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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([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: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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question: ['How many people are on the television?'], responses:['0']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
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[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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torch.Size([13, 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: 1861
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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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tensor([1.0000e+00, 1.3517e-09, 2.0294e-10, 1.6303e-10, 8.3428e-11, 1.2238e-08,
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3.0288e-08, 8.2128e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.3517e-09, 2.0294e-10, 1.6303e-10, 8.3428e-11, 1.2238e-08,
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3.0288e-08, 8.2128e-11], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.2566e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 2.4020e-07, 1.4702e-08, 1.9204e-10, 2.3320e-07, 1.0380e-08,
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1.8483e-07, 4.2030e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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0 *************
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['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 2.4020e-07, 1.4702e-08, 1.9204e-10, 2.3320e-07, 1.0380e-08,
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1.8483e-07, 4.2030e-06], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.8876e-06, device='cuda:1', grad_fn=<DivBackward0>)}
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[2024-10-24 10:34:04,611] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.44 | optimizer_gradients: 0.24 | optimizer_step: 0.30
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[2024-10-24 10:34:04,612] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7083.15 | backward_microstep: 10670.00 | backward_inner_microstep: 6770.02 | backward_allreduce_microstep: 3899.88 | step_microstep: 7.52
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[2024-10-24 10:34:04,612] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7083.15 | backward: 10669.99 | backward_inner: 6770.06 | backward_allreduce: 3899.86 | step: 7.53
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99%|ββββββββββ| 4780/4844 [19:52:48<17:17, 16.22s/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 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='How many upside crabs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 8')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=LEFT,question='How many assembled flutes 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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ANSWER0=VQA(image=RIGHT,question='How many water buffaloes 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([1, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Does the bottle of soap have a top pump dispenser?')
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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([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many assembled flutes are in the image?'], responses:['1']
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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([1, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 4.7823e-10, 4.7823e-10, 3.5538e-10, 3.3125e-10, 3.6739e-08,
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1.1496e-08, 9.4722e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.7823e-10, 4.7823e-10, 3.5538e-10, 3.3125e-10, 3.6739e-08,
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1.1496e-08, 9.4722e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(5.0826e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Does the laptop on the right image have a black background?')
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ANSWER1=RESULT(var=ANSWER0)
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question: ['How many water buffaloes are in the image?'], responses:['3']
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question: ['Does the bottle of soap have a top pump dispenser?'], responses:['no']
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torch.Size([13, 3, 448, 448])
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[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
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[['3', '4', '1', '5', '8', '2', '6', '12']]
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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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question: ['How many upside crabs are in the image?'], responses:['50']
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[('50', 0.12746329354121594), ('51', 0.12494443111915052), ('60', 0.12471995183640609), ('55', 0.12470016949940634), ('54', 0.12460076157014638), ('52', 0.12454269500997545), ('44', 0.12453681395238846), ('48', 0.1244918834713108)]
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[['50', '51', '60', '55', '54', '52', '44', '48']]
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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: 3398
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question: ['Does the laptop on the right image have a black background?'], responses:['yes']
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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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([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: 3398
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tensor([9.9994e-01, 4.5397e-05, 1.1206e-09, 5.8379e-08, 1.7523e-10, 1.4738e-05,
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2.1599e-09, 4.6712e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9994e-01, 4.5397e-05, 1.1206e-09, 5.8379e-08, 1.7523e-10, 1.4738e-05,
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2.1599e-09, 4.6712e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 7.7344e-08, 2.1187e-07, 1.1270e-10, 4.4933e-12, 2.0110e-09,
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1.6894e-10, 5.8471e-08], 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([1.0000e+00, 7.7344e-08, 2.1187e-07, 1.1270e-10, 4.4933e-12, 2.0110e-09,
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