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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.5888e-01, 2.6279e-02, 3.1123e-01, 1.7001e-03, 1.4205e-04, 7.2244e-04,
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2.4622e-04, 8.0172e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6589, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.3112, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0299, device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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ANSWER0=VQA(image=RIGHT,question='Are all pizzas in boxes?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([11, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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tensor([3.0321e-01, 2.6758e-01, 1.4322e-01, 1.6229e-01, 8.6870e-02, 1.4631e-02,
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2.1964e-02, 2.3281e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([3.0321e-01, 2.6758e-01, 1.4322e-01, 1.6229e-01, 8.6870e-02, 1.4631e-02,
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2.1964e-02, 2.3281e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([0.4019, 0.1898, 0.0374, 0.0955, 0.0138, 0.2151, 0.0424, 0.0042],
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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([0.4019, 0.1898, 0.0374, 0.0955, 0.0138, 0.2151, 0.0424, 0.0042],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2676, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.7324, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2151, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7849, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Are all animals in the image have horns?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=LEFT,question='How many velcro closures 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([1, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many velcro closures are in the image?'], responses:['4']
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[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
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[['4', '5', '3', '8', '6', '1', '2', '11']]
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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question: ['Are all pizzas in boxes?'], 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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tensor([0.1978, 0.1359, 0.1745, 0.0878, 0.1359, 0.0775, 0.1540, 0.0366],
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device='cuda:3', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.1978, 0.1359, 0.1745, 0.0878, 0.1359, 0.0775, 0.1540, 0.0366],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1540, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8460, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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torch.Size([11, 3, 448, 448]) knan debug pixel values shape
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question: ['Are all animals in the image have horns?'], 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([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: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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tensor([6.3628e-01, 3.6254e-01, 3.0155e-05, 1.1341e-04, 1.7093e-04, 4.7418e-04,
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3.5755e-04, 3.0529e-05], 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([6.3628e-01, 3.6254e-01, 3.0155e-05, 1.1341e-04, 1.7093e-04, 4.7418e-04,
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3.5755e-04, 3.0529e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3625, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6363, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0012, device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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tensor([6.0645e-01, 3.9156e-01, 1.4439e-04, 1.4481e-04, 2.8604e-04, 7.0398e-04,
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3.8962e-04, 3.1951e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.0645e-01, 3.9156e-01, 1.4439e-04, 1.4481e-04, 2.8604e-04, 7.0398e-04,
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3.8962e-04, 3.1951e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3916, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.6065, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0020, device='cuda:0', grad_fn=<DivBackward0>)}
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[2024-10-22 17:17:47,831] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.67 | optimizer_gradients: 0.21 | optimizer_step: 0.31
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[2024-10-22 17:17:47,832] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6957.50 | backward_microstep: 6681.73 | backward_inner_microstep: 6676.46 | backward_allreduce_microstep: 5.17 | step_microstep: 181.67
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[2024-10-22 17:17:47,832] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6957.52 | backward: 6681.72 | backward_inner: 6676.51 | backward_allreduce: 5.14 | step: 181.68
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0%| | 2/4844 [00:38<24:24:40, 18.15s/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=LEFT,question='Does the image appear to feature an open air shop?')
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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='How many chairs 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=LEFT,question='Is the dog swimming in a pool?')
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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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ANSWER0=VQA(image=RIGHT,question='Does the image show a "Whataburger" cup sitting on a surface?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([3, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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