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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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question: ['Is the dog looking toward the camera?'], responses:['no']
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question: ['Is the dog looking at the camera?'], 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: 3, images per sample: 3.0, dynamic token length: 839
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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: 3, images per sample: 3.0, dynamic token length: 838
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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: 3, images per sample: 3.0, dynamic token length: 838
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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tensor([1.0000e+00, 4.3727e-08, 6.4760e-07, 2.7296e-07, 2.2994e-09, 9.9312e-08,
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3.3751e-08, 2.4225e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.3727e-08, 6.4760e-07, 2.7296e-07, 2.2994e-09, 9.9312e-08,
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3.3751e-08, 2.4225e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(6.4760e-07, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.8292e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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question: ['Which direction is the dog facing?'], responses:['right']
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ANSWER0=VQA(image=RIGHT,question='How many parrots 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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[('right', 0.12743553739412528), ('right 1', 0.12490968573275477), ('straight', 0.12485251094891832), ('floating', 0.12468075392646753), ('flip', 0.12467791878738273), ('backwards', 0.12452118816110067), ('serious', 0.12447626064603681), ('working', 0.12444614440321403)]
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[['right', 'right 1', 'straight', 'floating', 'flip', 'backwards', 'serious', 'working']]
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['How many parrots 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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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: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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tensor([1.0000e+00, 4.6715e-10, 7.3289e-08, 8.1178e-11, 4.0776e-11, 2.9506e-08,
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2.4049e-09, 3.1820e-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, 4.6715e-10, 7.3289e-08, 8.1178e-11, 4.0776e-11, 2.9506e-08,
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2.4049e-09, 3.1820e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.6715e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many puppies 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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tensor([1.0000e+00, 1.5558e-09, 1.3737e-07, 6.8750e-12, 1.2214e-12, 2.9816e-09,
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2.0723e-10, 3.2133e-07], 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([1.0000e+00, 1.5558e-09, 1.3737e-07, 6.8750e-12, 1.2214e-12, 2.9816e-09,
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2.0723e-10, 3.2133e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.5558e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many parrots 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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question: ['How many puppies 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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question: ['How many parrots 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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tensor([9.9967e-01, 1.2340e-04, 2.0369e-04, 3.5579e-10, 3.6532e-09, 9.9801e-08,
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1.2323e-08, 3.8870e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9967e-01, 1.2340e-04, 2.0369e-04, 3.5579e-10, 3.6532e-09, 9.9801e-08,
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1.2323e-08, 3.8870e-08], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.2323e-08, 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>)}
|
tensor([9.8987e-01, 9.2713e-06, 8.1329e-05, 2.6894e-07, 5.4388e-08, 9.9541e-03,
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8.0570e-05, 8.5767e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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right *************
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['right', 'right 1', 'straight', 'floating', 'flip', 'backwards', 'serious', 'working'] tensor([9.8987e-01, 9.2713e-06, 8.1329e-05, 2.6894e-07, 5.4388e-08, 9.9541e-03,
|
8.0570e-05, 8.5767e-08], device='cuda:2', grad_fn=<SelectBackward0>)
|
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 wolves 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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tensor([1.0000e+00, 3.5539e-10, 1.1123e-10, 2.3952e-10, 1.2820e-10, 5.7762e-09,
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4.2958e-09, 2.4936e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.5539e-10, 1.1123e-10, 2.3952e-10, 1.2820e-10, 5.7762e-09,
|
4.2958e-09, 2.4936e-11], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.2958e-09, 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>)}
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tensor([1.0000e+00, 1.7052e-10, 2.8054e-11, 1.1359e-10, 5.4073e-11, 1.0141e-08,
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2.2286e-09, 1.1897e-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, 1.7052e-10, 2.8054e-11, 1.1359e-10, 5.4073e-11, 1.0141e-08,
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2.2286e-09, 1.1897e-10], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.2855e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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