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[['light', 'sunlight', 'lights', 'wine', 'water', 'glass', 'lamps', 'dark']]
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tensor([6.8600e-01, 3.0140e-02, 8.1122e-03, 1.9267e-03, 3.4890e-03, 1.4992e-03,
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2.6864e-01, 1.9551e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.8600e-01, 3.0140e-02, 8.1122e-03, 1.9267e-03, 3.4890e-03, 1.4992e-03,
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2.6864e-01, 1.9551e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3140, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6860, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is there a dish visible in the image?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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question: ['Is the case open?'], 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: 13, images per sample: 13.0, dynamic token length: 3395
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['Is there a dish visible in the image?'], 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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tensor([9.4358e-01, 1.4325e-02, 7.4305e-03, 2.9042e-03, 4.2293e-03, 2.9479e-03,
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2.4371e-02, 2.1517e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.4358e-01, 1.4325e-02, 7.4305e-03, 2.9042e-03, 4.2293e-03, 2.9479e-03,
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2.4371e-02, 2.1517e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9436, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0564, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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tensor([6.6288e-01, 2.0694e-02, 3.1312e-01, 1.5553e-03, 2.0547e-04, 5.9828e-04,
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9.6508e-05, 8.5317e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.6288e-01, 2.0694e-02, 3.1312e-01, 1.5553e-03, 2.0547e-04, 5.9828e-04,
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9.6508e-05, 8.5317e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6629, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3131, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0240, device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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tensor([7.4248e-01, 2.5659e-01, 3.8339e-05, 1.4398e-04, 1.7427e-04, 4.3088e-04,
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1.0796e-04, 3.7021e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.4248e-01, 2.5659e-01, 3.8339e-05, 1.4398e-04, 1.7427e-04, 4.3088e-04,
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1.0796e-04, 3.7021e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7425, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2566, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0009, device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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tensor([0.6604, 0.1125, 0.0674, 0.0873, 0.0145, 0.0199, 0.0080, 0.0300],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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light *************
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['light', 'sunlight', 'lights', 'wine', 'water', 'glass', 'lamps', 'dark'] tensor([0.6604, 0.1125, 0.0674, 0.0873, 0.0145, 0.0199, 0.0080, 0.0300],
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device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
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[2024-10-22 17:12:40,589] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.38 | optimizer_step: 0.33
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[2024-10-22 17:12:40,590] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6659.39 | backward_microstep: 5320.76 | backward_inner_microstep: 5315.55 | backward_allreduce_microstep: 4.97 | step_microstep: 12.83
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[2024-10-22 17:12:40,590] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6659.35 | backward: 5320.75 | backward_inner: 5315.72 | backward_allreduce: 4.96 | step: 12.84
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0%| | 1/4844 [00:24<32:18:19, 24.01s/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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ANSWER0=VQA(image=LEFT,question='How many windows are on the left wall?')
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ANSWER1=EVAL(expr='{ANSWER0} == 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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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=LEFT,question='How many boats are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Is there a human holding a dog in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Can you see the customers in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([5, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['Can you see the customers 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([1, 3, 448, 448]) knan debug pixel values shape
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question: ['Is there a human holding a dog 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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tensor([6.9835e-01, 2.3419e-02, 2.7348e-01, 2.6040e-03, 1.8967e-04, 6.1416e-04,
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1.0074e-04, 1.2463e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.9835e-01, 2.3419e-02, 2.7348e-01, 2.6040e-03, 1.8967e-04, 6.1416e-04,
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1.0074e-04, 1.2463e-03], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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{True: tensor(0.6983, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2735, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0282, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is there a woman in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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