text stringlengths 0 1.16k |
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tensor([1.0000e+00, 3.2110e-08, 1.7430e-07, 7.3055e-08, 7.8566e-11, 7.3337e-11, |
3.2833e-10, 8.5046e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.2110e-08, 1.7430e-07, 7.3055e-08, 7.8566e-11, 7.3337e-11, |
3.2833e-10, 8.5046e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.7430e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(6.4121e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the panda nibbling something?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['Is the animal looking toward the camera?'], responses:['yes'] |
tensor([1.0000e+00, 2.6892e-09, 9.8332e-09, 7.1805e-10, 2.6919e-12, 1.7284e-11, |
1.2601e-11, 1.7712e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.6892e-09, 9.8332e-09, 7.1805e-10, 2.6919e-12, 1.7284e-11, |
1.2601e-11, 1.7712e-09], device='cuda:3', grad_fn=<SelectBackward0>) |
question: ['How many vending machines are in the image?'], responses:['four'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(9.8332e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-9.8332e-09, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Does the left image contain a paper towel stand?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 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']] |
[('7 eleven', 0.12650899275575006), ('4', 0.125210025275264), ('first', 0.12483048280083887), ('3', 0.12473532336671392), ('5', 0.1247268629491862), ('dark', 0.12470563072493092), ('forward', 0.12466964370422237), ('bag', 0.12461303842309367)] |
[['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag']] |
question: ['Does the left image contain a paper towel stand?'], responses:['no'] |
[('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([1, 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: 3399 |
question: ['Is the panda nibbling something?'], responses:['no'] |
tensor([1.0000e+00, 5.2935e-10, 4.5210e-07, 8.9155e-11, 6.4972e-11, 1.5354e-08, |
1.1325e-09, 9.5677e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 5.2935e-10, 4.5210e-07, 8.9155e-11, 6.4972e-11, 1.5354e-08, |
1.1325e-09, 9.5677e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(5.2935e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
[('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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
tensor([1.0000e+00, 4.2810e-08, 4.5991e-10, 8.3971e-08, 6.9769e-10, 6.5503e-09, |
2.4358e-10, 1.7315e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.2810e-08, 4.5991e-10, 8.3971e-08, 6.9769e-10, 6.5503e-09, |
2.4358e-10, 1.7315e-08], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([1.4975e-13, 9.9435e-01, 4.7607e-06, 5.6063e-03, 2.4678e-05, 3.3087e-06, |
7.9768e-06, 1.1170e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([1.4975e-13, 9.9435e-01, 4.7607e-06, 5.6063e-03, 2.4678e-05, 3.3087e-06, |
7.9768e-06, 1.1170e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.6153e-05, device='cuda:0', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(4.5991e-10, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1875e-07, device='cuda:1', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many cheetahs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['How many animals 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']] |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 3.1924e-09, 2.2472e-07, 1.7832e-11, 2.3042e-11, 1.5811e-08, |
2.3255e-10, 4.0513e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.1924e-09, 2.2472e-07, 1.7832e-11, 2.3042e-11, 1.5811e-08, |
2.3255e-10, 4.0513e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.1924e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
question: ['How many cheetahs 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: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
tensor([1.0000e+00, 9.9476e-10, 1.4247e-10, 1.8010e-10, 1.4467e-10, 2.2505e-08, |
6.1056e-09, 8.5835e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 9.9476e-10, 1.4247e-10, 1.8010e-10, 1.4467e-10, 2.2505e-08, |
6.1056e-09, 8.5835e-10], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(9.9476e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
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: 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 |
tensor([9.9969e-01, 3.0942e-04, 8.6571e-08, 7.3918e-08, 8.7714e-11, 8.5569e-08, |
3.5349e-10, 3.5464e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
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