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ANSWER0=VQA(image=RIGHT,question='Are all animals in the image have horns?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.85 GiB. GPU 0 has a total capacty of 44.34 GiB of which 5.17 GiB is free. Including non-PyTorch memory, this process has 39.16 GiB memory in use. Of the allocated memory 36.20 GiB is allocated by PyTorch, and 2.35 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
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Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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[2024-10-22 17:19:00,904] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.32 | optimizer_step: 0.33
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[2024-10-22 17:19:00,904] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 11293.80 | backward_microstep: 8561.87 | backward_inner_microstep: 8556.33 | backward_allreduce_microstep: 5.47 | step_microstep: 12.09
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[2024-10-22 17:19:00,905] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 11293.71 | backward: 8561.86 | backward_inner: 8556.34 | backward_allreduce: 5.45 | step: 12.11
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0%| | 1/2424 [00:33<22:19:32, 33.17s/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 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 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='Does the image appear to feature an open air shop?')
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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 the dog swimming in a pool?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Does the image show a "Whataburger" cup sitting on a surface?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([3, 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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ANSWER0=VQA(image=LEFT,question='Is a person paddling a canoe diagonally to the left?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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question: ['Does the image show a "Whataburger" cup sitting on a surface?'], 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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tensor([8.9214e-01, 2.1889e-02, 8.2982e-02, 8.9115e-04, 7.9054e-05, 2.3252e-04,
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5.2222e-05, 1.7359e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.9214e-01, 2.1889e-02, 8.2982e-02, 8.9115e-04, 7.9054e-05, 2.3252e-04,
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5.2222e-05, 1.7359e-03], device='cuda:1', grad_fn=<SelectBackward0>)
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question: ['Does the image appear to feature an open air shop?'], responses:['yes']
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question: ['Is the dog swimming in a pool?'], responses:['yes']
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.8921, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.0830, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0249, device='cuda:1', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many people 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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torch.Size([4, 3, 448, 448])
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question: ['Is a person paddling a canoe diagonally to the left?'], responses:['no']
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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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[('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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[('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([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: 3399
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['How many people are in the image?'], responses:['3']
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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([4, 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: 3402
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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: 3400
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tensor([0.5147, 0.2588, 0.0187, 0.0840, 0.0057, 0.0952, 0.0212, 0.0016],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.5147, 0.2588, 0.0187, 0.0840, 0.0057, 0.0952, 0.0212, 0.0016],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.8874, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.1126, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many bottles 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([3, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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question: ['How many bottles are in the image?'], responses:['3']
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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([3, 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: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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tensor([0.3954, 0.2253, 0.0417, 0.1206, 0.0102, 0.1648, 0.0392, 0.0028],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.3954, 0.2253, 0.0417, 0.1206, 0.0102, 0.1648, 0.0392, 0.0028],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0417, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9583, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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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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torch.Size([7, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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