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torch.Size([7, 3, 448, 448])
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question: ['Are the two pins touching each other?'], responses:['no']
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question: ['Is there a plant in one of the vases?'], responses:['yes']
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[('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)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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tensor([8.0702e-01, 2.1212e-02, 1.6963e-01, 1.1133e-03, 6.6355e-05, 2.5260e-04,
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7.4205e-05, 6.2863e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.0702e-01, 2.1212e-02, 1.6963e-01, 1.1133e-03, 6.6355e-05, 2.5260e-04,
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7.4205e-05, 6.2863e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([5.1509e-01, 4.8389e-01, 1.1952e-04, 2.2497e-04, 4.7546e-05, 1.8805e-04,
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3.5437e-04, 8.1218e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.1509e-01, 4.8389e-01, 1.1952e-04, 2.2497e-04, 4.7546e-05, 1.8805e-04,
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3.5437e-04, 8.1218e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8070, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.1696, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0233, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many jellyfish are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} > 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5151, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4839, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many cheetahs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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question: ['How many dogs are standing on grass in the image?'], responses:['1']
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question: ['Do the golf balls in the left image look noticeably darker and grayer than those in the right image?'], responses:['yes']
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torch.Size([7, 3, 448, 448])
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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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question: ['How many jellyfish are in the image?'], responses:['1']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1877
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tensor([0.6178, 0.0949, 0.0396, 0.0219, 0.0255, 0.0106, 0.1888, 0.0009],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.6178, 0.0949, 0.0396, 0.0219, 0.0255, 0.0106, 0.1888, 0.0009],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3822, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6178, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874
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question: ['How many cheetahs are in the image?'], responses:['4']
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[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
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[['4', '5', '3', '8', '6', '1', '2', '11']]
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1875
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874
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question: ['How many dogs are in the image?'], responses:['2']
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1875
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1875
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tensor([9.6711e-01, 5.9330e-03, 2.6325e-03, 1.2411e-03, 1.7529e-03, 1.1522e-03,
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2.0071e-02, 1.0878e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6711e-01, 5.9330e-03, 2.6325e-03, 1.2411e-03, 1.7529e-03, 1.1522e-03,
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2.0071e-02, 1.0878e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9671, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0329, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='What color is the dog in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == "white"')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([5.4506e-01, 2.6233e-02, 4.2449e-01, 1.2944e-03, 1.6373e-04, 1.1832e-03,
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9.5590e-05, 1.4725e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.4506e-01, 2.6233e-02, 4.2449e-01, 1.2944e-03, 1.6373e-04, 1.1832e-03,
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9.5590e-05, 1.4725e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5451, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.4245, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0304, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many windows are on the rusted out bus?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 12')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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tensor([0.4735, 0.2336, 0.1974, 0.0058, 0.0432, 0.0145, 0.0297, 0.0023],
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device='cuda:3', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.4735, 0.2336, 0.1974, 0.0058, 0.0432, 0.0145, 0.0297, 0.0023],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0442, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9558, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the dog running in the image?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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tensor([9.2375e-01, 2.4621e-02, 5.1608e-03, 4.3206e-02, 1.7829e-03, 7.8809e-04,
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6.3465e-04, 5.6315e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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2 *************
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