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Registering RESULT step
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ANSWER0=VQA(image=RIGHT,question='Is there any animal standing on the roof?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=LEFT,question='Is there a ladder leaning against the bookcase?')
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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 people are in the car?')
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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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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many people are in the car?'], 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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question: ['Is there any animal standing on the roof?'], responses:['yes']
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question: ['Is there a ladder leaning against the bookcase?'], responses:['no']
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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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[('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: 3397
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['Is the dog wearing a collar?'], 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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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: 3397
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tensor([7.4496e-01, 3.2733e-02, 1.3225e-02, 2.4440e-03, 3.3434e-03, 1.7960e-03,
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2.0141e-01, 9.3126e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([7.4496e-01, 3.2733e-02, 1.3225e-02, 2.4440e-03, 3.3434e-03, 1.7960e-03,
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2.0141e-01, 9.3126e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.2550, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.7450, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many lotion bottles are visible 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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question: ['How many lotion bottles are visible 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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torch.Size([3, 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: 3398
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tensor([0.6306, 0.2181, 0.0267, 0.0486, 0.0058, 0.0457, 0.0185, 0.0062],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.6306, 0.2181, 0.0267, 0.0486, 0.0058, 0.0457, 0.0185, 0.0062],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.7029, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2971, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many monkeys 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([7, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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tensor([6.3436e-01, 2.3606e-02, 3.3955e-01, 8.7245e-04, 1.5775e-04, 5.5908e-04,
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1.5372e-04, 7.3579e-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([6.3436e-01, 2.3606e-02, 3.3955e-01, 8.7245e-04, 1.5775e-04, 5.5908e-04,
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1.5372e-04, 7.3579e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.6344, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3396, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0261, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='What is the material of the jewelry in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == "safety pins"')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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question: ['How many monkeys are in the image?'], responses:['3']
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tensor([7.7660e-01, 2.2250e-01, 4.8210e-05, 1.0244e-04, 2.0032e-04, 1.8026e-04,
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2.8830e-04, 7.6350e-05], 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([7.7660e-01, 2.2250e-01, 4.8210e-05, 1.0244e-04, 2.0032e-04, 1.8026e-04,
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2.8830e-04, 7.6350e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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torch.Size([13, 3, 448, 448])
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.2225, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7766, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0009, device='cuda:3', grad_fn=<DivBackward0>)}
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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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ANSWER0=VQA(image=RIGHT,question='How many laptop computers are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 4')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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tensor([8.4979e-01, 1.7365e-02, 1.3032e-01, 9.6562e-04, 6.0585e-05, 2.1476e-04,
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6.4105e-05, 1.2217e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.4979e-01, 1.7365e-02, 1.3032e-01, 9.6562e-04, 6.0585e-05, 2.1476e-04,
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6.4105e-05, 1.2217e-03], device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.8498, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1303, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0199, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many dogs are sitting on the wooden structure?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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question: ['How many dogs are sitting on the wooden structure?'], 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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question: ['What is the material of the jewelry in the image?'], responses:['metal']
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