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torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Are there white flowers in a vase in the image?'], 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']] |
question: ['Is the ferret seen coming out of a hole?'], responses:['no'] |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
[('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 |
question: ['How many striped pillows are in the image?'], responses:['0'] |
question: ['How many wine glasses are in the image?'], responses:['4'] |
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)] |
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']] |
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)] |
[['4', '5', '3', '8', '6', '1', '2', '11']] |
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: 3397 |
tensor([7.5519e-01, 2.4380e-01, 2.4070e-05, 1.3346e-04, 1.8536e-04, 3.2230e-04, |
3.0879e-04, 4.2577e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.5519e-01, 2.4380e-01, 2.4070e-05, 1.3346e-04, 1.8536e-04, 3.2230e-04, |
3.0879e-04, 4.2577e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2438, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7552, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:3', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
ANSWER0=VQA(image=LEFT,question='What color are the citrus fruits growing on the tree?') |
ANSWER1=EVAL(expr='{ANSWER0} == "yellow"') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([8.5154e-01, 1.4798e-01, 1.6203e-05, 6.5280e-05, 2.4570e-05, 1.8503e-04, |
1.6069e-04, 3.4258e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.5154e-01, 1.4798e-01, 1.6203e-05, 6.5280e-05, 2.4570e-05, 1.8503e-04, |
1.6069e-04, 3.4258e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1480, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8515, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many black barrels are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
question: ['How many black barrels 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([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([0.7042, 0.0291, 0.1147, 0.0076, 0.0014, 0.1384, 0.0035, 0.0012], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.7042, 0.0291, 0.1147, 0.0076, 0.0014, 0.1384, 0.0035, 0.0012], |
device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1147, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8853, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many lotion bottles are visible in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['What color are the citrus fruits growing on the tree?'], responses:['orange'] |
[('orange', 0.12675952683011027), ('purple', 0.12551481365388975), ('vanilla', 0.12482510860547383), ('cinnamon', 0.12472774302623701), ('maroon', 0.12470081889874104), ('lemon', 0.12449279608493648), ('lime', 0.1244923535449387), ('black', 0.12448683935567285)] |
[['orange', 'purple', 'vanilla', 'cinnamon', 'maroon', 'lemon', 'lime', 'black']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
question: ['How many lotion bottles are visible 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']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([9.8395e-01, 3.0053e-03, 2.7423e-03, 4.1660e-04, 9.0513e-04, 5.0039e-04, |
3.3938e-03, 5.0890e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.8395e-01, 3.0053e-03, 2.7423e-03, 4.1660e-04, 9.0513e-04, 5.0039e-04, |
3.3938e-03, 5.0890e-03], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([0.4380, 0.3536, 0.0842, 0.0067, 0.1013, 0.0045, 0.0098, 0.0020], |
device='cuda:0', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.4380, 0.3536, 0.0842, 0.0067, 0.1013, 0.0045, 0.0098, 0.0020], |
device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0.9839, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0161, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0098, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9902, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is a man wearing plaid pajama pants in the image?') |
ANSWER1=RESULT(var=ANSWER0) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['Is a man wearing plaid pajama pants 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']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
tensor([0.4989, 0.0296, 0.1926, 0.1811, 0.0201, 0.0519, 0.0055, 0.0203], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.4989, 0.0296, 0.1926, 0.1811, 0.0201, 0.0519, 0.0055, 0.0203], |
device='cuda:1', grad_fn=<SelectBackward0>) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869 |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0201, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9799, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Does the image contain a paper towel stand?') |
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