Datasets:
TMarcus
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InternVL

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94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4557/4844 [18:57:14<1:19:44, 16.67s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many multi-packs of paper towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there a colorful dog toy in the image?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many birds are standing on posts in water?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='What is the position of the dog in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == "side profile"')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many multi-packs of paper towels are in the image?'], responses:['3']
[('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([1, 3, 448, 448]) knan debug pixel values shape
tensor([9.4202e-01, 1.1032e-03, 7.5096e-05, 4.3671e-06, 4.9531e-06, 5.6614e-02,
9.0559e-05, 8.5072e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.4202e-01, 1.1032e-03, 7.5096e-05, 4.3671e-06, 4.9531e-06, 5.6614e-02,
9.0559e-05, 8.5072e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.5096e-05, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9999, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many hyenas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['Is there a colorful dog toy in the image?'], 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']]
question: ['What is the position of the dog in the image?'], responses:['s']
[('s', 0.1292678692397635), ('m', 0.12600113012185044), ('d', 0.125059711934273), ('closet', 0.12412510541505085), ('h', 0.12402688750925138), ('l', 0.12397377741182078), ('tan', 0.12384923745478878), ('striped', 0.12369628091320145)]
[['s', 'm', 'd', 'closet', 'h', 'l', 'tan', 'striped']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many birds are standing on posts in water?'], responses:['δΈ‰']
question: ['How many hyenas are in the image?'], responses:['1']
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
[('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([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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([1.0000e+00, 1.4460e-09, 3.9279e-07, 3.3193e-09, 1.4450e-12, 2.1974e-12,
1.0656e-10, 1.6251e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.4460e-09, 3.9279e-07, 3.3193e-09, 1.4450e-12, 2.1974e-12,
1.0656e-10, 1.6251e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(3.9279e-07, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-3.5158e-08, device='cuda:2', grad_fn=<SubBackward0>)}
tensor([2.1982e-03, 1.9767e-04, 1.2978e-03, 3.6242e-01, 4.9481e-04, 7.8052e-03,
8.4924e-02, 5.4066e-01], device='cuda:3', grad_fn=<SoftmaxBackward0>)
striped *************
['s', 'm', 'd', 'closet', 'h', 'l', 'tan', 'striped'] tensor([2.1982e-03, 1.9767e-04, 1.2978e-03, 3.6242e-01, 4.9481e-04, 7.8052e-03,
8.4924e-02, 5.4066e-01], device='cuda:3', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='How many birds are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many gorillas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
question: ['How many birds are in the image?'], responses:['3']
[('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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 4.2868e-10, 1.7731e-10, 4.7078e-10, 1.9463e-10, 2.4316e-08,
3.5199e-09, 4.4679e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.2868e-10, 1.7731e-10, 4.7078e-10, 1.9463e-10, 2.4316e-08,
3.5199e-09, 4.4679e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.5199e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many gorillas 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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
tensor([9.7557e-01, 7.0085e-06, 1.0436e-08, 3.2462e-08, 2.3881e-09, 2.4426e-02,
3.2715e-08, 2.7545e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)