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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.6689e-05, device='cuda:3', grad_fn=<DivBackward0>)}
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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])
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ANSWER0=VQA(image=RIGHT,question='Is a musician holding a guitar 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([3, 3, 448, 448])
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['Is a musician holding a guitar 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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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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question: ['Is there a little girl holding a large dog 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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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: 1865
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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tensor([1.0000e+00, 8.2456e-11, 7.1846e-07, 4.8183e-12, 1.0903e-10, 3.0088e-09,
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2.2977e-10, 6.3137e-07], 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([1.0000e+00, 8.2456e-11, 7.1846e-07, 4.8183e-12, 1.0903e-10, 3.0088e-09,
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2.2977e-10, 6.3137e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(8.2456e-11, 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>)}
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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tensor([1.0000e+00, 3.1363e-10, 4.9237e-11, 1.6269e-10, 1.3592e-10, 1.0455e-08,
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5.7806e-09, 2.5418e-10], 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, 3.1363e-10, 4.9237e-11, 1.6269e-10, 1.3592e-10, 1.0455e-08,
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5.7806e-09, 2.5418e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(5.7806e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
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ANSWER0=VQA(image=RIGHT,question='How many ferrets 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
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tensor([1.0000e+00, 1.8582e-10, 1.1421e-06, 7.8965e-13, 6.8769e-10, 1.5907e-08,
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8.6417e-11, 8.5334e-07], 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([1.0000e+00, 1.8582e-10, 1.1421e-06, 7.8965e-13, 6.8769e-10, 1.5907e-08,
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8.6417e-11, 8.5334e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.8582e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.0266e-06, device='cuda:0', grad_fn=<DivBackward0>)}
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question: ['How many ferrets are in the image?'], responses:['2']
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tensor([8.0910e-01, 2.6228e-08, 1.9090e-01, 4.2165e-08, 1.4260e-10, 4.4320e-08,
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3.3581e-09, 1.6682e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.0910e-01, 2.6228e-08, 1.9090e-01, 4.2165e-08, 1.4260e-10, 4.4320e-08,
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3.3581e-09, 1.6682e-08], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8091, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1909, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4901e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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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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tensor([1.0000e+00, 4.9937e-08, 2.8453e-08, 8.5432e-09, 2.2240e-10, 8.5216e-10,
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3.2679e-10, 1.6892e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 4.9937e-08, 2.8453e-08, 8.5432e-09, 2.2240e-10, 8.5216e-10,
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3.2679e-10, 1.6892e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(8.5432e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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[2024-10-24 10:45:34,933] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.43 | optimizer_gradients: 0.29 | optimizer_step: 0.31
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[2024-10-24 10:45:34,933] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5114.41 | backward_microstep: 12519.89 | backward_inner_microstep: 4838.88 | backward_allreduce_microstep: 7680.92 | step_microstep: 7.69
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[2024-10-24 10:45:34,933] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5114.41 | backward: 12519.89 | backward_inner: 4838.91 | backward_allreduce: 7680.88 | step: 7.70
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100%|ββββββββββ| 4827/4844 [20:04:18<04:18, 15.18s/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 VQA_lavis step
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Registering EVAL step
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Registering RESULT 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 there a person swimming with the animals 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='Is a window shade partially pulled up 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='Is the fragrance bottle a different color than its box?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([3, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='Where are the humans in relation to the cows?')
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ANSWER1=EVAL(expr='{ANSWER0} == "to the right"')
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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([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Is there a person swimming with the animals 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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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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