Datasets:
TMarcus
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InternVL

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tensor([1.0000e+00, 6.3852e-10, 6.7959e-11, 8.7087e-11, 1.0796e-10, 2.8132e-09,
1.8554e-07, 2.5691e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 6.3852e-10, 6.7959e-11, 8.7087e-11, 1.0796e-10, 2.8132e-09,
1.8554e-07, 2.5691e-11], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.8928e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.3860e-10, 4.0210e-07, 2.6146e-12, 2.6576e-11, 1.0455e-09,
3.9321e-10, 1.2264e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.3860e-10, 4.0210e-07, 2.6146e-12, 2.6576e-11, 1.0455e-09,
3.9321e-10, 1.2264e-06], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.3860e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.5497e-06, device='cuda:3', grad_fn=<DivBackward0>)}
question: ['Does the image contain food inside of a bowl?'], 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 5.1378e-09, 1.0103e-10, 8.2659e-09, 1.8237e-10, 4.2115e-11,
1.7356e-11, 1.2791e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.1378e-09, 1.0103e-10, 8.2659e-09, 1.8237e-10, 4.2115e-11,
1.7356e-11, 1.2791e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0103e-10, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.0103e-10, device='cuda:2', grad_fn=<SubBackward0>)}
[2024-10-24 09:27:52,976] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.37 | optimizer_gradients: 0.32 | optimizer_step: 0.32
[2024-10-24 09:27:52,977] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3138.90 | backward_microstep: 14602.96 | backward_inner_microstep: 2994.53 | backward_allreduce_microstep: 11608.35 | step_microstep: 8.01
[2024-10-24 09:27:52,977] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3138.90 | backward: 14602.95 | backward_inner: 2994.55 | backward_allreduce: 11608.34 | step: 8.02
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4516/4844 [18:46:36<1:23:17, 15.24s/it]Registering VQA_lavis 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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is the cow walking through the water?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is the white dog standing on all fours?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many golf balls are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many ointment containers are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is the white dog standing on all fours?'], 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
tensor([1.0000e+00, 1.6541e-09, 2.8173e-09, 1.3773e-09, 1.3458e-11, 7.7904e-12,
8.2209e-12, 1.5447e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.6541e-09, 2.8173e-09, 1.3773e-09, 1.3458e-11, 7.7904e-12,
8.2209e-12, 1.5447e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.8173e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.8173e-09, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many vases are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many vases 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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
tensor([9.9989e-01, 4.0016e-08, 2.8926e-08, 3.1105e-09, 2.8572e-10, 1.1232e-04,
6.3387e-09, 6.3076e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9989e-01, 4.0016e-08, 2.8926e-08, 3.1105e-09, 2.8572e-10, 1.1232e-04,
6.3387e-09, 6.3076e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.8926e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['Is the cow walking through the water?'], responses:['no']
question: ['How many golf balls are in the image?'], responses:['3']
question: ['How many ointment containers are in the image?'], responses:['2']
[('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']]
[('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: 3396
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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: 3397
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: 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
tensor([1.0000e+00, 4.6351e-10, 7.2712e-07, 1.2921e-10, 1.0763e-09, 1.5619e-07,
1.8036e-08, 2.1892e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)