Datasets:
TMarcus
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InternVL

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torch.Size([7, 3, 448, 448])
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
question: ['How many golf balls are in the image?'], responses:['7']
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
[['7', '8', '11', '5', '9', '10', '6', '12']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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
tensor([8.5023e-01, 1.1245e-03, 1.3840e-01, 8.3578e-07, 3.4834e-03, 6.4635e-03,
3.2101e-05, 2.6718e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([8.5023e-01, 1.1245e-03, 1.3840e-01, 8.3578e-07, 3.4834e-03, 6.4635e-03,
3.2101e-05, 2.6718e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.9997e-01, 3.4266e-05, 3.6248e-08, 3.1217e-09, 3.3533e-11, 6.1913e-07,
6.4442e-11, 3.3826e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9997e-01, 3.4266e-05, 3.6248e-08, 3.1217e-09, 3.3533e-11, 6.1913e-07,
6.4442e-11, 3.3826e-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(6.5538e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.3440e-01, 5.2156e-03, 1.8100e-02, 3.3826e-07, 4.0768e-02, 1.3567e-03,
9.5281e-05, 6.5503e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.3440e-01, 5.2156e-03, 1.8100e-02, 3.3826e-07, 4.0768e-02, 1.3567e-03,
9.5281e-05, 6.5503e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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>)}
tensor([9.9998e-01, 4.3671e-07, 4.2007e-07, 1.0205e-09, 1.4768e-05, 1.7527e-08,
8.7341e-07, 4.1390e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9998e-01, 4.3671e-07, 4.2007e-07, 1.0205e-09, 1.4768e-05, 1.7527e-08,
8.7341e-07, 4.1390e-06], 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(2.0742e-05, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:19:18,848] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.41 | optimizer_gradients: 0.23 | optimizer_step: 0.30
[2024-10-24 10:19:18,848] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6354.80 | backward_microstep: 7577.02 | backward_inner_microstep: 6082.54 | backward_allreduce_microstep: 1494.41 | step_microstep: 7.63
[2024-10-24 10:19:18,849] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6354.80 | backward: 7577.01 | backward_inner: 6082.55 | backward_allreduce: 1494.41 | step: 7.64
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4720/4844 [19:38:02<28:39, 13.87s/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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is the body of the hyena facing left?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many parrots are perched on a branch in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many birds 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([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many birds are in the image?'], responses:['11']
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
[['11', '10', '12', '9', '8', '13', '7', '14']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['Is the body of the hyena facing left?'], responses:['no']
question: ['How many parrots are perched on a branch in the image?'], responses:['1']
question: ['How many people are in the image?'], responses:['3']
[('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']]
[('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']]
[('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([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: 1867
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
tensor([9.4938e-01, 3.9262e-04, 6.8486e-03, 1.0208e-05, 1.3958e-08, 2.9417e-02,
2.4066e-07, 1.3948e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.4938e-01, 3.9262e-04, 6.8486e-03, 1.0208e-05, 1.3958e-08, 2.9417e-02,
2.4066e-07, 1.3948e-02], device='cuda:3', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many mountain goats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867