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TMarcus
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InternVL

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ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(9.3542e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:04:02,046] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.40 | optimizer_gradients: 0.26 | optimizer_step: 0.31
[2024-10-24 10:04:02,047] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5173.27 | backward_microstep: 8657.41 | backward_inner_microstep: 4967.18 | backward_allreduce_microstep: 3690.15 | step_microstep: 7.60
[2024-10-24 10:04:02,047] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5173.29 | backward: 8657.40 | backward_inner: 4967.20 | backward_allreduce: 3690.14 | step: 7.61
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4659/4844 [19:22:45<45:20, 14.70s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many rolls of paper towel are in the package?')
ANSWER1=EVAL(expr='{ANSWER0} == 6')
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='Is a person wearing a safety pin in their ear?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image have a row of three drawers?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is the entire dog visible in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does the image have a row of three drawers?'], 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([1, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.2117e-09, 2.8887e-07, 6.1276e-11, 2.4070e-11, 8.5097e-09,
5.2981e-10, 2.4066e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.2117e-09, 2.8887e-07, 6.1276e-11, 2.4070e-11, 8.5097e-09,
5.2981e-10, 2.4066e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.2117e-09, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:3', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many rolls of paper towel are in the package?'], responses:['6']
question: ['Is a person wearing a safety pin in their ear?'], responses:['no']
question: ['Is the entire dog visible 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']]
[('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']]
[('6', 0.12794147189263105), ('8', 0.12539492259598553), ('12', 0.12539359088927945), ('5', 0.12471292164321114), ('4', 0.12443617393590153), ('1', 0.12417386497855347), ('11', 0.12398049124372558), ('3', 0.12396656282071232)]
[['6', '8', '12', '5', '4', '1', '11', '3']]
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: 3399
question: ['How many dogs are in the image?'], responses:['2']
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
torch.Size([13, 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: 3399
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: 3399
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: 3399
tensor([1.0000e+00, 1.1496e-08, 5.7149e-07, 5.1893e-12, 6.5647e-11, 9.0472e-09,
6.5860e-09, 3.7459e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.1496e-08, 5.7149e-07, 5.1893e-12, 6.5647e-11, 9.0472e-09,
6.5860e-09, 3.7459e-07], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.5912e-02, 9.8409e-01, 3.5575e-08, 2.3994e-11, 1.3873e-12, 2.0493e-09,
4.4562e-11, 1.1014e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.5912e-02, 9.8409e-01, 3.5575e-08, 2.3994e-11, 1.3873e-12, 2.0493e-09,
4.4562e-11, 1.1014e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.1496e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are there shelves in the image?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9841, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0159, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.0291e-08, device='cuda:2', grad_fn=<DivBackward0>)}
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many apes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([9.9998e-01, 1.7787e-05, 4.2248e-06, 5.3627e-07, 8.3663e-08, 3.0421e-09,
3.6915e-07, 1.0070e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
6 *************
['6', '8', '12', '5', '4', '1', '11', '3'] tensor([9.9998e-01, 1.7787e-05, 4.2248e-06, 5.3627e-07, 8.3663e-08, 3.0421e-09,
3.6915e-07, 1.0070e-08], device='cuda:0', grad_fn=<SelectBackward0>)
torch.Size([13, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.3014e-05, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the dog wearing a collar?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 3.1742e-08, 4.5729e-09, 8.4946e-08, 1.8294e-10, 1.2999e-09,
6.7704e-10, 1.4468e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)