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torch.Size([13, 3, 448, 448]) |
question: ['Is there a bed with mostly solid white pillows?'], 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([3, 3, 448, 448]) knan debug pixel values shape |
question: ['Is there a visible orange vegetable in the image?'], 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']] |
question: ['Is the mouth of the dog open?'], responses:['yes'] |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350 |
[('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: 1353 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
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 |
tensor([1.0000e+00, 5.4717e-10, 7.6581e-09, 3.3909e-09, 5.0730e-11, 2.1176e-11, |
1.7683e-11, 2.6500e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.4717e-10, 7.6581e-09, 3.3909e-09, 5.0730e-11, 2.1176e-11, |
1.7683e-11, 2.6500e-09], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(7.6581e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-7.6581e-09, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many boars are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
question: ['Does the image show a mirror over the sink?'], responses:['no'] |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350 |
[('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']] |
question: ['How many boars 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([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350 |
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 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351 |
tensor([1.0000e+00, 1.1811e-10, 6.7684e-12, 4.6614e-11, 2.8054e-11, 9.7955e-09, |
1.5713e-08, 1.3333e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.1811e-10, 6.7684e-12, 4.6614e-11, 2.8054e-11, 9.7955e-09, |
1.5713e-08, 1.3333e-10], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.5713e-08, 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>)} |
tensor([1.0000e+00, 1.2425e-08, 1.7088e-09, 4.1617e-08, 2.6030e-10, 3.5539e-10, |
4.6454e-10, 2.1136e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.2425e-08, 1.7088e-09, 4.1617e-08, 2.6030e-10, 3.5539e-10, |
4.6454e-10, 2.1136e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.7088e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1750e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Can you see the lamp in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
tensor([1.0000e+00, 1.1553e-08, 1.1227e-10, 1.3790e-07, 3.3828e-10, 5.9641e-09, |
8.1944e-10, 2.4661e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.1553e-08, 1.1227e-10, 1.3790e-07, 3.3828e-10, 5.9641e-09, |
8.1944e-10, 2.4661e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.1227e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3831e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
question: ['Can you see the lamp in the image?'], responses:['no'] |
question: ['How many dogs are in the image?'], responses:['5'] |
[('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']] |
[('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([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
tensor([5.2736e-01, 7.8453e-09, 4.7262e-01, 1.6708e-05, 1.1318e-06, 3.8328e-06, |
4.3852e-08, 1.7810e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([5.2736e-01, 7.8453e-09, 4.7262e-01, 1.6708e-05, 1.1318e-06, 3.8328e-06, |
4.3852e-08, 1.7810e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
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: 1861 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
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, 3.6381e-10, 6.1466e-07, 4.8854e-11, 3.3379e-10, 2.4963e-08, |
4.8308e-10, 3.4304e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.6381e-10, 6.1466e-07, 4.8854e-11, 3.3379e-10, 2.4963e-08, |
4.8308e-10, 3.4304e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 4.3635e-09, 5.8346e-07, 4.6982e-11, 3.5238e-10, 1.1192e-08, |
1.2732e-09, 3.3597e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.3635e-09, 5.8346e-07, 4.6982e-11, 3.5238e-10, 1.1192e-08, |
1.2732e-09, 3.3597e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.6381e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
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