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InternVL

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Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many windows are on the left wall?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a floating buoy extending from a boat into the water by a rope on the right side of the image?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=RIGHT,question='How many computers are displayed in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is there an object on top of the television in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many windows are on the left wall?'], 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']]
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
question: ['Is there a floating buoy extending from a boat into the water by a rope on the right side of the image?'], responses:['no']
question: ['Is there an object on top of the television in the image?'], responses:['yes']
question: ['How many computers are displayed in the image?'], responses:['3']
[('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']]
[('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']]
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1876
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1876
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1877
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1876
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1876
tensor([1.0000e+00, 4.6912e-08, 1.3101e-09, 9.6604e-10, 6.3241e-09, 1.8554e-07,
4.1812e-07, 1.7149e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.6912e-08, 1.3101e-09, 9.6604e-10, 6.3241e-09, 1.8554e-07,
4.1812e-07, 1.7149e-09], device='cuda:2', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1877
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(4.6912e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many seals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many seals are in the image?'], responses:['four']
[('7 eleven', 0.12650899275575006), ('4', 0.125210025275264), ('first', 0.12483048280083887), ('3', 0.12473532336671392), ('5', 0.1247268629491862), ('dark', 0.12470563072493092), ('forward', 0.12466964370422237), ('bag', 0.12461303842309367)]
[['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1877
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1877
tensor([1.0000e+00, 7.9975e-08, 8.7275e-10, 4.2755e-07, 7.3336e-10, 5.9516e-10,
1.5806e-08, 2.8925e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([3.9954e-14, 9.9964e-01, 2.5058e-06, 5.8968e-06, 3.4594e-04, 1.0455e-06,
1.6961e-06, 1.2198e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
tensor([1.0000e+00, 7.9975e-08, 8.7275e-10, 4.2755e-07, 7.3336e-10, 5.9516e-10,
1.5806e-08, 2.8925e-08], device='cuda:1', grad_fn=<SelectBackward0>)
4 *************
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([3.9954e-14, 9.9964e-01, 2.5058e-06, 5.8968e-06, 3.4594e-04, 1.0455e-06,
1.6961e-06, 1.2198e-07], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 2.3356e-09, 4.0518e-07, 3.2290e-11, 1.3867e-10, 8.9616e-09,
1.4017e-09, 2.9910e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: ['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.3356e-09, 4.0518e-07, 3.2290e-11, 1.3867e-10, 8.9616e-09,
1.4017e-09, 2.9910e-07], device='cuda:0', grad_fn=<SelectBackward0>)
{True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.4240e-06, device='cuda:2', grad_fn=<DivBackward0>)}
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: tensor([9.9982e-01, 1.6865e-04, 1.7427e-07, 1.1140e-08, 2.7032e-10, 1.2217e-05,
1.6885e-09, 1.3865e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9982e-01, 1.6865e-04, 1.7427e-07, 1.1140e-08, 2.7032e-10, 1.2217e-05,
1.6885e-09, 1.3865e-08], device='cuda:3', grad_fn=<SelectBackward0>)
{True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(8.7275e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9517e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(2.3356e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:0', grad_fn=<SubBackward0>)}
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9998, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0002, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many corgi dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the sink square in the image?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is the sink square in the image?'], 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']]