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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Does the boat on the right have a blue sail cover?')
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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([5, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Does light spill through a small hole in the cave wall?')
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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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torch.Size([7, 3, 448, 448])
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question: ['Does the boat on the right have a blue sail cover?'], 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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question: ['How many basil leaves are on the pizza?'], responses:['0']
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[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
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[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
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tensor([1.0000e+00, 3.7898e-09, 9.2374e-09, 7.9041e-10, 7.6687e-12, 2.6355e-11,
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3.5030e-12, 3.8707e-10], 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, 3.7898e-09, 9.2374e-09, 7.9041e-10, 7.6687e-12, 2.6355e-11,
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3.5030e-12, 3.8707e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(9.2374e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-9.2374e-09, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many chairs 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([5, 3, 448, 448]) knan debug pixel values shape
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torch.Size([5, 3, 448, 448])
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question: ['Is the animal in the image lying down?'], responses:['yes']
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question: ['Does light spill through a small hole in the cave wall?'], 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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[('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([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: 1864
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
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question: ['How many chairs 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: 7, images per sample: 7.0, dynamic token length: 1864
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torch.Size([5, 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: 1865
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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tensor([9.9999e-01, 4.9908e-06, 6.5312e-08, 2.5486e-11, 1.5242e-07, 5.0108e-08,
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1.2313e-06, 3.4351e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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0 *************
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['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9999e-01, 4.9908e-06, 6.5312e-08, 2.5486e-11, 1.5242e-07, 5.0108e-08,
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1.2313e-06, 3.4351e-06], device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.8944e-06, device='cuda:2', 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} <= 1')
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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: 1864
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torch.Size([5, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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question: ['How many dogs 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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tensor([9.9933e-01, 2.0598e-09, 8.3223e-10, 4.0401e-10, 3.4154e-10, 7.0375e-08,
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6.6670e-04, 7.3719e-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([9.9933e-01, 2.0598e-09, 8.3223e-10, 4.0401e-10, 3.4154e-10, 7.0375e-08,
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6.6670e-04, 7.3719e-11], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 1.1708e-08, 4.7923e-09, 2.7851e-08, 5.9914e-11, 2.5005e-10,
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5.2935e-10, 6.9491e-09], 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, 1.1708e-08, 4.7923e-09, 2.7851e-08, 5.9914e-11, 2.5005e-10,
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5.2935e-10, 6.9491e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9993, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 7.3453e-09, 8.2457e-11, 8.2059e-09, 5.2405e-11, 1.8010e-10,
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3.8785e-11, 5.4956e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.3453e-09, 8.2457e-11, 8.2059e-09, 5.2405e-11, 1.8010e-10,
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3.8785e-11, 5.4956e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(4.7923e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.7923e-09, device='cuda:0', grad_fn=<DivBackward0>)}
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(8.2457e-11, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-8.2457e-11, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the building fenced in?')
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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='How many TVs 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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torch.Size([13, 3, 448, 448])
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question: ['How many TVs 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: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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tensor([1.0000e+00, 3.3911e-10, 7.3913e-11, 1.9935e-10, 9.4871e-11, 1.7776e-08,
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3.3195e-09, 2.6121e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.3911e-10, 7.3913e-11, 1.9935e-10, 9.4871e-11, 1.7776e-08,
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3.3195e-09, 2.6121e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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