Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='What color is the dog in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == "white"')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Do the golf balls in the left image look noticeably darker and grayer than those in the right image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many cheetahs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are the two pins touching each other?'], responses:['no']
[('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)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
tensor([5.2360e-01, 4.7535e-01, 1.2669e-04, 2.2963e-04, 5.1061e-05, 1.9501e-04,
3.5929e-04, 8.2538e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.2360e-01, 4.7535e-01, 1.2669e-04, 2.2963e-04, 5.1061e-05, 1.9501e-04,
3.5929e-04, 8.2538e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.5236, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.4754, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many boars are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['Do the golf balls in the left image look noticeably darker and grayer than those in the right image?'], responses:['yes']
question: ['How many cheetahs are in the image?'], responses:['4']
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many boars are in the image?'], responses:['4']
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1877
question: ['What color is the dog in the image?'], responses:['tan']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874
[('tan', 0.12670198546574601), ('pear', 0.12488736917128618), ('pan', 0.12483632219296452), ('broom', 0.12479636744714646), ('chimney', 0.12479439652246849), ('doll', 0.12468410687193951), ('hood', 0.12466784352901412), ('sauce', 0.12463160879943475)]
[['tan', 'pear', 'pan', 'broom', 'chimney', 'doll', 'hood', 'sauce']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1875
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874
tensor([0.2302, 0.2163, 0.1922, 0.0753, 0.1321, 0.0348, 0.0908, 0.0284],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.2302, 0.2163, 0.1922, 0.0753, 0.1321, 0.0348, 0.0908, 0.0284],
device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.8744, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.1256, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1874
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1875
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1875
tensor([5.4500e-01, 2.6244e-02, 4.2446e-01, 1.3627e-03, 1.6620e-04, 1.1466e-03,
9.6911e-05, 1.5286e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.4500e-01, 2.6244e-02, 4.2446e-01, 1.3627e-03, 1.6620e-04, 1.1466e-03,
9.6911e-05, 1.5286e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.5450, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.4245, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0305, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many Canadian geese are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([0.4696, 0.2361, 0.1995, 0.0059, 0.0437, 0.0140, 0.0287, 0.0024],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.4696, 0.2361, 0.1995, 0.0059, 0.0437, 0.0140, 0.0287, 0.0024],
device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([7, 3, 448, 448])
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0427, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9573, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the dog running in the image?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many Canadian geese are in the image?'], responses:['2']
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
question: ['Is the dog running in the image?'], responses:['yes']
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([9.4119e-01, 1.0041e-03, 2.3843e-03, 2.6707e-03, 3.1399e-02, 2.9495e-03,
1.4936e-04, 1.8255e-02], device='cuda:2', grad_fn=<SoftmaxBackward0>)
tan *************