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[['4', '5', '3', '8', '6', '1', '2', '11']]
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
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tensor([1.0000e+00, 2.1748e-09, 6.4852e-10, 1.8537e-09, 1.4615e-09, 9.7730e-08,
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8.6281e-08, 1.9208e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.1748e-09, 6.4852e-10, 1.8537e-09, 1.4615e-09, 9.7730e-08,
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8.6281e-08, 1.9208e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.9207e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
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tensor([7.7686e-01, 2.6499e-07, 2.2258e-01, 3.5924e-04, 1.2375e-05, 1.8064e-04,
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2.8299e-06, 5.4914e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([7.7686e-01, 2.6499e-07, 2.2258e-01, 3.5924e-04, 1.2375e-05, 1.8064e-04,
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2.8299e-06, 5.4914e-06], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many puppies 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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torch.Size([13, 3, 448, 448])
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tensor([1.0000e+00, 3.4663e-07, 4.9856e-08, 2.4514e-12, 1.4012e-12, 1.7590e-10,
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4.5074e-11, 8.3788e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.4663e-07, 4.9856e-08, 2.4514e-12, 1.4012e-12, 1.7590e-10,
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4.5074e-11, 8.3788e-08], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.4663e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the laptop in the image turned at an angle?')
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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 puppies are in the image?'], responses:['2']
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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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: 3396
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question: ['Is the laptop in the image turned at an angle?'], responses:['yes']
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tensor([9.9749e-01, 2.5108e-03, 1.2752e-06, 1.2527e-10, 9.4068e-09, 4.9046e-07,
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4.2097e-08, 2.3774e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9749e-01, 2.5108e-03, 1.2752e-06, 1.2527e-10, 9.4068e-09, 4.9046e-07,
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4.2097e-08, 2.3774e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9975, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0025, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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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([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: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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tensor([1.0000e+00, 4.6912e-08, 2.1855e-09, 1.2825e-08, 2.0730e-10, 3.7241e-10,
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3.4172e-10, 2.2933e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 4.6912e-08, 2.1855e-09, 1.2825e-08, 2.0730e-10, 3.7241e-10,
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3.4172e-10, 2.2933e-10], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(6.3073e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 9.4174e-09, 3.6899e-07, 6.0177e-09, 9.0559e-11, 9.7362e-10,
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6.9799e-11, 2.0873e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.4174e-09, 3.6899e-07, 6.0177e-09, 9.0559e-11, 9.7362e-10,
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6.9799e-11, 2.0873e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.6899e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1361e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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[2024-10-24 10:38:18,840] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.41 | optimizer_gradients: 0.27 | optimizer_step: 0.32
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[2024-10-24 10:38:18,841] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8379.41 | backward_microstep: 9393.10 | backward_inner_microstep: 8138.49 | backward_allreduce_microstep: 1254.48 | step_microstep: 7.70
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[2024-10-24 10:38:18,841] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8379.43 | backward: 9393.09 | backward_inner: 8138.53 | backward_allreduce: 1254.42 | step: 7.71
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99%|ββββββββββ| 4797/4844 [19:57:02<11:34, 14.78s/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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ANSWER0=VQA(image=LEFT,question='Is the dog looking toward the camera?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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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='Which direction is the dog facing?')
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ANSWER1=EVAL(expr='{ANSWER0} == "left"')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=LEFT,question='Is the dog looking at the camera?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Is the computer angled so that the screen isn't visible?')
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ANSWER1=EVAL(expr='{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([3, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Is the computer angled so that the screen isn'], 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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