Datasets:
TMarcus
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InternVL

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torch.Size([13, 3, 448, 448])
question: ['Is there a black horse in the image?'], responses:['no']
question: ['Is the plow on the truck yellow?'], responses:['yes']
[('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']]
[('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([3, 3, 448, 448]) knan debug pixel values shape
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 5.0511e-10, 4.7377e-07, 6.3222e-11, 4.5600e-10, 4.5820e-08,
7.0667e-10, 5.5387e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 5.0511e-10, 4.7377e-07, 6.3222e-11, 4.5600e-10, 4.5820e-08,
7.0667e-10, 5.5387e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.0511e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.0729e-06, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 6.5875e-09, 2.2067e-10, 1.1874e-08, 1.2184e-10, 4.7450e-10,
9.3685e-12, 1.0775e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.5875e-09, 2.2067e-10, 1.1874e-08, 1.2184e-10, 4.7450e-10,
9.3685e-12, 1.0775e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.2067e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.2067e-10, device='cuda:3', grad_fn=<DivBackward0>)}
question: ['How many llamas are in the image?'], responses:['2']
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([1.0000e+00, 2.0908e-09, 8.8649e-10, 2.1741e-09, 2.3165e-11, 1.3595e-10,
1.8204e-11, 7.5066e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.0908e-09, 8.8649e-10, 2.1741e-09, 2.3165e-11, 1.3595e-10,
1.8204e-11, 7.5066e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(8.8649e-10, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-8.8649e-10, device='cuda:2', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the dog situated in the grass?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
question: ['Is the dog situated in the grass?'], 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: 3397
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([5.5466e-01, 4.4533e-01, 4.8328e-06, 3.0700e-07, 2.5701e-08, 1.6202e-10,
2.9630e-09, 5.5249e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([5.5466e-01, 4.4533e-01, 4.8328e-06, 3.0700e-07, 2.5701e-08, 1.6202e-10,
2.9630e-09, 5.5249e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.0700e-07, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 8.5784e-10, 7.3699e-07, 9.9604e-11, 5.8822e-10, 1.4458e-07,
1.0608e-08, 2.9739e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.5784e-10, 7.3699e-07, 9.9604e-11, 5.8822e-10, 1.4458e-07,
1.0608e-08, 2.9739e-06], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(8.5784e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.8147e-06, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:07:27,932] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.42 | optimizer_gradients: 0.26 | optimizer_step: 0.31
[2024-10-24 10:07:27,932] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7084.20 | backward_microstep: 10678.52 | backward_inner_microstep: 6757.28 | backward_allreduce_microstep: 3921.16 | step_microstep: 7.62
[2024-10-24 10:07:27,932] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7084.21 | backward: 10678.51 | backward_inner: 6757.30 | backward_allreduce: 3921.12 | step: 7.63
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4672/4844 [19:26:11<46:19, 16.16s/it]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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is someone touching a vending machine in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a swoop design visible on the shoe on the right?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many bottles are in the image?'], responses:['6']
[('6', 0.12794147189263105), ('8', 0.12539492259598553), ('12', 0.12539359088927945), ('5', 0.12471292164321114), ('4', 0.12443617393590153), ('1', 0.12417386497855347), ('11', 0.12398049124372558), ('3', 0.12396656282071232)]
[['6', '8', '12', '5', '4', '1', '11', '3']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
tensor([4.5984e-01, 1.2082e-04, 2.7702e-06, 5.3998e-01, 1.8213e-06, 3.6675e-10,
4.7130e-05, 1.7886e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
5 *************
['6', '8', '12', '5', '4', '1', '11', '3'] tensor([4.5984e-01, 1.2082e-04, 2.7702e-06, 5.3998e-01, 1.8213e-06, 3.6675e-10,
4.7130e-05, 1.7886e-10], device='cuda:2', grad_fn=<SelectBackward0>)