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question: ['Is the dog sitting on a wood surface?'], 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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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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question: ['Is the dog in the image standing in the grass?'], responses:['yes']
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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([3, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 5.7236e-10, 5.3929e-07, 3.1976e-11, 3.8495e-11, 9.3803e-08,
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5.0865e-09, 1.5863e-06], 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, 5.7236e-10, 5.3929e-07, 3.1976e-11, 3.8495e-11, 9.3803e-08,
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5.0865e-09, 1.5863e-06], device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(5.7236e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.2650e-06, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the bird lying down?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([1, 3, 448, 448])
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question: ['How many animals are standing on the rock?'], responses:['1']
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question: ['How many balconies are on the building?'], responses:['31']
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question: ['Is the bird lying down?'], responses:['no']
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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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[('31', 0.12650531532885212), ('32', 0.12557960530881754), ('33', 0.12551236813893774), ('34', 0.12499924019115541), ('28', 0.12465699261580566), ('39', 0.12436485741776965), ('40', 0.12427729910815238), ('26', 0.12410432189050948)]
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[['31', '32', '33', '34', '28', '39', '40', '26']]
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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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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: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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tensor([1.0000e+00, 7.7017e-10, 1.1847e-06, 8.5740e-11, 6.0820e-11, 2.3135e-08,
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1.1476e-09, 2.7202e-06], 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, 7.7017e-10, 1.1847e-06, 8.5740e-11, 6.0820e-11, 2.3135e-08,
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1.1476e-09, 2.7202e-06], device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(7.7017e-10, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(3.9339e-06, device='cuda:3', grad_fn=<SubBackward0>)}
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tensor([1.0000e+00, 1.3814e-09, 1.3177e-10, 3.4679e-09, 4.0817e-11, 4.9237e-11,
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7.8421e-12, 3.1102e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.3814e-09, 1.3177e-10, 3.4679e-09, 4.0817e-11, 4.9237e-11,
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7.8421e-12, 3.1102e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.3177e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.3177e-10, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many water bottles 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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torch.Size([13, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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tensor([1.0000e+00, 3.2740e-10, 1.0754e-10, 1.0463e-10, 8.5069e-11, 8.4063e-09,
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3.5614e-09, 8.1063e-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, 3.2740e-10, 1.0754e-10, 1.0463e-10, 8.5069e-11, 8.4063e-09,
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3.5614e-09, 8.1063e-11], device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([0.6751, 0.0222, 0.1866, 0.0476, 0.0029, 0.0254, 0.0024, 0.0379],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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31 *************
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['31', '32', '33', '34', '28', '39', '40', '26'] tensor([0.6751, 0.0222, 0.1866, 0.0476, 0.0029, 0.0254, 0.0024, 0.0379],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(3.5614e-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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ANSWER0=VQA(image=RIGHT,question='How many stingrays 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 water bottles are in the image?'], responses:['ไธ']
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torch.Size([7, 3, 448, 448])
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many seals 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([1, 3, 448, 448])
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[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
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[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
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question: ['How many seals 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([1, 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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tensor([1.0000e+00, 1.4472e-10, 3.7166e-11, 2.0729e-10, 9.1978e-11, 2.0129e-08,
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2.1517e-09, 4.2601e-10], 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, 1.4472e-10, 3.7166e-11, 2.0729e-10, 9.1978e-11, 2.0129e-08,
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2.1517e-09, 4.2601e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(2.1517e-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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question: ['How many stingrays 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: 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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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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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, 2.1478e-08, 3.7244e-10, 1.0030e-06, 8.5404e-11, 3.7725e-11,
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