Datasets:
TMarcus
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InternVL

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ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.6919e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.8610e-06, device='cuda:0', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.1142e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1142e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.4506e-08, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many white cloth tables are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
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=LEFT,question='How many fish are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 0')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many white cloth tables are in the image?'], responses:['22']
question: ['How many boats 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']]
[('22', 0.127303611220748), ('21', 0.12580915567947262), ('20', 0.12462384285120268), ('29', 0.12456320418980461), ('24', 0.12452402576809017), ('27', 0.12444759934168027), ('28', 0.12444713205094754), ('23', 0.12428142889805403)]
[['22', '21', '20', '29', '24', '27', '28', '23']]
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: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([1.0000e+00, 6.2616e-10, 9.9252e-07, 1.9514e-09, 2.4259e-09, 4.5201e-07,
9.2034e-09, 4.9765e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.2616e-10, 9.9252e-07, 1.9514e-09, 2.4259e-09, 4.5201e-07,
9.2034e-09, 4.9765e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.2616e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.9073e-06, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many fish are in the image?'], responses:['1']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([0.4260, 0.0351, 0.0508, 0.0478, 0.0802, 0.2174, 0.0843, 0.0585],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
22 *************
['22', '21', '20', '29', '24', '27', '28', '23'] tensor([0.4260, 0.0351, 0.0508, 0.0478, 0.0802, 0.2174, 0.0843, 0.0585],
device='cuda:0', grad_fn=<SelectBackward0>)
tensor([9.9999e-01, 7.7838e-06, 3.3587e-09, 1.3104e-08, 1.6853e-10, 1.2477e-06,
2.5057e-09, 6.0059e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9999e-01, 7.7838e-06, 3.3587e-09, 1.3104e-08, 1.6853e-10, 1.2477e-06,
2.5057e-09, 6.0059e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.2477e-06, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
tensor([9.9994e-01, 4.5465e-08, 1.4701e-09, 3.7320e-09, 1.8260e-09, 1.6892e-07,
6.2050e-05, 3.6001e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9994e-01, 4.5465e-08, 1.4701e-09, 3.7320e-09, 1.8260e-09, 1.6892e-07,
6.2050e-05, 3.6001e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:05:45,435] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.37 | optimizer_step: 0.33
[2024-10-24 10:05:45,435] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7107.55 | backward_microstep: 10760.06 | backward_inner_microstep: 6808.52 | backward_allreduce_microstep: 3951.43 | step_microstep: 7.80
[2024-10-24 10:05:45,436] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7107.57 | backward: 10760.05 | backward_inner: 6808.55 | backward_allreduce: 3951.37 | step: 7.82
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4665/4844 [19:24:29<50:30, 16.93s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there a white razor knife in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
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
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many chairs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a black pair of sneakers sitting on a shoe box in the image?')
ANSWER1=RESULT(var=ANSWER0)
ANSWER0=VQA(image=RIGHT,question='Does the image contain an outside view of a storefront?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([5, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Is there a white razor knife 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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many chairs 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']]
tensor([1.0000e+00, 7.8241e-10, 1.0313e-06, 4.8761e-11, 4.1037e-10, 6.9002e-08,
4.0819e-09, 1.1794e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 7.8241e-10, 1.0313e-06, 4.8761e-11, 4.1037e-10, 6.9002e-08,