11', '5', '9', '10', '6', '12']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
tensor([1.0000e+00, 4.4576e-10, 2.5657e-07, 1.4411e-12, 4.0778e-12, 1.2775e-08, |
2.5367e-10, 7.7221e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.4576e-10, 2.5657e-07, 1.4411e-12, 4.0778e-12, 1.2775e-08, |
2.5367e-10, 7.7221e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([1.6090e-05, 3.7368e-03, 1.1976e-02, 4.0303e-01, 2.8786e-01, 2.6271e-01, |
2.3143e-02, 7.5275e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
bulldog ************* |
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] ๆๅ็ๆฆ็ๅๅธไธบ: tensor([1.6090e-05, 3.7368e-03, 1.1976e-02, 4.0303e-01, 2.8786e-01, 2.6271e-01, |
2.3143e-02, 7.5275e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
{True: tensor(4.4576e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.0729e-06, device='cuda:2', grad_fn=<DivBackward0>)} |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)} |
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