Datasets:
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InternVL

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2.1941e-09, 4.2718e-11], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(4.1241e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many bulldogs are in the image?'], responses:['3']
[('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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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: 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: 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: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([1.0000e+00, 7.7363e-10, 4.9937e-08, 3.8946e-10, 1.8494e-12, 1.3328e-12,
9.8404e-13, 2.5573e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.7363e-10, 4.9937e-08, 3.8946e-10, 1.8494e-12, 1.3328e-12,
9.8404e-13, 2.5573e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(4.9937e-08, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-4.9937e-08, device='cuda:1', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 1.6832e-07, 1.0351e-06, 1.0652e-09, 1.6140e-10, 8.6484e-08,
3.5855e-10, 4.8078e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 1.6832e-07, 1.0351e-06, 1.0652e-09, 1.6140e-10, 8.6484e-08,
3.5855e-10, 4.8078e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.3396e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 10:23:13,872] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.41 | optimizer_gradients: 0.21 | optimizer_step: 0.31
[2024-10-24 10:23:13,872] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7158.75 | backward_microstep: 6837.04 | backward_inner_microstep: 6831.85 | backward_allreduce_microstep: 5.11 | step_microstep: 7.37
[2024-10-24 10:23:13,872] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7158.76 | backward: 6837.03 | backward_inner: 6831.86 | backward_allreduce: 5.10 | step: 7.38
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4736/4844 [19:41:57<26:57, 14.98s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT 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=RIGHT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dung beetles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there any animal lying on the ground in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Does the animal in the image have a white coat?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many people are in the image?'], responses:['0']
question: ['Is there any animal lying on the ground in the image?'], responses:['no']
question: ['Does the animal in the image have a white coat?'], responses:['no']
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
[('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']]
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: 1864
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
question: ['How many dung beetles are in the image?'], responses:['40']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
[('40', 0.12638022987124733), ('39', 0.12509919407251455), ('42', 0.12494223232783619), ('41', 0.12482626048065008), ('45', 0.12479694604159434), ('38', 0.12473125094691345), ('47', 0.1246423477331973), ('32', 0.1245815385260468)]
[['40', '39', '42', '41', '45', '38', '47', '32']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
tensor([9.9997e-01, 1.1063e-05, 3.8354e-07, 1.0043e-08, 8.7291e-06, 1.1651e-07,
2.7939e-06, 2.2947e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9997e-01, 1.1063e-05, 3.8354e-07, 1.0043e-08, 8.7291e-06, 1.1651e-07,
2.7939e-06, 2.2947e-06], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.5392e-05, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the image contain a woman wearing an earring?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
tensor([1.0000e+00, 1.7630e-09, 1.6187e-06, 1.8801e-10, 1.5106e-09, 1.2684e-07,
8.9932e-10, 1.5057e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.7630e-09, 1.6187e-06, 1.8801e-10, 1.5106e-09, 1.2684e-07,
8.9932e-10, 1.5057e-06], device='cuda:0', grad_fn=<SelectBackward0>)
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=<SoftmaxBackward0>)
no *************