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InternVL

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0%| | 2/4844 [00:37<24:03:12, 17.88s/it]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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many chairs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Does the image appear to feature an open air shop?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is the dog swimming in a pool?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the image show a "Whataburger" cup sitting on a surface?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many chairs are in the image?'], responses:['6']
[('6', 0.12794147189263105), ('8', 0.12539492259598553), ('12', 0.12539359088927945), ('5', 0.12471292164321114), ('4', 0.12443617393590153), ('1', 0.12417386497855347), ('11', 0.12398049124372558), ('3', 0.12396656282071232)]
[['6', '8', '12', '5', '4', '1', '11', '3']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['Does the image show a "Whataburger" cup sitting on a surface?'], 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([3, 3, 448, 448]) knan debug pixel values shape
tensor([0.3836, 0.1702, 0.0199, 0.2279, 0.1502, 0.0082, 0.0150, 0.0248],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
6 *************
['6', '8', '12', '5', '4', '1', '11', '3'] tensor([0.3836, 0.1702, 0.0199, 0.2279, 0.1502, 0.0082, 0.0150, 0.0248],
device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the left image show food served in a rectangular dish?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([8.9230e-01, 2.1710e-02, 8.2997e-02, 8.8495e-04, 7.9196e-05, 2.3256e-04,
5.0350e-05, 1.7463e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.9230e-01, 2.1710e-02, 8.2997e-02, 8.8495e-04, 7.9196e-05, 2.3256e-04,
5.0350e-05, 1.7463e-03], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: question: ['Is the dog swimming in a pool?'], responses:['yes']
{True: tensor(0.8923, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.0830, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0247, device='cuda:1', grad_fn=<SubBackward0>)}
question: ['Does the image appear to feature an open air shop?'], responses:['yes']
ANSWER0=VQA(image=LEFT,question='How many hamsters are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
[('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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
question: ['How many hamsters are in the image?'], responses:['1']
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
[('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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
question: ['Does the left image show food served in a rectangular dish?'], 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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([6.5198e-01, 4.1679e-02, 1.0214e-02, 1.7743e-03, 3.5298e-03, 1.4252e-03,
2.8931e-01, 7.9676e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.5198e-01, 4.1679e-02, 1.0214e-02, 1.7743e-03, 3.5298e-03, 1.4252e-03,
2.8931e-01, 7.9676e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.2893, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.7107, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([8.6585e-01, 2.1692e-02, 1.1008e-01, 1.0154e-03, 1.1963e-04, 4.9138e-04,
5.5701e-05, 6.9404e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.6585e-01, 2.1692e-02, 1.1008e-01, 1.0154e-03, 1.1963e-04, 4.9138e-04,
5.5701e-05, 6.9404e-04], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([5.7441e-01, 2.6460e-02, 3.9479e-01, 2.1466e-03, 2.5609e-04, 8.5304e-04,
1.3771e-04, 9.5435e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.7441e-01, 2.6460e-02, 3.9479e-01, 2.1466e-03, 2.5609e-04, 8.5304e-04,
1.3771e-04, 9.5435e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.8659, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1101, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0241, device='cuda:2', grad_fn=<DivBackward0>)}
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.5744, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3948, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0308, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Are the dogs heading to the right?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)