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FINAL_ANSWER=RESULT(var=ANSWER1)
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Registering VQA_lavis step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering EVAL step
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Registering RESULT step
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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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ANSWER0=VQA(image=RIGHT,question='Is there sky in the background?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Is the food being served in a white dish?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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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([7, 3, 448, 448])
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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: ['Is there sky in the background?'], 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([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: 835
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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question: ['How many wild pigs are in the image?'], responses:['11']
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question: ['Is the food being served in a white dish?'], responses:['yes']
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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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[('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: 3, images per sample: 3.0, dynamic token length: 835
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
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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: 836
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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tensor([1.0000e+00, 2.5398e-10, 1.0135e-06, 6.1276e-11, 5.1429e-10, 7.5283e-08,
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1.7801e-09, 1.3912e-06], 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, 2.5398e-10, 1.0135e-06, 6.1276e-11, 5.1429e-10, 7.5283e-08,
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1.7801e-09, 1.3912e-06], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(2.5398e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.5034e-06, device='cuda:0', grad_fn=<DivBackward0>)}
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question: ['Are there people in a shop in the image?'], responses:['no']
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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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[('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])
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 6.2570e-09, 4.0501e-11, 2.9122e-09, 1.3177e-10, 2.2767e-10,
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3.0750e-12, 8.5675e-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, 6.2570e-09, 4.0501e-11, 2.9122e-09, 1.3177e-10, 2.2767e-10,
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3.0750e-12, 8.5675e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([9.5754e-01, 8.2040e-04, 8.2846e-03, 4.6280e-05, 2.8376e-07, 2.5523e-02,
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4.3047e-06, 7.7824e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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11 *************
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['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.5754e-01, 8.2040e-04, 8.2846e-03, 4.6280e-05, 2.8376e-07, 2.5523e-02,
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4.3047e-06, 7.7824e-03], device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(4.0501e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.0501e-11, device='cuda:1', grad_fn=<DivBackward0>)}
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1., 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 wartgogs 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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ANSWER0=VQA(image=RIGHT,question='How many bottles 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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question: ['How many dogs are in the image?'], responses:['1']
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torch.Size([13, 3, 448, 448])
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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 wartgogs 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([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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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tensor([1.0000e+00, 2.1908e-06, 1.6727e-08, 1.3867e-08, 2.0854e-09, 1.5376e-09,
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6.2952e-09, 3.1109e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.1908e-06, 1.6727e-08, 1.3867e-08, 2.0854e-09, 1.5376e-09,
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6.2952e-09, 3.1109e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.2205e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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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: 3396
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question: ['How many bottles 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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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: 3396
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tensor([1.3226e-01, 8.6774e-01, 4.0912e-08, 6.8709e-10, 4.5592e-09, 3.0834e-09,
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2.8032e-09, 3.3732e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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