Datasets:
TMarcus
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InternVL

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tensor([9.7355e-01, 1.6573e-03, 6.1000e-04, 8.5238e-05, 1.0054e-03, 1.8025e-04,
2.2906e-02, 6.5728e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.7355e-01, 1.6573e-03, 6.1000e-04, 8.5238e-05, 1.0054e-03, 1.8025e-04,
2.2906e-02, 6.5728e-06], 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 09:46:57,900] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.50 | optimizer_gradients: 0.26 | optimizer_step: 0.30
[2024-10-24 09:46:57,900] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7043.28 | backward_microstep: 10651.26 | backward_inner_microstep: 6725.22 | backward_allreduce_microstep: 3925.97 | step_microstep: 7.87
[2024-10-24 09:46:57,900] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7043.29 | backward: 10651.25 | backward_inner: 6725.24 | backward_allreduce: 3925.96 | step: 7.88
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4592/4844 [19:05:41<1:10:55, 16.89s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis stepRegistering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many bottles are grouped together in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the right image include rustic curving walls made of stones of varying shapes?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=RIGHT,question='Is there a human hand holding the animal in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the pizza in the image have green peppers on it?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does the pizza in the image have green peppers on it?'], 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many bottles are grouped together in the image?'], responses:['3']
question: ['Is there a human hand holding the animal in the image?'], responses:['yes']
question: ['Does the right image include rustic curving walls made of stones of varying shapes?'], responses:['yes']
[('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']]
[('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']]
[('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
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3407
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
tensor([1.0000e+00, 1.4951e-08, 1.8190e-09, 1.7463e-08, 1.5400e-10, 3.0161e-10,
2.4980e-11, 3.1452e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.4951e-08, 1.8190e-09, 1.7463e-08, 1.5400e-10, 3.0161e-10,
2.4980e-11, 3.1452e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.8190e-09, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1739e-07, device='cuda:3', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
question: ['How many dogs 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: 13, images per sample: 13.0, dynamic token length: 3404
tensor([1.0000e+00, 1.1096e-10, 3.7166e-11, 1.3176e-10, 6.7297e-11, 1.3643e-08,
1.4730e-09, 1.6012e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.1096e-10, 3.7166e-11, 1.3176e-10, 6.7297e-11, 1.3643e-08,
1.4730e-09, 1.6012e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.4730e-09, 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>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
tensor([1.0000e+00, 5.8683e-09, 2.2987e-11, 9.8293e-08, 5.6814e-10, 1.5558e-09,
8.4425e-11, 4.5839e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.8683e-09, 2.2987e-11, 9.8293e-08, 5.6814e-10, 1.5558e-09,
8.4425e-11, 4.5839e-09], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([9.9987e-01, 1.3217e-04, 5.3500e-07, 5.0273e-08, 5.6583e-10, 3.3110e-07,
3.1987e-09, 2.3454e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9987e-01, 1.3217e-04, 5.3500e-07, 5.0273e-08, 5.6583e-10, 3.3110e-07,
3.1987e-09, 2.3454e-07], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 1.7445e-09, 5.3769e-10, 2.1713e-08, 8.4233e-11, 1.2970e-10,
5.0272e-11, 3.1373e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.7445e-09, 5.3769e-10, 2.1713e-08, 8.4233e-11, 1.2970e-10,
5.0272e-11, 3.1373e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9999, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0001, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.2987e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1919e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many puppies are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)