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ANSWER0=VQA(image=RIGHT,question='Is the animal in the image on all fours?')
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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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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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torch.Size([13, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['How many chimpanzees are in the image?'], responses:['5']
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[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
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[['5', '8', '4', '6', '3', '7', '11', '9']]
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torch.Size([3, 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: 3404
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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tensor([0.1864, 0.1281, 0.1182, 0.1864, 0.0644, 0.1625, 0.0576, 0.0965],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.1864, 0.1281, 0.1182, 0.1864, 0.0644, 0.1625, 0.0576, 0.0965],
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device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0644, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9356, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
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question: ['Is the animal in the image on all fours?'], 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: 3405
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([6.6449e-01, 1.8618e-02, 3.1388e-01, 1.6659e-03, 1.5867e-04, 6.0531e-04,
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1.1817e-04, 4.6642e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.6449e-01, 1.8618e-02, 3.1388e-01, 1.6659e-03, 1.5867e-04, 6.0531e-04,
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1.1817e-04, 4.6642e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.6645, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3139, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0216, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Does the left image feature a barn style door made of weathered-look horizontal wood boards?')
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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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question: ['Does the left image feature a barn style door made of weathered-look horizontal wood boards?'], 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: 1871
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1871
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tensor([0.8094, 0.0442, 0.0209, 0.0144, 0.0148, 0.0096, 0.0853, 0.0014],
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device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.8094, 0.0442, 0.0209, 0.0144, 0.0148, 0.0096, 0.0853, 0.0014],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.1906, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8094, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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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: 1871
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1871
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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: 1872
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872
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tensor([7.1226e-01, 2.2419e-02, 2.6203e-01, 1.0223e-03, 2.0444e-04, 7.0565e-04,
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5.7957e-05, 1.2969e-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([7.1226e-01, 2.2419e-02, 2.6203e-01, 1.0223e-03, 2.0444e-04, 7.0565e-04,
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5.7957e-05, 1.2969e-03], device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.7123, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2620, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0257, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([6.0566e-01, 3.9256e-01, 9.9124e-05, 2.0128e-04, 3.0053e-04, 3.0160e-04,
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6.6936e-04, 2.0477e-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([6.0566e-01, 3.9256e-01, 9.9124e-05, 2.0128e-04, 3.0053e-04, 3.0160e-04,
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6.6936e-04, 2.0477e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.3926, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.6057, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0018, device='cuda:0', grad_fn=<DivBackward0>)}
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[2024-10-22 17:13:53,036] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.52 | optimizer_gradients: 0.21 | optimizer_step: 0.31
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[2024-10-22 17:13:53,036] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7065.49 | backward_microstep: 6778.89 | backward_inner_microstep: 6774.08 | backward_allreduce_microstep: 4.71 | step_microstep: 7.53
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[2024-10-22 17:13:53,036] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7065.49 | backward: 6778.88 | backward_inner: 6774.09 | backward_allreduce: 4.69 | step: 7.55
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0%| | 6/4844 [01:36<19:52:48, 14.79s/it]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 an unworn knee pad to the right of a model's legs?')
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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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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=RIGHT,question='Does the sleepwear feature a Disney Princess theme on the front?')
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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([5, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='How many white dogs 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='How many sled dogs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 6')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Does the sleepwear feature a Disney Princess theme on the front?'], responses:['no']
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