Datasets:
TMarcus
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InternVL

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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([9.9960e-01, 1.5376e-07, 5.1797e-09, 4.0448e-04, 1.3966e-09, 1.5238e-09,
3.9293e-09, 2.2936e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9960e-01, 1.5376e-07, 5.1797e-09, 4.0448e-04, 1.3966e-09, 1.5238e-09,
3.9293e-09, 2.2936e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.6601e-07, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 8.7275e-10, 1.7731e-10, 3.0875e-10, 2.6825e-10, 4.7904e-09,
1.4761e-08, 3.7844e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 8.7275e-10, 1.7731e-10, 3.0875e-10, 2.6825e-10, 4.7904e-09,
1.4761e-08, 3.7844e-11], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(6.4553e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:23:43,073] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.26 | optimizer_step: 0.31
[2024-10-24 10:23:43,074] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5776.19 | backward_microstep: 12197.62 | backward_inner_microstep: 5460.90 | backward_allreduce_microstep: 6736.65 | step_microstep: 7.57
[2024-10-24 10:23:43,074] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5776.19 | backward: 12197.61 | backward_inner: 5460.91 | backward_allreduce: 6736.63 | step: 7.58
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4738/4844 [19:42:26<26:39, 15.09s/it]Registering VQA_lavis step
Registering EVAL stepRegistering VQA_lavis step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are the animals surrounded by a fence?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
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 multi-packs of paper towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many bottles are in the image?'], responses:['five']
[('7 eleven', 0.1264466744091217), ('babies', 0.124977990347662), ('sunrise', 0.12490143984830117), ('eating', 0.1247676656843781), ('feet', 0.12475702323703439), ('candle', 0.12473210928138137), ('light', 0.12472650705175181), ('floating', 0.12469059014036947)]
[['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many animals are in the image?'], responses:['1']
question: ['How many multi-packs of paper towels are in the image?'], responses:['4']
[('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']]
[('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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.9221e-09, 3.9130e-01, 1.8481e-01, 2.8242e-04, 4.1838e-01, 6.3817e-04,
1.3769e-03, 3.2200e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
feet *************
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([1.9221e-09, 3.9130e-01, 1.8481e-01, 2.8242e-04, 4.1838e-01, 6.3817e-04,
1.3769e-03, 3.2200e-03], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
question: ['Are the animals surrounded by a fence?'], responses:['yes']
ANSWER0=VQA(image=RIGHT,question='How many dung beetles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
[('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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
question: ['How many dung beetles 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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([1.0000e+00, 1.4222e-09, 4.7450e-10, 8.1348e-10, 3.3118e-10, 4.9154e-08,
8.0886e-09, 1.5771e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.4222e-09, 4.7450e-10, 8.1348e-10, 3.3118e-10, 4.9154e-08,
8.0886e-09, 1.5771e-09], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([7.1409e-01, 8.0817e-05, 2.8583e-01, 2.4604e-08, 7.0865e-07, 1.2125e-07,
1.2183e-06, 7.6469e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([7.1409e-01, 8.0817e-05, 2.8583e-01, 2.4604e-08, 7.0865e-07, 1.2125e-07,
1.2183e-06, 7.6469e-08], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 4.7450e-10, 1.1096e-10, 2.9453e-10, 2.1712e-10, 2.8350e-08,
1.5961e-08, 8.5624e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.7450e-10, 1.1096e-10, 2.9453e-10, 2.1712e-10, 2.8350e-08,
1.5961e-08, 8.5624e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.5961e-08, 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>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.2125e-07, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(6.1861e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many ferrets are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many vases are in the image?')