Datasets:
TMarcus
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InternVL

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ANSWER0=VQA(image=RIGHT,question='Is the food being served in a white dish?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many laptops are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1077, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8923, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many blue parrots are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many laptops 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']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([9.6884e-01, 4.3482e-03, 1.7568e-03, 6.6647e-04, 8.2965e-04, 7.2638e-04,
2.2783e-02, 4.9345e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6884e-01, 4.3482e-03, 1.7568e-03, 6.6647e-04, 8.2965e-04, 7.2638e-04,
2.2783e-02, 4.9345e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0228, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9772, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
question: ['Is the food being served in a white dish?'], responses:['yes']
question: ['How many blue parrots are in the image?'], responses:['1']
ANSWER0=VQA(image=RIGHT,question='Is the dog on a leash?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 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']]
[('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 the dog on a leash?'], 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([1, 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: 13, images per sample: 13.0, dynamic token length: 3399
tensor([9.4625e-01, 5.3384e-02, 5.2762e-05, 5.9635e-05, 9.4810e-06, 8.6973e-05,
9.4035e-05, 6.5530e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.4625e-01, 5.3384e-02, 5.2762e-05, 5.9635e-05, 9.4810e-06, 8.6973e-05,
9.4035e-05, 6.5530e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0534, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9462, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0004, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([0.2178, 0.0801, 0.1911, 0.1574, 0.1589, 0.0956, 0.0330, 0.0661],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2178, 0.0801, 0.1911, 0.1574, 0.1589, 0.0956, 0.0330, 0.0661],
device='cuda:1', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='Is there a dark blue bottle in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many jellyfish are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
question: ['How many jellyfish are in the image?'], responses:['1']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
[('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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
question: ['Is there a dark blue bottle 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([8.7440e-01, 2.6402e-02, 1.0668e-02, 5.5282e-03, 7.5622e-03, 3.4424e-03,
7.1774e-02, 2.1977e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.7440e-01, 2.6402e-02, 1.0668e-02, 5.5282e-03, 7.5622e-03, 3.4424e-03,
7.1774e-02, 2.1977e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1256, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8744, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='What are the vultures doing in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == "feeding"')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([7.9924e-01, 1.8995e-02, 1.7833e-01, 1.2058e-03, 1.5983e-04, 5.3165e-04,
2.9110e-05, 1.5082e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.9924e-01, 1.8995e-02, 1.7833e-01, 1.2058e-03, 1.5983e-04, 5.3165e-04,
2.9110e-05, 1.5082e-03], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([8.4721e-01, 3.4940e-02, 1.5503e-02, 5.7041e-03, 8.2961e-03, 3.9012e-03,
8.3873e-02, 5.7342e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.4721e-01, 3.4940e-02, 1.5503e-02, 5.7041e-03, 8.2961e-03, 3.9012e-03,
8.3873e-02, 5.7342e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7992, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1783, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0224, device='cuda:2', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1528, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8472, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many apes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)