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ANSWER0=VQA(image=RIGHT,question='How many people 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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question: ['Is a person pushing the dispenser?'], 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([8.5392e-01, 2.0419e-02, 1.2319e-01, 8.8939e-04, 1.5797e-04, 8.5125e-04,
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6.9221e-05, 5.0469e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.5392e-01, 2.0419e-02, 1.2319e-01, 8.8939e-04, 1.5797e-04, 8.5125e-04,
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6.9221e-05, 5.0469e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.8539, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1232, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0229, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many dog feet are visible 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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question: ['How many animals are in the image?'], responses:['1']
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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 people are in the image?'], responses:['0']
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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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[('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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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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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: 1860
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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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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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question: ['How many dog feet are visible in the image?'], 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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torch.Size([13, 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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tensor([9.5929e-01, 7.3242e-03, 2.7800e-03, 9.9085e-04, 1.3978e-03, 9.2792e-04,
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2.7213e-02, 7.2496e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5929e-01, 7.3242e-03, 2.7800e-03, 9.9085e-04, 1.3978e-03, 9.2792e-04,
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2.7213e-02, 7.2496e-05], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0407, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9593, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([9.8154e-01, 3.4307e-03, 1.5702e-03, 8.1228e-04, 1.1478e-03, 8.6835e-04,
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1.0568e-02, 6.6293e-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([9.8154e-01, 3.4307e-03, 1.5702e-03, 8.1228e-04, 1.1478e-03, 8.6835e-04,
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1.0568e-02, 6.6293e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ANSWER0=VQA(image=LEFT,question='How many balls are next to the hole?')
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ANSWER1=EVAL(expr='{ANSWER0} == 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([9.8515e-01, 3.3847e-03, 1.6429e-03, 4.9360e-04, 1.3823e-03, 6.7034e-04,
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2.0711e-03, 5.2041e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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0 *************
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['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.8515e-01, 3.3847e-03, 1.6429e-03, 4.9360e-04, 1.3823e-03, 6.7034e-04,
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2.0711e-03, 5.2041e-03], device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0185, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9815, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0.9852, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0148, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many humans 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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ANSWER0=VQA(image=LEFT,question='How many women are wearing swimsuits 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([13, 3, 448, 448])
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torch.Size([1, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many women are wearing swimsuits 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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tensor([5.1914e-01, 9.0095e-02, 1.9510e-02, 3.5681e-01, 8.3914e-03, 2.5590e-03,
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3.1848e-03, 3.0336e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([5.1914e-01, 9.0095e-02, 1.9510e-02, 3.5681e-01, 8.3914e-03, 2.5590e-03,
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3.1848e-03, 3.0336e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5191, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4809, 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=RIGHT,question='How many vases 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([13, 3, 448, 448])
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question: ['How many humans 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([7, 3, 448, 448]) knan debug pixel values shape
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question: ['How many balls are next to the hole?'], responses:['4']
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[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
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[['4', '5', '3', '8', '6', '1', '2', '11']]
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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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question: ['How many vases are in the image?'], responses:['2']
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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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([13, 3, 448, 448]) knan debug pixel values shape
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