Datasets:
TMarcus
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InternVL

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['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions'] tensor([0.5892, 0.0967, 0.1939, 0.0116, 0.0235, 0.0528, 0.0126, 0.0197],
device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9821, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0179, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many penguins are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 7')
FINAL_ANSWER=RESULT(var=ANSWER1)
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>)}
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])
torch.Size([13, 3, 448, 448])
question: ['How many seals 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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['How many penguins are in the image?'], responses:['5']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
[['5', '8', '4', '6', '3', '7', '11', '9']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([6.2615e-01, 1.8471e-02, 3.5159e-01, 1.4149e-03, 1.6828e-04, 6.3398e-04,
1.0466e-04, 1.4692e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.2615e-01, 1.8471e-02, 3.5159e-01, 1.4149e-03, 1.6828e-04, 6.3398e-04,
1.0466e-04, 1.4692e-03], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([6.5061e-01, 3.4824e-01, 8.6254e-05, 1.0908e-04, 3.1648e-04, 1.9570e-04,
2.7770e-04, 1.6678e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.5061e-01, 3.4824e-01, 8.6254e-05, 1.0908e-04, 3.1648e-04, 1.9570e-04,
2.7770e-04, 1.6678e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.3516, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6261, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0223, device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: 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)
{True: tensor(0.3482, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6506, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0012, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many boars are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([7.9146e-01, 1.3754e-01, 2.0443e-02, 3.9402e-02, 7.5200e-03, 1.6261e-03,
1.9014e-03, 1.1109e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.9146e-01, 1.3754e-01, 2.0443e-02, 3.9402e-02, 7.5200e-03, 1.6261e-03,
1.9014e-03, 1.1109e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7915, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2085, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
question: ['How many boars are in the image?'], responses:['1']
[('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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is there a human visible next to the german shepherd dog?'], 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([9.4885e-01, 9.0158e-03, 3.8774e-03, 1.6657e-03, 2.3514e-03, 1.6530e-03,
3.2464e-02, 1.2310e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.4885e-01, 9.0158e-03, 3.8774e-03, 1.6657e-03, 2.3514e-03, 1.6530e-03,
3.2464e-02, 1.2310e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0512, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9488, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([0.2856, 0.1051, 0.0599, 0.2683, 0.0121, 0.1959, 0.0134, 0.0598],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2856, 0.1051, 0.0599, 0.2683, 0.0121, 0.1959, 0.0134, 0.0598],
device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8218, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1782, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([9.4275e-01, 5.6631e-02, 4.2231e-05, 5.4109e-05, 1.6869e-04, 1.9920e-04,
7.7341e-05, 7.3935e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.4275e-01, 5.6631e-02, 4.2231e-05, 5.4109e-05, 1.6869e-04, 1.9920e-04,
7.7341e-05, 7.3935e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9428, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0566, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0006, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-23 14:51:14,181] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.33 | optimizer_step: 0.33
[2024-10-23 14:51:14,181] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5156.64 | backward_microstep: 12622.50 | backward_inner_microstep: 4829.70 | backward_allreduce_microstep: 7792.69 | step_microstep: 7.91
[2024-10-23 14:51:14,181] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5156.64 | backward: 12622.49 | backward_inner: 4829.73 | backward_allreduce: 7792.64 | step: 7.93
1%| | 39/4844 [09:57<21:57:24, 16.45s/it]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='How many pencil cases are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many monkeys are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step