Datasets:
TMarcus
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InternVL

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[('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']]
[('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']]
[('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']]
[('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([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
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
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: 1861
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: 1861
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: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([8.6690e-01, 1.3276e-01, 1.0130e-05, 4.1791e-05, 5.8557e-05, 1.6932e-04,
5.2957e-05, 1.0021e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.6690e-01, 1.3276e-01, 1.0130e-05, 4.1791e-05, 5.8557e-05, 1.6932e-04,
5.2957e-05, 1.0021e-05], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([5.4734e-01, 8.1097e-03, 8.8189e-04, 2.2281e-04, 3.9110e-04, 2.6708e-04,
4.4277e-01, 1.6019e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.4734e-01, 8.1097e-03, 8.8189e-04, 2.2281e-04, 3.9110e-04, 2.6708e-04,
4.4277e-01, 1.6019e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0099, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9901, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([8.5659e-01, 1.0768e-02, 8.3031e-04, 1.3118e-01, 3.3523e-04, 1.2837e-04,
1.4400e-04, 2.2459e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.5659e-01, 1.0768e-02, 8.3031e-04, 1.3118e-01, 3.3523e-04, 1.2837e-04,
1.4400e-04, 2.2459e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1328, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8669, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.5942e-01, 3.6837e-03, 9.0276e-04, 3.0221e-04, 4.3988e-04, 2.9134e-04,
3.4950e-02, 1.4096e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5942e-01, 3.6837e-03, 9.0276e-04, 3.0221e-04, 4.3988e-04, 2.9134e-04,
3.4950e-02, 1.4096e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='How many elephants are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0406, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9594, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8566, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1434, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many water buffaloes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many anemones are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many elephants 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: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['How many dogs are in the image?'], responses:['1']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['How many water buffaloes are in the image?'], responses:['1']
question: ['How many anemones are in the image?'], responses:['2']
[('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']]
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([9.7919e-01, 1.4869e-02, 1.2992e-03, 4.0012e-03, 4.0879e-04, 9.9999e-05,
1.2077e-04, 7.7105e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.7919e-01, 1.4869e-02, 1.2992e-03, 4.0012e-03, 4.0879e-04, 9.9999e-05,
1.2077e-04, 7.7105e-06], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9792, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0208, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.9584e-01, 4.5659e-04, 1.7876e-04, 8.9478e-05, 1.3907e-04, 1.1926e-04,
3.1695e-03, 8.0434e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9584e-01, 4.5659e-04, 1.7876e-04, 8.9478e-05, 1.3907e-04, 1.1926e-04,
3.1695e-03, 8.0434e-06], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0042, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9958, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.7791e-01, 3.1124e-03, 1.4701e-03, 4.6220e-04, 7.6262e-04, 4.4596e-04,
1.5806e-02, 2.6134e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7791e-01, 3.1124e-03, 1.4701e-03, 4.6220e-04, 7.6262e-04, 4.4596e-04,