text stringlengths 0 1.16k |
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9.2421e-11, 1.1268e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.4200e-08, 2.2414e-10, 3.9039e-08, 3.6057e-11, 4.7450e-10, |
9.2421e-11, 1.1268e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.2414e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1899e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
question: ['How many dogs are in the image?'], responses:['2'] |
[('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']] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
question: ['How many golf balls are in the image?'], responses:['1'] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
[('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']] |
tensor([5.7413e-01, 3.6481e-01, 1.0501e-02, 3.8810e-04, 5.9135e-04, 7.2630e-05, |
9.1704e-05, 4.9412e-02], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
12 ************* |
['12', '11', '10', '8', '6', '26', '47', '13'] tensor([5.7413e-01, 3.6481e-01, 1.0501e-02, 3.8810e-04, 5.9135e-04, 7.2630e-05, |
9.1704e-05, 4.9412e-02], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 8.4108e-09, 9.6134e-10, 9.6808e-09, 9.0561e-11, 5.9512e-10, |
3.3115e-10, 1.5276e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 8.4108e-09, 9.6134e-10, 9.6808e-09, 9.0561e-11, 5.9512e-10, |
3.3115e-10, 1.5276e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.0222e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many lions are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
question: ['How many bottles of beer are in the image?'], responses:['5'] |
torch.Size([3, 3, 448, 448]) |
[('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([11, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
question: ['How many lions are in the image?'], responses:['2'] |
[('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([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
tensor([4.5266e-01, 6.5205e-08, 5.7355e-10, 5.4734e-01, 1.1461e-10, 1.9256e-10, |
2.1558e-10, 3.0697e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([4.5266e-01, 6.5205e-08, 5.7355e-10, 5.4734e-01, 1.1461e-10, 1.9256e-10, |
2.1558e-10, 3.0697e-11], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5473, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.4527, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 2.9006e-10, 6.9435e-11, 8.7738e-11, 8.7746e-11, 1.4468e-08, |
7.6581e-09, 4.3727e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.9006e-10, 6.9435e-11, 8.7738e-11, 8.7746e-11, 1.4468e-08, |
7.6581e-09, 4.3727e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.3098e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
tensor([9.8461e-01, 1.8793e-05, 1.2946e-05, 9.8277e-03, 2.5488e-10, 5.4294e-03, |
5.7485e-05, 3.9510e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.8461e-01, 1.8793e-05, 1.2946e-05, 9.8277e-03, 2.5488e-10, 5.4294e-03, |
5.7485e-05, 3.9510e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9846, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0154, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the netting in the image white?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([5, 3, 448, 448]) |
question: ['How many dogs are in the image?'], responses:['2'] |
[('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 |
question: ['Is the netting in the image white?'], 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']] |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 4.5468e-08, 3.7323e-09, 4.2714e-08, 1.6019e-10, 5.7012e-10, |
3.9031e-10, 5.3218e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 4.5468e-08, 3.7323e-09, 4.2714e-08, 1.6019e-10, 5.7012e-10, |
3.9031e-10, 5.3218e-11], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 2.7805e-09, 2.7306e-09, 5.8295e-09, 5.9048e-10, 8.1178e-11, |
1.8756e-11, 1.2273e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.7805e-09, 2.7306e-09, 5.8295e-09, 5.9048e-10, 8.1178e-11, |
1.8756e-11, 1.2273e-08], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(9.3088e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(2.7306e-09, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-2.7306e-09, device='cuda:1', grad_fn=<SubBackward0>)} |
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