Datasets:
TMarcus
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InternVL

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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([7.6683e-01, 2.4650e-02, 7.7557e-03, 1.7371e-03, 2.8646e-03, 1.4294e-03,
1.9466e-01, 7.5309e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1947, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8053, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many seals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many boars are in the image?'], responses:['1']
question: ['How many seals are in the image?'], responses:['2']
[('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']]
[('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([7, 3, 448, 448]) knan debug pixel values shape
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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([9.6499e-01, 5.0635e-03, 1.8053e-03, 6.8403e-04, 1.0608e-03, 6.3789e-04,
2.5715e-02, 4.1845e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6499e-01, 5.0635e-03, 1.8053e-03, 6.8403e-04, 1.0608e-03, 6.3789e-04,
2.5715e-02, 4.1845e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0257, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9743, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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([9.3240e-01, 1.2891e-02, 5.7198e-03, 2.3819e-03, 3.2585e-03, 2.2238e-03,
4.0966e-02, 1.6121e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.3240e-01, 1.2891e-02, 5.7198e-03, 2.3819e-03, 3.2585e-03, 2.2238e-03,
4.0966e-02, 1.6121e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0676, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9324, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a human visible next to the german shepherd dog?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([7.6515e-01, 1.5067e-01, 2.4595e-02, 4.5949e-02, 9.0481e-03, 2.0186e-03,
2.4351e-03, 1.3809e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.6515e-01, 1.5067e-01, 2.4595e-02, 4.5949e-02, 9.0481e-03, 2.0186e-03,
2.4351e-03, 1.3809e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7651, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2349, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
torch.Size([13, 3, 448, 448])
Encountered ExecuteError: CUDA out of memory. Tried to allocate 1.17 GiB. GPU 3 has a total capacty of 44.34 GiB of which 924.94 MiB is free. Including non-PyTorch memory, this process has 43.42 GiB memory in use. Of the allocated memory 37.85 GiB is allocated by PyTorch, and 5.02 GiB is reserved by PyTorch but unalloc...
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
ANSWER0=VQA(image=RIGHT,question='Are straws visible in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['Are straws visible 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
tensor([6.4060e-01, 1.3414e-02, 3.4289e-01, 9.4435e-04, 8.3034e-05, 2.4960e-04,
8.8756e-05, 1.7375e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.4060e-01, 1.3414e-02, 3.4289e-01, 9.4435e-04, 8.3034e-05, 2.4960e-04,
8.8756e-05, 1.7375e-03], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.6406, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3429, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0165, device='cuda:3', grad_fn=<DivBackward0>)}
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.93 GiB. GPU 0 has a total capacty of 44.34 GiB of which 788.94 MiB is free. Including non-PyTorch memory, this process has 43.55 GiB memory in use. Of the allocated memory 40.73 GiB is allocated by PyTorch, and 2.20 GiB is reserved by PyTorch but unalloc...
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
[2024-10-22 17:26:35,779] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.52 | optimizer_gradients: 0.24 | optimizer_step: 0.31
[2024-10-22 17:26:35,780] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 13289.40 | backward_microstep: 10773.76 | backward_inner_microstep: 10768.32 | backward_allreduce_microstep: 5.22 | step_microstep: 7.82
[2024-10-22 17:26:35,780] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 13289.42 | backward: 10773.75 | backward_inner: 10768.39 | backward_allreduce: 5.20 | step: 7.83
1%| | 20/2424 [08:08<16:06:56, 24.13s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 10')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
ANSWER0=VQA(image=RIGHT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
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='Is there at least one person standing in front of and staring ahead at a row of vending machines?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many round plates are visible in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many dogs are in the image?'], responses:['15']
[('15', 0.12850265658859292), ('14', 0.12554598114685298), ('13', 0.12491622450863256), ('16', 0.12450938797787274), ('29', 0.12444750181633149), ('35', 0.12413627702798803), ('22', 0.12400388658176363), ('21', 0.12393808435196574)]
[['15', '14', '13', '16', '29', '35', '22', '21']]