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[2024-10-22 17:22:58,709] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.37 | optimizer_gradients: 0.32 | optimizer_step: 0.33
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[2024-10-22 17:22:58,710] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9276.14 | backward_microstep: 14640.41 | backward_inner_microstep: 8506.43 | backward_allreduce_microstep: 6133.91 | step_microstep: 7.91
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[2024-10-22 17:22:58,710] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9276.17 | backward: 14640.40 | backward_inner: 8506.45 | backward_allreduce: 6133.90 | step: 7.92
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0%| | 11/2424 [04:30<16:10:12, 24.12s/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='Is there at least one person standing in front of the open door to the bus?')
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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='Does any animal in the image on the right have its mouth open?')
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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='Are all the sled dogs running towards the left?')
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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 elephant in the right image walking towards the right?')
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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([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['Is there at least one person standing in front of the open door to the bus?'], responses:['yes']
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question: ['Does any animal in the image on the right have its mouth open?'], responses:['no']
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question: ['Are all the sled dogs running towards the left?'], responses:['yes']
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question: ['Is the elephant in the right image walking towards the right?'], 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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[('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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[('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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[('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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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: 1869
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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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: 1872
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1870
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1870
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1870
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tensor([5.3935e-01, 4.5913e-01, 4.3167e-05, 1.6053e-04, 3.7935e-04, 7.6080e-04,
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1.5869e-04, 1.9878e-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([5.3935e-01, 4.5913e-01, 4.3167e-05, 1.6053e-04, 3.7935e-04, 7.6080e-04,
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1.5869e-04, 1.9878e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([6.0233e-01, 3.9644e-01, 3.3588e-05, 1.7480e-04, 1.4801e-04, 4.7210e-04,
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3.5974e-04, 4.3604e-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([6.0233e-01, 3.9644e-01, 3.3588e-05, 1.7480e-04, 1.4801e-04, 4.7210e-04,
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3.5974e-04, 4.3604e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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tensor([5.0083e-01, 2.5136e-02, 4.7049e-01, 1.2061e-03, 1.7115e-04, 8.5298e-04,
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6.7033e-05, 1.2443e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.4363e-01, 1.9586e-02, 4.3388e-01, 1.0655e-03, 1.5699e-04, 7.2008e-04,
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2.4159e-04, 7.2356e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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tensor([5.0083e-01, 2.5136e-02, 4.7049e-01, 1.2061e-03, 1.7115e-04, 8.5298e-04,
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6.7033e-05, 1.2443e-03], device='cuda:3', grad_fn=<SelectBackward0>)
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.4363e-01, 1.9586e-02, 4.3388e-01, 1.0655e-03, 1.5699e-04, 7.2008e-04,
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2.4159e-04, 7.2356e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.3964, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6023, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0012, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the bird facing towards the left?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([3, 3, 448, 448])
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.4591, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5394, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0015, device='cuda:1', grad_fn=<DivBackward0>)}
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5008, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4705, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0287, device='cuda:3', grad_fn=<DivBackward0>)}
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ๆๅ็ๆฆ็ๅๅธไธบ: ANSWER0=VQA(image=RIGHT,question='How many elephants 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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{True: tensor(0.5436, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.4339, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0225, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is there a silver fork near the food 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='Are there white inflated sails 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([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['Is the bird facing towards the left?'], 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([3, 3, 448, 448]) knan debug pixel values shape
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question: ['Is there a silver fork near the food in the image?'], responses:['yes']
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