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TMarcus
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InternVL

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Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='What is the background color behind the dogs?')
ANSWER1=EVAL(expr='{ANSWER0} == "white" or {ANSWER0} == "plain white"')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the phone in the slide out position?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many boats are sailing in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Is the man holding something in his hands?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['What is the background color behind the dogs?'], responses:['white']
question: ['Is the phone in the slide out position?'], responses:['no']
[('white', 0.12741698904857263), ('black', 0.12562195821587463), ('purple', 0.12482758531934457), ('orange', 0.12467593918870701), ('maroon', 0.12456097552653009), ('color', 0.12448461429606533), ('brown', 0.12421598902969112), ('dark', 0.12419594937521464)]
[['white', 'black', 'purple', 'orange', 'maroon', 'color', 'brown', 'dark']]
[('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
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
tensor([9.9952e-01, 1.4036e-04, 1.6343e-06, 1.1761e-06, 6.6406e-05, 2.3489e-06,
2.5727e-04, 1.3999e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
white *************
['white', 'black', 'purple', 'orange', 'maroon', 'color', 'brown', 'dark'] tensor([9.9952e-01, 1.4036e-04, 1.6343e-06, 1.1761e-06, 6.6406e-05, 2.3489e-06,
2.5727e-04, 1.3999e-05], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([9.9962e-01, 3.7998e-04, 9.3250e-08, 6.4192e-11, 1.1237e-11, 6.4937e-10,
7.8934e-10, 2.1592e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9962e-01, 3.7998e-04, 9.3250e-08, 6.4192e-11, 1.1237e-11, 6.4937e-10,
7.8934e-10, 2.1592e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0004, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.9996, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:0', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the image contain a chandelier?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Do the structures in the image have grass on the roof?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many boats are sailing in the image?'], responses:['2']
question: ['Is the man holding something in his hands?'], responses:['yes']
question: ['Does the image contain a chandelier?'], 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']]
[('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']]
[('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: 1861
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: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
question: ['Do the structures in the image have grass on the roof?'], 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: 7, images per sample: 7.0, dynamic token length: 1861
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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([1.0000e+00, 7.8233e-10, 4.4499e-07, 1.8476e-09, 9.7714e-10, 1.4405e-07,
6.7576e-09, 2.3824e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 7.8233e-10, 4.4499e-07, 1.8476e-09, 9.7714e-10, 1.4405e-07,
6.7576e-09, 2.3824e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(7.8233e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.2325e-08, 8.6778e-09, 2.0208e-07, 1.2562e-10, 1.1744e-09,
1.7711e-09, 1.2781e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.2325e-08, 8.6778e-09, 2.0208e-07, 1.2562e-10, 1.1744e-09,
1.7711e-09, 1.2781e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(8.6778e-09, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(2.2974e-07, device='cuda:2', grad_fn=<SubBackward0>)}
tensor([9.9984e-01, 4.5393e-05, 7.9787e-08, 1.1591e-04, 6.9717e-09, 2.0545e-10,
2.0933e-09, 1.1902e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9984e-01, 4.5393e-05, 7.9787e-08, 1.1591e-04, 6.9717e-09, 2.0545e-10,