Datasets:
TMarcus
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InternVL

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device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the water's edge visible in the image?')
FINAL_ANSWER=RESULT(var=ANSWER0)
question: ['Is a person driving and holding a cell phone?'], responses:['yes']
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']]
question: ['Is the water'], 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([1, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 319
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 322
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 319
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 320
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 319
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 319
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 320
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 320
tensor([1.0000e+00, 2.6590e-08, 5.7150e-07, 1.3747e-06, 1.8954e-08, 3.4321e-08,
1.3044e-08, 3.3151e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.6590e-08, 5.7150e-07, 1.3747e-06, 1.8954e-08, 3.4321e-08,
1.3044e-08, 3.3151e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(5.7150e-07, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.4551e-06, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 3.3583e-09, 4.1875e-10, 1.5984e-09, 1.5735e-11, 4.9237e-11,
1.3944e-11, 2.0904e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.3583e-09, 4.1875e-10, 1.5984e-09, 1.5735e-11, 4.9237e-11,
1.3944e-11, 2.0904e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(4.1875e-10, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-4.1875e-10, device='cuda:1', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 6.4370e-09, 1.1009e-10, 5.9983e-08, 2.6202e-10, 3.6954e-10,
6.3471e-10, 2.1176e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.4370e-09, 1.1009e-10, 5.9983e-08, 2.6202e-10, 3.6954e-10,
6.3471e-10, 2.1176e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.1009e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1910e-07, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.9406e-01, 6.6319e-04, 3.8164e-03, 5.1139e-05, 4.5324e-09, 1.1639e-03,
5.7218e-09, 2.4397e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.9406e-01, 6.6319e-04, 3.8164e-03, 5.1139e-05, 4.5324e-09, 1.1639e-03,
5.7218e-09, 2.4397e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many white dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many white dogs are in the image?'], responses:['δΈ‰']
[('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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.6281e-05, 1.6338e-03, 6.3586e-02, 6.7693e-01, 1.0612e-01, 4.5982e-02,
3.8125e-03, 1.0192e-01], device='cuda:3', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([1.6281e-05, 1.6338e-03, 6.3586e-02, 6.7693e-01, 1.0612e-01, 4.5982e-02,
3.8125e-03, 1.0192e-01], device='cuda:3', grad_fn=<SelectBackward0>)
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>)}
[2024-10-24 09:41:18,594] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.35 | optimizer_step: 0.33
[2024-10-24 09:41:18,595] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 2524.88 | backward_microstep: 10140.13 | backward_inner_microstep: 2398.93 | backward_allreduce_microstep: 7741.14 | step_microstep: 7.88
[2024-10-24 09:41:18,595] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 2524.88 | backward: 10140.12 | backward_inner: 2398.94 | backward_allreduce: 7741.13 | step: 7.89
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4570/4844 [19:00:02<54:48, 12.00s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL stepANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is there a bottle of wine in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many animals are eating in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is there a bottle of wine in the image?'], responses:['yes']
question: ['How many animals are eating in the image?'], responses:['4']
[('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']]
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
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