Datasets:
TMarcus
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InternVL

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Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the dog in the image have one paw resting on a ball?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are there triangular pennants on display?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many towels 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([5, 3, 448, 448]) knan debug pixel values shape
question: ['Does the dog in the image have one paw resting on a ball?'], 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is a human at least partially visible in the image?'], responses:['yes']
question: ['Are there triangular pennants on display?'], responses:['no']
[('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']]
[('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([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([7.3335e-01, 2.5824e-02, 2.3953e-01, 5.9142e-06, 9.9133e-04, 4.0018e-06,
2.4716e-06, 3.0168e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([7.3335e-01, 2.5824e-02, 2.3953e-01, 5.9142e-06, 9.9133e-04, 4.0018e-06,
2.4716e-06, 3.0168e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.2395, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7605, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the dog facing right?')
FINAL_ANSWER=RESULT(var=ANSWER0)
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 6.4121e-08, 3.0705e-07, 6.3583e-12, 2.4576e-12, 3.3569e-09,
3.1473e-10, 3.3170e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.4121e-08, 3.0705e-07, 6.3583e-12, 2.4576e-12, 3.3569e-09,
3.1473e-10, 3.3170e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(6.4121e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(6.5565e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a red canoe in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
question: ['Is there a red canoe 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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([1.0000e+00, 3.8897e-09, 3.0636e-10, 1.9883e-08, 7.8671e-11, 2.2106e-11,
1.0964e-10, 5.5651e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.8897e-09, 3.0636e-10, 1.9883e-08, 7.8671e-11, 2.2106e-11,
1.0964e-10, 5.5651e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(3.0636e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.0636e-10, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['Is the dog facing right?'], 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 1.0622e-08, 9.4366e-10, 3.5070e-08, 5.4485e-11, 2.2414e-10,
1.6305e-10, 1.7133e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.0622e-08, 9.4366e-10, 3.5070e-08, 5.4485e-11, 2.2414e-10,
1.6305e-10, 1.7133e-09], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 1.1744e-09, 4.2471e-07, 3.1857e-10, 6.1139e-09, 4.7422e-08,
1.7521e-08, 8.0590e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: tensor([1.0000e+00, 1.1744e-09, 4.2471e-07, 3.1857e-10, 6.1139e-09, 4.7422e-08,
1.7521e-08, 8.0590e-07], device='cuda:0', grad_fn=<SelectBackward0>)
{True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(9.4366e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-9.4366e-10, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many baboons are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1.1744e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:0', grad_fn=<DivBackward0>)}
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many cases are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
question: ['How many baboons are in the image?'], responses:['1']
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
[['1', '3', '4', '8', '6', '12', '2', '47']]