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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.5455e-10, 6.2241e-11, 9.0556e-11, 7.4493e-11, 6.4459e-09,
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4.8401e-08, 1.3114e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.5660e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([9.9108e-01, 1.0902e-03, 5.7859e-03, 3.8106e-06, 9.2327e-05, 4.6408e-05,
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1.4898e-03, 4.1374e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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black *************
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['black', 'white', 'dark', 'purple', 'orange', 'red', 'maroon', 'blue'] tensor([9.9108e-01, 1.0902e-03, 5.7859e-03, 3.8106e-06, 9.2327e-05, 4.6408e-05,
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1.4898e-03, 4.1374e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
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[2024-10-24 10:30:59,889] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.33 | optimizer_step: 0.32
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[2024-10-24 10:30:59,889] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5045.94 | backward_microstep: 12752.98 | backward_inner_microstep: 4826.15 | backward_allreduce_microstep: 7926.76 | step_microstep: 7.53
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[2024-10-24 10:30:59,889] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5045.95 | backward: 12752.97 | backward_inner: 4826.17 | backward_allreduce: 7926.75 | step: 7.55
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98%|ββββββββββ| 4768/4844 [19:49:43<19:57, 15.75s/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 the woman lifting a green bottle to her mouth?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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ANSWER0=VQA(image=RIGHT,question='How many snow plows 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='Is the laptop in the image opening in several positions?')
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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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ANSWER0=VQA(image=RIGHT,question='Does the image show a "Whataburger" cup?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([5, 3, 448, 448])
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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 the laptop in the image opening in several positions?'], 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([5, 3, 448, 448]) knan debug pixel values shape
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question: ['Is the woman lifting a green bottle to her mouth?'], responses:['yes']
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question: ['How many snow plows are in the image?'], responses:['3']
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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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[('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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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: 1863
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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: 1866
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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question: ['Does the image show a "Whataburger" cup?'], 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: 7, images per sample: 7.0, dynamic token length: 1864
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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: 1863
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tensor([1.0000e+00, 1.0800e-08, 1.0479e-07, 6.8352e-11, 3.1555e-11, 1.5293e-09,
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2.1504e-09, 1.0463e-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, 1.0800e-08, 1.0479e-07, 6.8352e-11, 3.1555e-11, 1.5293e-09,
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2.1504e-09, 1.0463e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0800e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is there a ball in the image?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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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, 9.4581e-09, 2.3126e-10, 2.3030e-08, 1.1086e-10, 1.3176e-10,
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4.3234e-11, 4.6309e-09], 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, 9.4581e-09, 2.3126e-10, 2.3030e-08, 1.1086e-10, 1.3176e-10,
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4.3234e-11, 4.6309e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(2.3126e-10, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-2.3126e-10, device='cuda:0', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Does the left image show a pile of forward-facing reddish-orange shell-side up crabs?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([9.9998e-01, 1.4739e-05, 5.9640e-09, 4.2957e-09, 2.3999e-10, 4.7850e-06,
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4.2291e-09, 3.3455e-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([9.9998e-01, 1.4739e-05, 5.9640e-09, 4.2957e-09, 2.3999e-10, 4.7850e-06,
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4.2291e-09, 3.3455e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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torch.Size([7, 3, 448, 448])
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(5.9640e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the dog running?')
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ANSWER1=EVAL(expr='not {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: ['Is there a ball in the image?'], responses:['no']
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question: ['Does the left image show a pile of forward-facing reddish-orange shell-side up crabs?'], responses:['yes']
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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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[('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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