Datasets:
TMarcus
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InternVL

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2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9984e-01, 4.1337e-05, 2.9916e-07, 1.1868e-04, 1.1605e-08, 1.4435e-09,
6.8772e-09, 1.4240e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9998, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0002, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
tensor([9.8525e-01, 2.9461e-03, 3.3392e-03, 1.1070e-03, 4.7526e-03, 9.4372e-08,
2.6002e-03, 1.3155e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yellow *************
['yellow', 'red', 'green', 'maroon', 'pink', 'mask', 'orange', 'color'] tensor([9.8525e-01, 2.9461e-03, 3.3392e-03, 1.1070e-03, 4.7526e-03, 9.4372e-08,
2.6002e-03, 1.3155e-06], device='cuda:0', grad_fn=<SelectBackward0>)
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>)}
tensor([9.7597e-01, 4.5011e-03, 1.3062e-02, 4.0586e-09, 5.7961e-03, 6.4941e-04,
2.4509e-06, 1.5313e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.7597e-01, 4.5011e-03, 1.3062e-02, 4.0586e-09, 5.7961e-03, 6.4941e-04,
2.4509e-06, 1.5313e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9760, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0240, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many bowls are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many animals are in the picture?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many animals are in the picture?'], responses:['1']
question: ['How many bowls are in the image?'], responses:['δΈ‰']
[('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']]
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([1.0000e+00, 1.0364e-09, 1.9935e-10, 2.4234e-10, 9.2659e-11, 9.2328e-09,
6.8647e-09, 5.6911e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.0364e-09, 1.9935e-10, 2.4234e-10, 9.2659e-11, 9.2328e-09,
6.8647e-09, 5.6911e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: tensor([2.1379e-05, 1.5936e-03, 1.2033e-01, 5.2702e-01, 1.0078e-01, 1.9865e-01,
4.4234e-02, 7.3688e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([2.1379e-05, 1.5936e-03, 1.2033e-01, 5.2702e-01, 1.0078e-01, 1.9865e-01,
4.4234e-02, 7.3688e-03], device='cuda:0', grad_fn=<SelectBackward0>)
{True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.8237e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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>)}
tensor([1.0000e+00, 1.6019e-10, 6.6256e-11, 1.6144e-10, 7.7455e-11, 1.3862e-08,
1.4445e-09, 1.3693e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.6019e-10, 6.6256e-11, 1.6144e-10, 7.7455e-11, 1.3862e-08,
1.4445e-09, 1.3693e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.5909e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:21:42,030] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-24 10:21:42,030] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7056.78 | backward_microstep: 6807.13 | backward_inner_microstep: 6659.34 | backward_allreduce_microstep: 147.66 | step_microstep: 7.69
[2024-10-24 10:21:42,031] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7056.80 | backward: 6807.12 | backward_inner: 6659.37 | backward_allreduce: 147.59 | step: 7.70
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4730/4844 [19:40:25<25:46, 13.57s/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='Can you see the customers in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there at least one person sitting in a canoe?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is a person holding the lemon?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([5, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many birds are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')