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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([1.0000e+00, 1.4450e-07, 2.1266e-09, 1.0677e-06, 1.2952e-10, 3.3125e-10,
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6.2861e-10, 2.7603e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.4450e-07, 2.1266e-09, 1.0677e-06, 1.2952e-10, 3.3125e-10,
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6.2861e-10, 2.7603e-11], device='cuda:3', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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torch.Size([7, 3, 448, 448])
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tensor([9.9982e-01, 1.6081e-04, 1.5422e-05, 1.1583e-10, 1.8471e-09, 4.6434e-07,
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4.2295e-08, 6.5713e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9982e-01, 1.6081e-04, 1.5422e-05, 1.1583e-10, 1.8471e-09, 4.6434e-07,
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4.2295e-08, 6.5713e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many boars are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.2295e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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torch.Size([7, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='How many white birds 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([7, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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question: ['Is there a female 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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question: ['How many boars are in the image?'], responses:['3']
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question: ['How many white birds are in the image?'], responses:['0']
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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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[('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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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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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: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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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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tensor([1.0000e+00, 2.2762e-07, 5.7364e-10, 3.1007e-08, 2.3579e-08, 1.4056e-09,
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2.6712e-10, 3.1128e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.2762e-07, 5.7364e-10, 3.1007e-08, 2.3579e-08, 1.4056e-09,
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2.6712e-10, 3.1128e-08], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(5.7364e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3784e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 2.4461e-09, 1.2186e-10, 3.9237e-09, 3.7533e-10, 5.2412e-11,
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2.1964e-11, 1.2592e-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, 2.4461e-09, 1.2186e-10, 3.9237e-09, 3.7533e-10, 5.2412e-11,
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2.1964e-11, 1.2592e-08], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.2186e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.2186e-10, device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 2.6415e-06, 2.5313e-08, 1.9661e-09, 2.0197e-11, 5.9275e-08,
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5.8400e-11, 1.2295e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 2.6415e-06, 2.5313e-08, 1.9661e-09, 2.0197e-11, 5.9275e-08,
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5.8400e-11, 1.2295e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.7294e-06, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 4.2365e-07, 7.9228e-10, 3.5905e-12, 7.0255e-08, 2.2448e-10,
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5.1815e-08, 2.1828e-08], 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, 4.2365e-07, 7.9228e-10, 3.5905e-12, 7.0255e-08, 2.2448e-10,
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5.1815e-08, 2.1828e-08], 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(5.9605e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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[2024-10-24 10:43:52,116] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.22 | optimizer_step: 0.31
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[2024-10-24 10:43:52,116] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7076.38 | backward_microstep: 6895.25 | backward_inner_microstep: 6775.37 | backward_allreduce_microstep: 119.79 | step_microstep: 7.51
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[2024-10-24 10:43:52,116] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7076.39 | backward: 6895.24 | backward_inner: 6775.39 | backward_allreduce: 119.78 | step: 7.52
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100%|ββββββββββ| 4820/4844 [20:02:35<06:10, 15.45s/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=LEFT,question='Is there at least one orange cap visible in the image?')
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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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ANSWER0=VQA(image=RIGHT,question='How many chimpanzees 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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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='How many dogs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 3')
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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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ANSWER0=VQA(image=RIGHT,question='Is the pair of gloves mostly red in color?')
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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([13, 3, 448, 448])
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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 the pair of gloves mostly red in color?'], 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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