Datasets:
TMarcus
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InternVL

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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 5.6028e-09, 1.3268e-07, 1.0588e-10, 5.7682e-13, 1.2840e-09,
7.0426e-10, 6.4674e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.6028e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many cases are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 6.1615e-09, 1.4166e-09, 4.2867e-08, 2.1740e-11, 3.9255e-11,
1.4111e-10, 8.5357e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.1615e-09, 1.4166e-09, 4.2867e-08, 2.1740e-11, 3.9255e-11,
1.4111e-10, 8.5357e-09], device='cuda:2', grad_fn=<SelectBackward0>)
torch.Size([1, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.4166e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.4166e-09, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are the instruments displayed horizontally?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many cases 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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
tensor([9.9944e-01, 1.4878e-04, 5.0909e-06, 1.3149e-07, 3.4644e-07, 1.2092e-06,
4.0442e-04, 6.9269e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9944e-01, 1.4878e-04, 5.0909e-06, 1.3149e-07, 3.4644e-07, 1.2092e-06,
4.0442e-04, 6.9269e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9994, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0006, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
question: ['How many windows 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([13, 3, 448, 448]) knan debug pixel values shape
question: ['Are the instruments displayed horizontally?'], 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([1.4854e-01, 8.5137e-01, 2.9608e-09, 9.3639e-05, 2.8264e-09, 1.5641e-08,
6.7582e-08, 1.1294e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
4 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.4854e-01, 8.5137e-01, 2.9608e-09, 9.3639e-05, 2.8264e-09, 1.5641e-08,
6.7582e-08, 1.1294e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.9608e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the lefthand image contain a vertical stack of three solid-white pillows?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['Does the lefthand image contain a vertical stack of three solid-white pillows?'], 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, 6.1405e-10, 4.2115e-11, 3.8950e-11, 2.0092e-10, 2.4313e-08,
1.7806e-08, 9.7029e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 6.1405e-10, 4.2115e-11, 3.8950e-11, 2.0092e-10, 2.4313e-08,
1.7806e-08, 9.7029e-11], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(4.3112e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.9027e-09, 3.2563e-07, 1.1085e-09, 2.4131e-12, 5.6112e-12,
5.2580e-12, 5.5249e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.9027e-09, 3.2563e-07, 1.1085e-09, 2.4131e-12, 5.6112e-12,
5.2580e-12, 5.5249e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.2563e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.1997e-08, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 4.9429e-09, 1.0223e-10, 6.4794e-09, 1.5943e-09, 3.1369e-10,
7.4553e-11, 1.5276e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.9429e-09, 1.0223e-10, 6.4794e-09, 1.5943e-09, 3.1369e-10,
7.4553e-11, 1.5276e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0223e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.0223e-10, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:17:54,211] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.35 | optimizer_step: 0.33
[2024-10-24 10:17:54,211] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3121.34 | backward_microstep: 14945.53 | backward_inner_microstep: 3014.32 | backward_allreduce_microstep: 11931.10 | step_microstep: 7.76
[2024-10-24 10:17:54,211] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3121.35 | backward: 14945.52 | backward_inner: 3014.34 | backward_allreduce: 11931.08 | step: 7.78
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4714/4844 [19:36:38<31:22, 14.48s/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 VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the dog in the image on all fours?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many pillows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)