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ANSWER1=EVAL(expr='{ANSWER0} >= 6')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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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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[('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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question: ['How many syringes are in the image?'], responses:['1']
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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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: 1862
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torch.Size([1, 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: 1863
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question: ['How many vending machines 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([5, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 1.1562e-09, 3.5265e-10, 8.5890e-10, 5.7979e-10, 1.1913e-07,
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7.8094e-09, 4.9311e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.1562e-09, 3.5265e-10, 8.5890e-10, 5.7979e-10, 1.1913e-07,
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7.8094e-09, 4.9311e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.2550e-07, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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tensor([1.0000e+00, 3.4623e-08, 1.2092e-10, 2.8999e-08, 3.4102e-10, 2.3859e-10,
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1.3770e-09, 5.3419e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.4623e-08, 1.2092e-10, 2.8999e-08, 3.4102e-10, 2.3859e-10,
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1.3770e-09, 5.3419e-08], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 6.7583e-09, 3.2612e-10, 6.4857e-10, 3.8728e-10, 2.0176e-08,
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4.9937e-08, 1.2281e-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, 6.7583e-09, 3.2612e-10, 6.4857e-10, 3.8728e-10, 2.0176e-08,
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4.9937e-08, 1.2281e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(7.8357e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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tensor([9.9999e-01, 9.5782e-07, 5.2405e-07, 4.9165e-09, 4.1980e-06, 2.3301e-08,
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4.4175e-07, 3.3220e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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0 *************
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['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9999e-01, 9.5782e-07, 5.2405e-07, 4.9165e-09, 4.1980e-06, 2.3301e-08,
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4.4175e-07, 3.3220e-06], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.2092e-10, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1909e-07, device='cuda:1', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is there a collar in the image?')
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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:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.4175e-06, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Does the elephant in the image have tusks?')
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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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question: ['Is there a collar 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: ['Does the elephant in the image have tusks?'], 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([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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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: 3398
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tensor([1.0000e+00, 1.1383e-09, 4.6161e-07, 1.7908e-09, 4.7749e-09, 7.2807e-07,
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4.3289e-09, 3.4113e-07], 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, 1.1383e-09, 4.6161e-07, 1.7908e-09, 4.7749e-09, 7.2807e-07,
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4.3289e-09, 3.4113e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.1383e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.5497e-06, device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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tensor([1.0000e+00, 1.3515e-08, 4.6074e-11, 4.6011e-08, 1.7639e-10, 1.7088e-09,
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5.5314e-11, 1.9741e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.3515e-08, 4.6074e-11, 4.6011e-08, 1.7639e-10, 1.7088e-09,
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5.5314e-11, 1.9741e-08], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(4.6074e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1916e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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[2024-10-24 10:41:00,283] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.47 | optimizer_gradients: 0.27 | optimizer_step: 0.32
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[2024-10-24 10:41:00,283] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7076.87 | backward_microstep: 6785.64 | backward_inner_microstep: 6779.33 | backward_allreduce_microstep: 6.19 | step_microstep: 10.20
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[2024-10-24 10:41:00,283] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7076.87 | backward: 6785.63 | backward_inner: 6779.36 | backward_allreduce: 6.18 | step: 10.21
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99%|ββββββββββ| 4809/4844 [19:59:44<08:02, 13.78s/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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ANSWER0=VQA(image=LEFT,question='How many balloons are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 4')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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Registering EVAL step
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