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ANSWER0=VQA(image=LEFT,question='Is there water in the image?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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tensor([5.4583e-01, 4.5250e-01, 7.9005e-05, 1.2066e-04, 1.8481e-04, 8.2797e-04,
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4.2399e-04, 3.5353e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.4583e-01, 4.5250e-01, 7.9005e-05, 1.2066e-04, 1.8481e-04, 8.2797e-04,
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4.2399e-04, 3.5353e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.4525, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5458, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0017, device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['Is there water in the image?'], responses:['yes']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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question: ['Are the dogs heading to the right?'], responses:['yes']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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tensor([8.9725e-01, 1.2081e-02, 8.8839e-02, 1.0062e-03, 1.0230e-04, 2.4899e-04,
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1.4564e-05, 4.6254e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.9725e-01, 1.2081e-02, 8.8839e-02, 1.0062e-03, 1.0230e-04, 2.4899e-04,
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1.4564e-05, 4.6254e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.8972, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.0888, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0139, device='cuda:0', grad_fn=<SubBackward0>)}
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tensor([5.1551e-01, 2.5256e-02, 4.5493e-01, 1.2894e-03, 1.6311e-04, 1.6927e-03,
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1.3004e-04, 1.0257e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.1551e-01, 2.5256e-02, 4.5493e-01, 1.2894e-03, 1.6311e-04, 1.6927e-03,
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1.3004e-04, 1.0257e-03], device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5155, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4549, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0296, device='cuda:2', grad_fn=<DivBackward0>)}
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[2024-10-22 17:13:11,663] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.32 | optimizer_step: 0.33
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[2024-10-22 17:13:11,664] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6971.26 | backward_microstep: 10487.29 | backward_inner_microstep: 6690.47 | backward_allreduce_microstep: 3796.69 | step_microstep: 7.77
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[2024-10-22 17:13:11,664] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6971.28 | backward: 10487.27 | backward_inner: 6690.52 | backward_allreduce: 3796.66 | step: 7.78
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0%| | 3/4844 [00:55<23:48:06, 17.70s/it]Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=RIGHT,question='How many people are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 4')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([4, 3, 448, 448])
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torch.Size([3, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Does the image contain a human child playing a saxophone?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([5, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='Is a person paddling a canoe diagonally to the left?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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question: ['How many bottles are in the image?'], responses:['3']
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[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
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[['3', '4', '1', '5', '8', '2', '6', '12']]
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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question: ['How many people are in the image?'], responses:['3']
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[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
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[['3', '4', '1', '5', '8', '2', '6', '12']]
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question: ['Does the image contain a human child playing a saxophone?'], responses:['no']
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[('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)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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torch.Size([4, 3, 448, 448]) knan debug pixel values shape
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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tensor([0.3943, 0.2247, 0.0405, 0.1202, 0.0102, 0.1707, 0.0367, 0.0028],
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device='cuda:3', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.3943, 0.2247, 0.0405, 0.1202, 0.0102, 0.1707, 0.0367, 0.0028],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0405, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9595, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is there at least one person in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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question: ['Is a person paddling a canoe diagonally to the left?'], responses:['no']
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torch.Size([13, 3, 448, 448])
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[('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)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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tensor([0.5175, 0.2599, 0.0188, 0.0795, 0.0056, 0.0957, 0.0213, 0.0016],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.5175, 0.2599, 0.0188, 0.0795, 0.0056, 0.0957, 0.0213, 0.0016],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.8920, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.1080, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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