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[['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions']]
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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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question: ['How many chimneys are in the image?'], responses:['2']
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question: ['Is there a structure with a wooden roof to the right of the yurt?'], responses:['yes']
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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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[('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([13, 3, 448, 448]) knan debug pixel values shape
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3407
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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tensor([0.6079, 0.1005, 0.1816, 0.0107, 0.0271, 0.0413, 0.0192, 0.0117],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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many *************
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['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions'] tensor([0.6079, 0.1005, 0.1816, 0.0107, 0.0271, 0.0413, 0.0192, 0.0117],
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device='cuda:2', grad_fn=<SelectBackward0>)
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tensor([0.3133, 0.0546, 0.1679, 0.2310, 0.0700, 0.1216, 0.0105, 0.0311],
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device='cuda:3', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.3133, 0.0546, 0.1679, 0.2310, 0.0700, 0.1216, 0.0105, 0.0311],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many computers are displayed in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([3, 3, 448, 448])
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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>)}
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ANSWER0=VQA(image=LEFT,question='Is a person holding up the crab?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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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: 13, images per sample: 13.0, dynamic token length: 3405
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question: ['How many computers are displayed in the image?'], responses:['9']
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[('9', 0.12801736482258133), ('8', 0.12565135970392036), ('11', 0.1254560343890198), ('10', 0.1248838582125673), ('7', 0.12420801006143238), ('12', 0.12408347303550306), ('5', 0.12385261492086817), ('14', 0.12384728485410773)]
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[['9', '8', '11', '10', '7', '12', '5', '14']]
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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question: ['Is a person holding up the crab?'], responses:['no']
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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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
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tensor([0.2449, 0.1115, 0.1548, 0.1562, 0.0417, 0.1947, 0.0100, 0.0862],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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9 *************
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['9', '8', '11', '10', '7', '12', '5', '14'] tensor([0.2449, 0.1115, 0.1548, 0.1562, 0.0417, 0.1947, 0.0100, 0.0862],
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device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many creatures 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([13, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
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tensor([5.8602e-01, 1.4817e-01, 4.2586e-02, 2.0246e-01, 1.3686e-02, 3.1726e-03,
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3.8005e-03, 1.0593e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([5.8602e-01, 1.4817e-01, 4.2586e-02, 2.0246e-01, 1.3686e-02, 3.1726e-03,
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3.8005e-03, 1.0593e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7975, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2025, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many binders are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([6.6447e-01, 1.8594e-02, 3.1388e-01, 1.6777e-03, 1.5700e-04, 6.0538e-04,
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1.2874e-04, 4.8784e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.6447e-01, 1.8594e-02, 3.1388e-01, 1.6777e-03, 1.5700e-04, 6.0538e-04,
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1.2874e-04, 4.8784e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6645, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3139, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0217, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many drawers are on the cabinet?')
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ANSWER1=EVAL(expr='{ANSWER0} == 4')
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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([7, 3, 448, 448])
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question: ['How many creatures are in the image?'], responses:['1']
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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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tensor([6.5083e-01, 3.4836e-01, 7.2766e-05, 1.0915e-04, 2.0293e-04, 1.2032e-04,
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2.1763e-04, 8.0914e-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([6.5083e-01, 3.4836e-01, 7.2766e-05, 1.0915e-04, 2.0293e-04, 1.2032e-04,
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2.1763e-04, 8.0914e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3484, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.6508, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many pillows are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 6')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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question: ['How many drawers are on the cabinet?'], 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([13, 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: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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question: ['How many binders are in the image?'], responses:['5']
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[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
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[['5', '8', '4', '6', '3', '7', '11', '9']]
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