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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8950, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1050, 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='Does a bird fly right above the water in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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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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question: ['Does a bird fly right above the water in the image?'], 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([1, 3, 448, 448]) knan debug pixel values shape
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tensor([0.5924, 0.3954, 0.0006, 0.0008, 0.0072, 0.0017, 0.0007, 0.0012],
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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([0.5924, 0.3954, 0.0006, 0.0008, 0.0072, 0.0017, 0.0007, 0.0012],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3954, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5924, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0122, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Are there any dogs lying down in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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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: 13, images per sample: 13.0, dynamic token length: 3399
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tensor([7.2264e-01, 2.3603e-02, 2.4974e-01, 2.3721e-03, 1.1352e-04, 6.0481e-04,
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8.6600e-05, 8.4623e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.2264e-01, 2.3603e-02, 2.4974e-01, 2.3721e-03, 1.1352e-04, 6.0481e-04,
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8.6600e-05, 8.4623e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7226, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.2497, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0276, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many animals 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([1, 3, 448, 448])
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question: ['How many animals 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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question: ['Are there any dogs lying down in the image?'], 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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question: ['What color is the jellyfish?'], responses:['blue']
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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[('blue', 0.12610723189030773), ('kitten', 0.12505925935446505), ('iris', 0.12496487399785434), ('lemon', 0.12480860793572608), ('cherry', 0.12478264542061647), ('bright', 0.12478001416316817), ('peach', 0.12475640037922975), ('cookie', 0.12474096685863247)]
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[['blue', 'kitten', 'iris', 'lemon', 'cherry', 'bright', 'peach', 'cookie']]
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tensor([9.5795e-01, 7.5461e-03, 3.1455e-03, 1.2310e-03, 1.5814e-03, 1.2612e-03,
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2.7175e-02, 1.1040e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5795e-01, 7.5461e-03, 3.1455e-03, 1.2310e-03, 1.5814e-03, 1.2612e-03,
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2.7175e-02, 1.1040e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9579, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0421, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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torch.Size([7, 3, 448, 448])
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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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question: ['How many dogs are in the image?'], responses:['1']
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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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: 13, images per sample: 13.0, dynamic token length: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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tensor([5.4098e-01, 2.4127e-02, 4.3178e-01, 1.6544e-03, 1.0554e-04, 4.6233e-04,
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1.0697e-04, 7.8748e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.4098e-01, 2.4127e-02, 4.3178e-01, 1.6544e-03, 1.0554e-04, 4.6233e-04,
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1.0697e-04, 7.8748e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5410, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.4318, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0272, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many animals 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([7.6964e-01, 2.1081e-02, 2.0714e-01, 1.1798e-03, 9.4304e-05, 1.9223e-04,
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8.8413e-05, 5.8503e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.6964e-01, 2.1081e-02, 2.0714e-01, 1.1798e-03, 9.4304e-05, 1.9223e-04,
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8.8413e-05, 5.8503e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7696, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2071, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0232, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many laptops 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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question: ['How many laptops are in the image?'], responses:['3']
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[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
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[['3', '4', '1', '5', '8', '2', '6', '12']]
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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: 836
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question: ['How many animals 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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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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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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tensor([9.6856e-01, 5.0822e-03, 2.0532e-03, 8.2881e-04, 1.1696e-03, 8.4133e-04,
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2.1397e-02, 7.0041e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6856e-01, 5.0822e-03, 2.0532e-03, 8.2881e-04, 1.1696e-03, 8.4133e-04,
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2.1397e-02, 7.0041e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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