Datasets:
TMarcus
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InternVL

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8.3153e-07, 9.2572e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.3481e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.9998e-01, 1.8586e-09, 2.4300e-05, 2.2610e-10, 1.2957e-11, 8.7772e-11,
1.6934e-11, 2.0597e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9998e-01, 1.8586e-09, 2.4300e-05, 2.2610e-10, 1.2957e-11, 8.7772e-11,
1.6934e-11, 2.0597e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.4300e-05, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.8455e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Was the image taken from downstairs?')
FINAL_ANSWER=RESULT(var=ANSWER0)
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3406
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
tensor([9.4609e-01, 8.5612e-09, 5.3912e-02, 2.9657e-09, 2.4060e-11, 3.6850e-11,
7.8384e-11, 1.7430e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.4609e-01, 8.5612e-09, 5.3912e-02, 2.9657e-09, 2.4060e-11, 3.6850e-11,
7.8384e-11, 1.7430e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9461, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.0539, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.4901e-08, device='cuda:3', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the dog standing on all fours?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
question: ['Is the dog standing on all fours?'], 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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
tensor([1.0000e+00, 5.5475e-10, 4.6971e-07, 2.9401e-11, 5.5338e-12, 2.0621e-08,
3.5130e-10, 8.4253e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 5.5475e-10, 4.6971e-07, 2.9401e-11, 5.5338e-12, 2.0621e-08,
3.5130e-10, 8.4253e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.5475e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:3', grad_fn=<DivBackward0>)}
question: ['Was the image taken from downstairs?'], responses:['yes']
[('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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
tensor([1.0000e+00, 1.0946e-08, 2.0408e-10, 8.3876e-09, 3.4897e-11, 4.5537e-11,
3.5835e-11, 2.3419e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.0946e-08, 2.0408e-10, 8.3876e-09, 3.4897e-11, 4.5537e-11,
3.5835e-11, 2.3419e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.0408e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.0408e-10, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Does the image show a young puppy?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
question: ['Does the image show a young puppy?'], responses:['yes']
[('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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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: 1860
tensor([1.0000e+00, 8.9705e-09, 2.7306e-09, 2.3929e-09, 2.7191e-11, 6.0928e-10,
2.9712e-11, 1.3825e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.9705e-09, 2.7306e-09, 2.3929e-09, 2.7191e-11, 6.0928e-10,
2.9712e-11, 1.3825e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(2.7306e-09, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-2.7306e-09, device='cuda:1', grad_fn=<SubBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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
tensor([1.0000e+00, 5.3444e-09, 4.4656e-11, 1.5273e-08, 4.2997e-10, 2.1724e-10,
4.1156e-11, 1.0016e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.3444e-09, 4.4656e-11, 1.5273e-08, 4.2997e-10, 2.1724e-10,
4.1156e-11, 1.0016e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(4.4656e-11, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-4.4656e-11, device='cuda:0', grad_fn=<SubBackward0>)}
[2024-10-24 09:57:10,434] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.25 | optimizer_step: 0.31
[2024-10-24 09:57:10,434] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7035.03 | backward_microstep: 6772.26 | backward_inner_microstep: 6766.01 | backward_allreduce_microstep: 6.08 | step_microstep: 7.22
[2024-10-24 09:57:10,434] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7035.05 | backward: 6772.25 | backward_inner: 6766.05 | backward_allreduce: 6.02 | step: 7.23
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4633/4844 [19:15:54<50:11, 14.27s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are there sea mammals in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a human standing near the dogs?')
ANSWER1=EVAL(expr='{ANSWER0}')
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)