text stringlengths 0 1.16k |
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3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9999e-01, 5.6495e-06, 2.0092e-08, 3.5795e-09, 4.3008e-11, 6.4213e-08, |
5.5102e-11, 3.4255e-09], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(7.1031e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['How many perfume bottles are in the image?'], responses:['200'] |
ANSWER0=VQA(image=LEFT,question='How many wine glasses are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
question: ['How many glasses are the desserts being served in?'], responses:['3'] |
torch.Size([7, 3, 448, 448]) |
[('200', 0.1321606270186138), ('400', 0.12559688033658498), ('150', 0.12485068989614088), ('500', 0.12470360679460982), ('300', 0.12446878919489603), ('600', 0.12292388257579578), ('700', 0.12273847440460704), ('1000', 0.12255704977875165)] |
[['200', '400', '150', '500', '300', '600', '700', '1000']] |
[('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: 3399 |
question: ['How many wine glasses are in the image?'], responses:['1'] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
[('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([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
tensor([9.9554e-01, 4.8012e-06, 3.7843e-04, 6.6916e-08, 1.1794e-07, 3.9819e-03, |
9.3355e-05, 1.6352e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
right ************* |
['right', 'right 1', 'straight', 'floating', 'flip', 'backwards', 'serious', 'working'] tensor([9.9554e-01, 4.8012e-06, 3.7843e-04, 6.6916e-08, 1.1794e-07, 3.9819e-03, |
9.3355e-05, 1.6352e-08], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., 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) |
torch.Size([1, 3, 448, 448]) |
question: ['How many dogs are in the image?'], responses:['δΈ'] |
[('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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
tensor([1.4308e-04, 1.2055e-03, 3.4752e-02, 5.4730e-01, 7.9926e-02, 7.6545e-02, |
1.8913e-03, 2.5824e-01], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
bulldog ************* |
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([1.4308e-04, 1.2055e-03, 3.4752e-02, 5.4730e-01, 7.9926e-02, 7.6545e-02, |
1.8913e-03, 2.5824e-01], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
tensor([1.0000e+00, 3.6667e-10, 4.3451e-11, 1.4465e-10, 5.6661e-11, 5.0385e-08, |
3.8891e-08, 4.3210e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.6667e-10, 4.3451e-11, 1.4465e-10, 5.6661e-11, 5.0385e-08, |
3.8891e-08, 4.3210e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.8891e-08, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
tensor([0.6161, 0.0868, 0.0712, 0.0759, 0.0917, 0.0030, 0.0035, 0.0519], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
200 ************* |
['200', '400', '150', '500', '300', '600', '700', '1000'] tensor([0.6161, 0.0868, 0.0712, 0.0759, 0.0917, 0.0030, 0.0035, 0.0519], |
device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([9.9996e-01, 4.0398e-05, 1.8645e-07, 1.0555e-08, 1.8231e-10, 2.4939e-06, |
1.5204e-09, 1.9308e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9996e-01, 4.0398e-05, 1.8645e-07, 1.0555e-08, 1.8231e-10, 2.4939e-06, |
1.5204e-09, 1.9308e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Are there shelves in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(4.3110e-05, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many stingrays are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['Are there shelves in the image?'], responses:['yes'] |
question: ['How many stingrays are in the image?'], responses:['1'] |
[('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']] |
[('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([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: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
tensor([1.0000e+00, 3.0780e-09, 2.9989e-09, 1.3131e-08, 1.6397e-10, 1.1032e-09, |
2.9556e-11, 4.2575e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.0780e-09, 2.9989e-09, 1.3131e-08, 1.6397e-10, 1.1032e-09, |
2.9556e-11, 4.2575e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.9989e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.9989e-09, device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 1.0045e-09, 5.0405e-11, 3.5050e-11, 4.2777e-11, 2.4470e-09, |
1.4594e-06, 2.3327e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.0045e-09, 5.0405e-11, 3.5050e-11, 4.2777e-11, 2.4470e-09, |
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