Datasets:
TMarcus
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InternVL

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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.2043e-01, 1.4966e-02, 6.2636e-02, 1.3146e-03, 7.4059e-05, 2.4780e-04,
3.5491e-05, 2.9625e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9204, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0626, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0169, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
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: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
question: ['How many dogs 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([9.2710e-01, 7.1490e-02, 5.5080e-05, 1.0412e-04, 3.5295e-04, 5.5302e-04,
2.1335e-04, 1.3233e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.2710e-01, 7.1490e-02, 5.5080e-05, 1.0412e-04, 3.5295e-04, 5.5302e-04,
2.1335e-04, 1.3233e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0715, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9271, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0014, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([6.1279e-01, 2.2320e-02, 1.0369e-02, 3.4915e-01, 3.0651e-03, 1.0589e-03,
1.1112e-03, 1.4187e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.1279e-01, 2.2320e-02, 1.0369e-02, 3.4915e-01, 3.0651e-03, 1.0589e-03,
1.1112e-03, 1.4187e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='Is the golf ball on a tee?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.6128, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3872, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the baby seal lying down?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([13, 3, 448, 448])
question: ['Is the golf ball on a tee?'], 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([3, 3, 448, 448]) knan debug pixel values shape
tensor([7.0518e-01, 2.9397e-01, 4.9058e-05, 1.1162e-04, 6.7161e-05, 1.1557e-04,
4.6555e-04, 3.8154e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.0518e-01, 2.9397e-01, 4.9058e-05, 1.1162e-04, 6.7161e-05, 1.1557e-04,
4.6555e-04, 3.8154e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.2940, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.7052, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['Is the baby seal lying down?'], 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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([6.1803e-01, 1.6570e-01, 1.2776e-02, 3.4861e-02, 2.1604e-03, 1.5623e-01,
9.6446e-03, 5.9979e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([6.1803e-01, 1.6570e-01, 1.2776e-02, 3.4861e-02, 2.1604e-03, 1.5623e-01,
9.6446e-03, 5.9979e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7870, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.2130, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([7.9768e-01, 2.0168e-01, 2.0550e-05, 4.2963e-05, 4.8296e-05, 3.0904e-04,
1.6217e-04, 5.4263e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.9768e-01, 2.0168e-01, 2.0550e-05, 4.2963e-05, 4.8296e-05, 3.0904e-04,
1.6217e-04, 5.4263e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.2017, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.7977, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0006, device='cuda:0', grad_fn=<SubBackward0>)}
[2024-10-23 14:53:40,400] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.43 | optimizer_gradients: 0.26 | optimizer_step: 0.32
[2024-10-23 14:53:40,400] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9061.14 | backward_microstep: 8750.42 | backward_inner_microstep: 8744.57 | backward_allreduce_microstep: 5.69 | step_microstep: 7.63
[2024-10-23 14:53:40,400] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9061.15 | backward: 8750.40 | backward_inner: 8744.64 | backward_allreduce: 5.67 | step: 7.65
1%| | 48/4844 [12:24<22:24:54, 16.83s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
ANSWER0=VQA(image=LEFT,question='Does the image have a white background?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image contain a saxophone?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many dogs are sitting in the grass?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many wine glasses are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([13, 3, 448, 448])