Datasets:
TMarcus
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InternVL

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ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.3140, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6860, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
ANSWER0=VQA(image=LEFT,question='Is there a dish visible in the image?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([9.4358e-01, 1.4325e-02, 7.4305e-03, 2.9042e-03, 4.2293e-03, 2.9479e-03,
2.4371e-02, 2.1517e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.4358e-01, 1.4325e-02, 7.4305e-03, 2.9042e-03, 4.2293e-03, 2.9479e-03,
2.4371e-02, 2.1517e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9436, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0564, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
question: ['Is there a dish visible in the image?'], responses:['no']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
[('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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
tensor([6.6288e-01, 2.0694e-02, 3.1312e-01, 1.5553e-03, 2.0547e-04, 5.9828e-04,
9.6508e-05, 8.5317e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.6288e-01, 2.0694e-02, 3.1312e-01, 1.5553e-03, 2.0547e-04, 5.9828e-04,
9.6508e-05, 8.5317e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.6629, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3131, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0240, 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: 3394
tensor([0.6604, 0.1125, 0.0674, 0.0873, 0.0145, 0.0199, 0.0080, 0.0300],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
light *************
['light', 'sunlight', 'lights', 'wine', 'water', 'glass', 'lamps', 'dark'] tensor([0.6604, 0.1125, 0.0674, 0.0873, 0.0145, 0.0199, 0.0080, 0.0300],
device='cuda:0', grad_fn=<SelectBackward0>)
tensor([7.4248e-01, 2.5659e-01, 3.8339e-05, 1.4398e-04, 1.7427e-04, 4.3088e-04,
1.0796e-04, 3.7021e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.4248e-01, 2.5659e-01, 3.8339e-05, 1.4398e-04, 1.7427e-04, 4.3088e-04,
1.0796e-04, 3.7021e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7425, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2566, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0009, device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-23 14:41:40,211] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-23 14:41:40,211] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6737.87 | backward_microstep: 5297.60 | backward_inner_microstep: 5291.77 | backward_allreduce_microstep: 5.74 | step_microstep: 14.79
[2024-10-23 14:41:40,212] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6737.74 | backward: 5297.59 | backward_inner: 5291.79 | backward_allreduce: 5.73 | step: 14.80
0%| | 1/4844 [00:24<32:18:28, 24.02s/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='How many windows are on the left wall?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many boats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a human holding a dog in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Can you see the customers in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Can you see the customers 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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['Is there a human holding a dog 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']]
tensor([6.9835e-01, 2.3419e-02, 2.7348e-01, 2.6040e-03, 1.8967e-04, 6.1416e-04,
1.0074e-04, 1.2463e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.9835e-01, 2.3419e-02, 2.7348e-01, 2.6040e-03, 1.8967e-04, 6.1416e-04,
1.0074e-04, 1.2463e-03], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.6983, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2735, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0282, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a woman in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
question: ['How many windows are on the left wall?'], responses:['2']
question: ['How many boats are in the image?'], responses:['3']
[('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']]
[('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']]
question: ['Is there a woman in the image?'], 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']]