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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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question: ['Is there a pattern of bold diagonal lines near a red-orange train car?'], responses:['no']
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question: ['How many animals are in the image?'], responses:['ไธ']
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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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[('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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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: 1868
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868
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question: ['Is there a person sitting on the ground in front of a store?'], responses:['yes']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869
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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868
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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: 1868
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tensor([1.0000e+00, 3.1609e-10, 6.9658e-07, 8.7092e-11, 4.3266e-10, 1.8635e-07,
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9.9182e-09, 1.0510e-06], device='cuda:2', 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, 3.1609e-10, 6.9658e-07, 8.7092e-11, 4.3266e-10, 1.8635e-07,
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9.9182e-09, 1.0510e-06], device='cuda:2', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(3.1609e-10, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.9073e-06, device='cuda:2', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many antelopes 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([13, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869
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tensor([1.0000e+00, 7.8232e-10, 2.9329e-07, 2.4610e-12, 6.8102e-11, 1.6639e-08,
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9.4141e-10, 5.7740e-07], device='cuda:0', 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, 7.8232e-10, 2.9329e-07, 2.4610e-12, 6.8102e-11, 1.6639e-08,
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9.4141e-10, 5.7740e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([8.4100e-06, 3.7024e-03, 2.6648e-02, 8.7816e-01, 4.4228e-02, 2.7732e-02,
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8.7725e-03, 1.0750e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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bulldog *************
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['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([8.4100e-06, 3.7024e-03, 2.6648e-02, 8.7816e-01, 4.4228e-02, 2.7732e-02,
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8.7725e-03, 1.0750e-02], device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(7.8232e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is the image clearly a bed?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many wild pigs 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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torch.Size([3, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many wild pigs are in the image?'], responses:['11']
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[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
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[['11', '10', '12', '9', '8', '13', '7', '14']]
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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question: ['How many antelopes 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([13, 3, 448, 448]) knan debug pixel values shape
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tensor([9.9146e-01, 6.2138e-04, 1.4906e-03, 4.5013e-05, 1.1221e-08, 5.8955e-03,
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9.8465e-08, 4.8393e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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11 *************
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['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.9146e-01, 6.2138e-04, 1.4906e-03, 4.5013e-05, 1.1221e-08, 5.8955e-03,
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9.8465e-08, 4.8393e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['Is the image clearly a bed?'], 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([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: 3395
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tensor([1.0000e+00, 3.2358e-08, 7.9012e-09, 7.1281e-09, 6.5083e-11, 2.8780e-10,
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8.6922e-11, 2.8626e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.2358e-08, 7.9012e-09, 7.1281e-09, 6.5083e-11, 2.8780e-10,
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8.6922e-11, 2.8626e-09], device='cuda:1', grad_fn=<SelectBackward0>)
|
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(7.9012e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-7.9012e-09, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Are neon pink jellyfish shown in the image?')
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ANSWER1=RESULT(var=ANSWER0)
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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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: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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question: ['Are neon pink jellyfish shown in the image?'], 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: 3396
|
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: 3396
|
tensor([9.9409e-01, 5.9110e-03, 3.2690e-06, 6.2395e-07, 2.8285e-08, 6.6517e-10,
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1.1977e-08, 3.2697e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9409e-01, 5.9110e-03, 3.2690e-06, 6.2395e-07, 2.8285e-08, 6.6517e-10,
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1.1977e-08, 3.2697e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(6.2395e-07, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
|
tensor([1.0000e+00, 2.6205e-10, 3.2563e-07, 1.8835e-11, 2.9324e-11, 5.6520e-09,
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3.5479e-10, 3.8869e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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no *************
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