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ANSWER0=VQA(image=LEFT,question='How many water buffalos 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([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many rolls of paper towel are in the package?'], responses:['6']
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[('6', 0.12794147189263105), ('8', 0.12539492259598553), ('12', 0.12539359088927945), ('5', 0.12471292164321114), ('4', 0.12443617393590153), ('1', 0.12417386497855347), ('11', 0.12398049124372558), ('3', 0.12396656282071232)]
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[['6', '8', '12', '5', '4', '1', '11', '3']]
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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tensor([9.9881e-01, 7.1120e-04, 4.0523e-04, 2.2861e-05, 3.3264e-05, 2.1938e-06,
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1.1860e-05, 3.8505e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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6 *************
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['6', '8', '12', '5', '4', '1', '11', '3'] tensor([9.9881e-01, 7.1120e-04, 4.0523e-04, 2.2861e-05, 3.3264e-05, 2.1938e-06,
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1.1860e-05, 3.8505e-06], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9988, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0012, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,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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question: ['How many birds 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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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['Does the right image feature side-by-side towels arranged decoratively on a bar?'], responses:['no']
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question: ['How many water buffalos are in the image?'], responses:['2']
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question: ['How many dogs are in the image?'], responses:['1']
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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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[('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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[('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: 1860
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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: 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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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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tensor([1.0000e+00, 8.3931e-10, 1.5617e-10, 1.1052e-10, 1.0102e-10, 6.3454e-09,
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1.3440e-08, 2.0915e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 8.3931e-10, 1.5617e-10, 1.1052e-10, 1.0102e-10, 6.3454e-09,
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1.3440e-08, 2.0915e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.3440e-08, 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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ANSWER0=VQA(image=RIGHT,question='How many guinea pigs 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([3, 3, 448, 448])
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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 guinea pigs are in the image?'], responses:['1']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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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([3, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 4.3452e-11, 3.5114e-12, 1.3888e-11, 8.2927e-12, 8.4059e-09,
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6.4488e-09, 4.2698e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.3452e-11, 3.5114e-12, 1.3888e-11, 8.2927e-12, 8.4059e-09,
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6.4488e-09, 4.2698e-11], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.4488e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 2.3490e-10, 8.9157e-11, 2.4139e-10, 1.6453e-10, 1.2576e-08,
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7.5394e-09, 1.6795e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.3490e-10, 8.9157e-11, 2.4139e-10, 1.6453e-10, 1.2576e-08,
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7.5394e-09, 1.6795e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(7.5394e-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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tensor([1.0000e+00, 7.1231e-10, 1.1530e-06, 6.3084e-12, 2.8190e-11, 6.0696e-09,
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5.2820e-10, 6.4910e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 7.1231e-10, 1.1530e-06, 6.3084e-12, 2.8190e-11, 6.0696e-09,
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5.2820e-10, 6.4910e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(7.1231e-10, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.9073e-06, device='cuda:1', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many pigs 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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tensor([9.9023e-01, 9.7082e-03, 8.7653e-07, 5.7727e-05, 1.1746e-08, 7.3192e-11,
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8.5082e-10, 5.5026e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9023e-01, 9.7082e-03, 8.7653e-07, 5.7727e-05, 1.1746e-08, 7.3192e-11,
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8.5082e-10, 5.5026e-11], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(5.7727e-05, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9999, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many bottles 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([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many pigs 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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