Datasets:
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InternVL

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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['Is there a stack of three books on the front-most corner of the shelf under the couch?'], 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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1875
tensor([8.3082e-01, 2.1460e-02, 1.4437e-01, 1.6612e-03, 7.9024e-05, 3.4628e-04,
4.4869e-05, 1.2158e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.3082e-01, 2.1460e-02, 1.4437e-01, 1.6612e-03, 7.9024e-05, 3.4628e-04,
4.4869e-05, 1.2158e-03], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.8308, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.1444, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0248, device='cuda:2', grad_fn=<SubBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872
ANSWER0=VQA(image=LEFT,question='Is there sun coming in through the window?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many tusked animals 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']]
question: ['Is there sun coming in through the window?'], 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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1873
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['Does the image on the left have a man'], responses:['No']
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872
tensor([6.9130e-01, 3.0676e-01, 9.9584e-05, 2.4792e-04, 3.9925e-04, 2.0964e-04,
5.7984e-04, 3.9668e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.9130e-01, 3.0676e-01, 9.9584e-05, 2.4792e-04, 3.9925e-04, 2.0964e-04,
5.7984e-04, 3.9668e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.3068, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6913, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0019, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many cheetahs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1873
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1873
tensor([6.4896e-01, 2.2896e-02, 3.2243e-01, 1.9143e-03, 2.1075e-04, 8.4359e-04,
1.3002e-04, 2.6169e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.4896e-01, 2.2896e-02, 3.2243e-01, 1.9143e-03, 2.1075e-04, 8.4359e-04,
1.3002e-04, 2.6169e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.6490, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3224, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0286, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many cheetahs are in the image?'], responses:['1']
question: ['How many animals 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']]
[('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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
tensor([9.7085e-01, 4.3576e-03, 1.6030e-03, 4.4494e-04, 7.5712e-04, 4.9946e-04,
2.1449e-02, 3.5999e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7085e-01, 4.3576e-03, 1.6030e-03, 4.4494e-04, 7.5712e-04, 4.9946e-04,
2.1449e-02, 3.5999e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9967, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0033, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are there sea mammals in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['Are there sea mammals in the image?'], responses:['yes']
tensor([9.0520e-01, 1.9373e-02, 7.5886e-03, 3.3553e-03, 4.1891e-03, 2.2891e-03,
5.7867e-02, 1.3874e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.0520e-01, 1.9373e-02, 7.5886e-03, 3.3553e-03, 4.1891e-03, 2.2891e-03,
5.7867e-02, 1.3874e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0176, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9824, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='What color is the jellyfish?')
ANSWER1=EVAL(expr='{ANSWER0} == "pink"')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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])
tensor([8.9496e-01, 1.9763e-02, 8.2395e-03, 2.6651e-03, 3.2247e-03, 1.9275e-03,
6.9012e-02, 2.0614e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.9496e-01, 1.9763e-02, 8.2395e-03, 2.6651e-03, 3.2247e-03, 1.9275e-03,
6.9012e-02, 2.0614e-04], device='cuda:3', grad_fn=<SelectBackward0>)