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1.6806e-10, 6.8434e-11], 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, 2.1478e-08, 3.7244e-10, 1.0030e-06, 8.5404e-11, 3.7725e-11,
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1.6806e-10, 6.8434e-11], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0252e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([8.9575e-06, 1.3200e-03, 4.2858e-02, 3.6262e-01, 3.9310e-01, 1.8340e-01,
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1.0769e-02, 5.9279e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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striped *************
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['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([8.9575e-06, 1.3200e-03, 4.2858e-02, 3.6262e-01, 3.9310e-01, 1.8340e-01,
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1.0769e-02, 5.9279e-03], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
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[2024-10-24 10:32:43,685] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.46 | optimizer_gradients: 0.28 | optimizer_step: 0.32
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[2024-10-24 10:32:43,685] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5059.77 | backward_microstep: 6166.45 | backward_inner_microstep: 4826.54 | backward_allreduce_microstep: 1339.70 | step_microstep: 7.75
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[2024-10-24 10:32:43,685] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5059.79 | backward: 6166.44 | backward_inner: 4826.63 | backward_allreduce: 1339.68 | step: 7.77
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99%|ββββββββββ| 4775/4844 [19:51:27<15:47, 13.73s/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 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 EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=RIGHT,question='Is there a human head 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=LEFT,question='Is there a pink bag 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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ANSWER0=VQA(image=LEFT,question='How many different shades of lip gloss are there in their tubes?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 8')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='How many tusked animals 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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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Is there a human head in the image?'], 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 pink bag 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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question: ['How many different shades of lip gloss are there in their tubes?'], responses:['2']
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tensor([1.0000e+00, 6.3463e-09, 1.9363e-09, 3.6191e-08, 2.9165e-10, 1.1012e-10,
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2.0054e-10, 5.1616e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.3463e-09, 1.9363e-09, 3.6191e-08, 2.9165e-10, 1.1012e-10,
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2.0054e-10, 5.1616e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.9363e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.9363e-09, device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['How many tusked animals are in the image?'], responses:['1']
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ANSWER0=VQA(image=LEFT,question='How many wolves 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([5, 3, 448, 448])
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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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[('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]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['How many wolves 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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torch.Size([5, 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: 3401
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tensor([1.0000e+00, 1.7630e-09, 8.4906e-07, 7.0807e-11, 4.9434e-10, 1.0713e-07,
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1.3600e-09, 1.6069e-06], 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, 1.7630e-09, 8.4906e-07, 7.0807e-11, 4.9434e-10, 1.0713e-07,
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1.3600e-09, 1.6069e-06], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.7630e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.5034e-06, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Are there windows in the image?')
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ANSWER1=RESULT(var=ANSWER0)
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torch.Size([7, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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tensor([9.9997e-01, 2.6685e-05, 9.7761e-08, 5.2324e-08, 7.8428e-09, 3.1924e-09,
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1.2923e-08, 4.4087e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9997e-01, 2.6685e-05, 9.7761e-08, 5.2324e-08, 7.8428e-09, 3.1924e-09,
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1.2923e-08, 4.4087e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(5.2324e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['Are there windows 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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torch.Size([7, 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: 3401
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
|
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