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question: ['Is the animal looking toward the camera?'], responses:['no'] |
[('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']] |
[('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 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([9.7808e-01, 2.1794e-03, 1.5736e-02, 4.4764e-05, 2.7042e-03, 7.0351e-07, |
1.2593e-03, 5.0305e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
forward ************* |
['forward', 'backwards', 'sideways', 'back', 'straight', 'movement', 'swing', 'working'] tensor([9.7808e-01, 2.1794e-03, 1.5736e-02, 4.4764e-05, 2.7042e-03, 7.0351e-07, |
1.2593e-03, 5.0305e-07], 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>)} |
question: ['How many animals are in the image?'], responses:['2'] |
ANSWER0=VQA(image=RIGHT,question='Does the image have a row of three drawers?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
[('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']] |
question: ['Does the image have a row of three drawers?'], 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([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([1.0000e+00, 2.4344e-09, 2.8173e-09, 5.4454e-09, 7.3911e-11, 6.5228e-11, |
1.3993e-11, 4.0823e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.4344e-09, 2.8173e-09, 5.4454e-09, 7.3911e-11, 6.5228e-11, |
1.3993e-11, 4.0823e-09], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.8173e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.8173e-09, device='cuda:1', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([1.0000e+00, 8.4590e-10, 1.5526e-10, 1.9170e-10, 8.7065e-11, 1.0139e-08, |
4.2292e-09, 3.4330e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 8.4590e-10, 1.5526e-10, 1.9170e-10, 8.7065e-11, 1.0139e-08, |
4.2292e-09, 3.4330e-10], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([9.9994e-01, 5.8291e-05, 1.5856e-07, 1.7282e-11, 1.3754e-12, 2.0031e-09, |
7.4213e-10, 1.8745e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9994e-01, 5.8291e-05, 1.5856e-07, 1.7282e-11, 1.3754e-12, 2.0031e-09, |
7.4213e-10, 1.8745e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.5991e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many lipsticks are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 6') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(5.8291e-05, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9999, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.5763e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
torch.Size([5, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='Is the dog in the image lying down?') |
ANSWER1=RESULT(var=ANSWER0) |
torch.Size([13, 3, 448, 448]) |
question: ['How many lipsticks are in the image?'], responses:['six'] |
[('7 eleven', 0.1258716720461554), ('dusk', 0.12512990238684168), ('blue', 0.12502287564185594), ('rose', 0.12495109740026594), ('peach', 0.12486403486105606), ('kitten', 0.12474151468778871), ('laundry', 0.12473504457146048), ('sunrise', 0.12468385840457588)] |
[['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise']] |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351 |
question: ['Is the dog in the image lying down?'], 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']] |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351 |
tensor([4.3877e-05, 1.1763e-03, 2.0958e-03, 2.2329e-01, 3.6727e-01, 1.0343e-04, |
2.2394e-01, 1.8208e-01], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
peach ************* |
['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise'] tensor([4.3877e-05, 1.1763e-03, 2.0958e-03, 2.2329e-01, 3.6727e-01, 1.0343e-04, |
2.2394e-01, 1.8208e-01], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {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>)} |
tensor([9.9883e-01, 1.1695e-03, 2.8989e-06, 1.9924e-06, 1.2611e-08, 1.5267e-10, |
2.4448e-09, 3.1383e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9883e-01, 1.1695e-03, 2.8989e-06, 1.9924e-06, 1.2611e-08, 1.5267e-10, |
2.4448e-09, 3.1383e-11], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.9924e-06, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many ferrets are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
question: ['How many ferrets 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, 4.7823e-10, 1.3176e-10, 2.6200e-10, 1.3351e-10, 1.3164e-08, |
5.1014e-09, 3.7966e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.7823e-10, 1.3176e-10, 2.6200e-10, 1.3351e-10, 1.3164e-08, |
5.1014e-09, 3.7966e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(5.1014e-09, 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>)} |
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