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, 1.1861e-08, 1.4539e-07, 4.2688e-12, 1.6234e-11, 1.1067e-08,
1.5196e-10, 5.6504e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.1861e-08, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.7630e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.3379e-06, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
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])
torch.Size([3, 3, 448, 448])
question: ['Does the image contain a woman wearing an earring?'], 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']]
question: ['How many dogs are in the image?'], responses:['1']
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
[('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
question: ['How many animals are in the image?'], responses:['1']
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
[('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: 1, images per sample: 1.0, dynamic token length: 324
torch.Size([3, 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
tensor([1.0000e+00, 9.7366e-10, 4.6893e-07, 5.3550e-11, 2.2777e-09, 5.2566e-08,
1.7887e-09, 3.3742e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 9.7366e-10, 4.6893e-07, 5.3550e-11, 2.2777e-09, 5.2566e-08,
1.7887e-09, 3.3742e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(9.7366e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
tensor([1.0000e+00, 2.0249e-10, 7.5077e-11, 2.5398e-10, 9.6388e-11, 9.8223e-09,
2.0451e-09, 1.5645e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.0249e-10, 7.5077e-11, 2.5398e-10, 9.6388e-11, 9.8223e-09,
2.0451e-09, 1.5645e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.2652e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.0324e-09, 2.8113e-10, 2.7893e-10, 1.2966e-10, 3.0269e-08,
4.8488e-09, 1.0250e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.0324e-09, 2.8113e-10, 2.7893e-10, 1.2966e-10, 3.0269e-08,
4.8488e-09, 1.0250e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.7864e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
tensor([0.8991, 0.0117, 0.0037, 0.0034, 0.0521, 0.0027, 0.0237, 0.0035],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
40 *************
['40', '39', '42', '41', '45', '38', '47', '32'] tensor([0.8991, 0.0117, 0.0037, 0.0034, 0.0521, 0.0027, 0.0237, 0.0035],
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., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a red sports car in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['Is there a red sports car in the image?'], 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([3, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 4.3884e-10, 1.3853e-06, 5.0012e-11, 8.9708e-10, 1.0389e-07,
4.9763e-09, 1.3916e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.3884e-10, 1.3853e-06, 5.0012e-11, 8.9708e-10, 1.0389e-07,
4.9763e-09, 1.3916e-06], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.3884e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.9802e-06, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:23:25,077] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.27 | optimizer_step: 0.31
[2024-10-24 10:23:25,077] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3174.07 | backward_microstep: 8009.17 | backward_inner_microstep: 3015.27 | backward_allreduce_microstep: 4993.83 | step_microstep: 7.61
[2024-10-24 10:23:25,077] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3174.08 | backward: 8009.16 | backward_inner: 3015.28 | backward_allreduce: 4993.81 | step: 7.62
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4737/4844 [19:42:08<24:41, 13.84s/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 EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is there at least one person standing outside the hut?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many rolls of paper towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Are seats available in the reading area?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there anyone standing outside near the machines?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])