Datasets:
TMarcus
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InternVL

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Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the dog lying down on the ground?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=RIGHT,question='Is the dog lying in the grass outside?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=LEFT,question='Is the bird eating from a flower?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is the sled rider wearing a white vest with a number?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is the dog lying down on the ground?'], responses:['yes']
question: ['Is the dog lying in the grass outside?'], 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']]
[('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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is the bird eating from a flower?'], responses:['no']
question: ['Is the sled rider wearing a white vest with a number?'], 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']]
[('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: 3401
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
tensor([9.9995e-01, 7.3028e-09, 5.1442e-05, 8.7192e-10, 2.4895e-12, 6.2595e-12,
2.4478e-11, 1.6188e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9995e-01, 7.3028e-09, 5.1442e-05, 8.7192e-10, 2.4895e-12, 6.2595e-12,
2.4478e-11, 1.6188e-09], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 3.3209e-09, 1.3177e-10, 2.1305e-09, 2.5540e-11, 3.9563e-11,
9.8931e-12, 2.3721e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.3209e-09, 1.3177e-10, 2.1305e-09, 2.5540e-11, 3.9563e-11,
9.8931e-12, 2.3721e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9999, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(5.1442e-05, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.6199e-08, device='cuda:1', grad_fn=<SubBackward0>)}
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.3177e-10, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.3177e-10, device='cuda:2', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is the laptop facing forward?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Is the laptop facing forward?'], 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: 3401
tensor([1.0000e+00, 9.4781e-09, 5.7150e-07, 1.5027e-08, 6.6253e-11, 3.4715e-10,
2.0116e-10, 5.4701e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.4781e-09, 5.7150e-07, 1.5027e-08, 6.6253e-11, 3.4715e-10,
2.0116e-10, 5.4701e-09], device='cuda:1', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.7150e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.4546e-08, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many wolves 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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
tensor([1.0000e+00, 3.3647e-10, 3.0245e-07, 1.0361e-11, 2.2745e-11, 1.7925e-08,
1.7582e-09, 1.3678e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.3647e-10, 3.0245e-07, 1.0361e-11, 2.2745e-11, 1.7925e-08,
1.7582e-09, 1.3678e-06], device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(3.3647e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-06, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 6.7441e-10, 5.3132e-07, 1.0689e-10, 1.5923e-09, 1.0604e-07,
6.2346e-09, 1.4746e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.7441e-10, 5.3132e-07, 1.0689e-10, 1.5923e-09, 1.0604e-07,
6.2346e-09, 1.4746e-06], device='cuda:0', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=RIGHT,question='How many black beetles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(6.7441e-10, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(2.1458e-06, device='cuda:0', grad_fn=<SubBackward0>)}
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many syringes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])