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Registering EVAL step
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Registering RESULT step
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torch.Size([1, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Are all of the sails on the boat red?')
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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 vehicle driving in front of a house?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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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: ['How many sets of measuring utensils are in the image?'], responses:['8']
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[('8', 0.12723446457289017), ('9', 0.12488291461145089), ('12', 0.12481394644705951), ('7', 0.12480302292408052), ('5', 0.12471410185987472), ('6', 0.12470198211184266), ('11', 0.12452966814724155), ('10', 0.12431989932555992)]
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[['8', '9', '12', '7', '5', '6', '11', '10']]
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
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tensor([0.1679, 0.1656, 0.1141, 0.1052, 0.1073, 0.1372, 0.0900, 0.1127],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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8 *************
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['8', '9', '12', '7', '5', '6', '11', '10'] tensor([0.1679, 0.1656, 0.1141, 0.1052, 0.1073, 0.1372, 0.0900, 0.1127],
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device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is there a chalkboard in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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question: ['Does the image show a hound standing on thick green grass?'], responses:['no']
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torch.Size([3, 3, 448, 448])
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question: ['Is the vehicle driving in front of a house?'], responses:['yes']
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question: ['Are all of the sails on the boat red?'], responses:['yes']
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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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[('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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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['Is there a chalkboard 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([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: 837
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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tensor([9.1892e-01, 1.9735e-02, 5.8761e-02, 1.4188e-03, 6.6666e-05, 2.6144e-04,
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3.9054e-05, 7.9562e-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([9.1892e-01, 1.9735e-02, 5.8761e-02, 1.4188e-03, 6.6666e-05, 2.6144e-04,
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3.9054e-05, 7.9562e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9189, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0588, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0223, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many dogs are standing on all fours?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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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 dogs are standing on all fours?'], 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([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: 838
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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tensor([6.4929e-01, 2.4419e-02, 3.2259e-01, 1.5078e-03, 1.4360e-04, 6.4930e-04,
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1.4927e-04, 1.2507e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.4929e-01, 2.4419e-02, 3.2259e-01, 1.5078e-03, 1.4360e-04, 6.4930e-04,
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1.4927e-04, 1.2507e-03], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([8.8638e-01, 1.1269e-01, 6.4219e-05, 6.3209e-05, 7.6051e-05, 3.2663e-04,
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3.1318e-04, 8.0692e-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([8.8638e-01, 1.1269e-01, 6.4219e-05, 6.3209e-05, 7.6051e-05, 3.2663e-04,
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3.1318e-04, 8.0692e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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tensor([6.0308e-01, 2.7463e-02, 3.6579e-01, 1.4532e-03, 2.1674e-04, 8.4974e-04,
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1.1397e-04, 1.0410e-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([6.0308e-01, 2.7463e-02, 3.6579e-01, 1.4532e-03, 2.1674e-04, 8.4974e-04,
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1.1397e-04, 1.0410e-03], device='cuda:2', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.6493, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.3226, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0281, device='cuda:1', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many locks 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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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.1127, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.8864, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0009, device='cuda:3', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many dogs are visible on grassy ground?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.6031, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.3658, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0311, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Are the boats in the water?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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