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TMarcus
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InternVL

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question: ['How many dogs are standing in the snow?'], responses:['1']
question: ['How many wild pigs are in the image?'], responses:['10']
[('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']]
question: ['How many cats are lying down?'], responses:['1']
[('10', 0.1277249466426885), ('11', 0.12579928416580372), ('12', 0.12560051978633632), ('8', 0.1247991444010043), ('9', 0.12459861387933152), ('26', 0.12389435171102943), ('13', 0.12388731669200545), ('6', 0.12369582272180085)]
[['10', '11', '12', '8', '9', '26', '13', '6']]
[('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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many wolves 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([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
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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([0.3858, 0.1616, 0.0670, 0.0128, 0.0215, 0.0085, 0.3422, 0.0005],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.3858, 0.1616, 0.0670, 0.0128, 0.0215, 0.0085, 0.3422, 0.0005],
device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.3422, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6578, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([0.1734, 0.1329, 0.1441, 0.1638, 0.1601, 0.0166, 0.1120, 0.0972],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
10 *************
['10', '11', '12', '8', '9', '26', '13', '6'] tensor([0.1734, 0.1329, 0.1441, 0.1638, 0.1601, 0.0166, 0.1120, 0.0972],
device='cuda:3', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=RIGHT,question='How many gorillas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([5.7155e-01, 7.7362e-02, 1.7260e-02, 1.8783e-03, 4.5022e-03, 1.3724e-03,
3.2597e-01, 9.7435e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.7155e-01, 7.7362e-02, 1.7260e-02, 1.8783e-03, 4.5022e-03, 1.3724e-03,
3.2597e-01, 9.7435e-05], device='cuda:2', grad_fn=<SelectBackward0>)
torch.Size([3, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.4284, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.5716, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many gorillas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the panda in the image have paws on a branch?')
ANSWER1=EVAL(expr='{ANSWER0}')
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
question: ['How many gorillas 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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([0.8058, 0.0704, 0.0243, 0.0143, 0.0022, 0.0749, 0.0063, 0.0018],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.8058, 0.0704, 0.0243, 0.0143, 0.0022, 0.0749, 0.0063, 0.0018],
device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0992, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9008, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
question: ['How many gorillas are in the image?'], responses:['4']
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([6.8696e-01, 5.0122e-02, 1.3915e-02, 3.1012e-03, 4.6598e-03, 1.9986e-03,
2.3908e-01, 1.5593e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.8696e-01, 5.0122e-02, 1.3915e-02, 3.1012e-03, 4.6598e-03, 1.9986e-03,
2.3908e-01, 1.5593e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.2391, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.7609, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['Does the panda in the image have paws on a branch?'], 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([0.4534, 0.2581, 0.1776, 0.0094, 0.0614, 0.0117, 0.0256, 0.0028],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.4534, 0.2581, 0.1776, 0.0094, 0.0614, 0.0117, 0.0256, 0.0028],
device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9627, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0373, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([8.8677e-01, 1.1263e-01, 4.8391e-05, 6.3174e-05, 4.3440e-05, 2.3679e-04,
1.1581e-04, 8.6132e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.8677e-01, 1.1263e-01, 4.8391e-05, 6.3174e-05, 4.3440e-05, 2.3679e-04,
1.1581e-04, 8.6132e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1126, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8868, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0006, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-23 14:48:48,665] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.28 | optimizer_step: 0.31
[2024-10-23 14:48:48,666] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3158.73 | backward_microstep: 10718.87 | backward_inner_microstep: 3055.19 | backward_allreduce_microstep: 7663.60 | step_microstep: 7.92
[2024-10-23 14:48:48,666] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3158.75 | backward: 10718.85 | backward_inner: 3055.20 | backward_allreduce: 7663.59 | step: 7.94
1%| | 30/4844 [07:32<20:11:47, 15.10s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step