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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
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tensor([4.7601e-01, 2.9483e-02, 4.9060e-01, 1.2110e-03, 1.9515e-04, 9.7798e-04,
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1.4793e-04, 1.3803e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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no *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([4.7601e-01, 2.9483e-02, 4.9060e-01, 1.2110e-03, 1.9515e-04, 9.7798e-04,
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1.4793e-04, 1.3803e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4760, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.4906, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0334, device='cuda:0', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many arches 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([13, 3, 448, 448])
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tensor([6.7045e-01, 2.6022e-02, 3.0075e-01, 1.3238e-03, 1.4883e-04, 4.7973e-04,
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1.0540e-04, 7.1840e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.7045e-01, 2.6022e-02, 3.0075e-01, 1.3238e-03, 1.4883e-04, 4.7973e-04,
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1.0540e-04, 7.1840e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6705, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.3008, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0288, device='cuda:2', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many bottles 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([13, 3, 448, 448])
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Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.93 GiB. GPU 0 has a total capacty of 44.34 GiB of which 1.07 GiB is free. Including non-PyTorch memory, this process has 43.25 GiB memory in use. Of the allocated memory 40.79 GiB is allocated by PyTorch, and 1.85 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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ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.93 GiB. GPU 2 has a total capacty of 44.34 GiB of which 1.05 GiB is free. Including non-PyTorch memory, this process has 43.28 GiB memory in use. Of the allocated memory 40.77 GiB is allocated by PyTorch, and 1.87 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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ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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[2024-10-22 17:22:34,766] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.36 | optimizer_step: 0.33
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[2024-10-22 17:22:34,767] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12751.29 | backward_microstep: 10965.44 | backward_inner_microstep: 10693.59 | backward_allreduce_microstep: 271.75 | step_microstep: 8.86
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[2024-10-22 17:22:34,767] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12751.31 | backward: 10965.43 | backward_inner: 10693.62 | backward_allreduce: 271.73 | step: 8.87
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0%| | 10/2424 [04:07<16:13:49, 24.20s/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='How many mostly black dogs 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 there a blue seating area near the books in the image?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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ANSWER0=VQA(image=RIGHT,question='How many men are working on the roof of the house?')
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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=LEFT,question='How many horned animals 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([3, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([3, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Is there a blue seating area near the books in the image?'], responses:['no']
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question: ['How many horned animals are in the image?'], responses:['1']
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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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[('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([3, 3, 448, 448]) knan debug pixel values shape
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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question: ['How many mostly black dogs 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([7, 3, 448, 448]) knan debug pixel values shape
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tensor([5.6177e-01, 4.3750e-01, 1.1427e-05, 1.1786e-04, 1.8298e-04, 1.7538e-04,
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2.1789e-04, 2.6700e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.6177e-01, 4.3750e-01, 1.1427e-05, 1.1786e-04, 1.8298e-04, 1.7538e-04,
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2.1789e-04, 2.6700e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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tensor([8.1985e-01, 2.9868e-02, 1.3672e-02, 3.1464e-03, 4.8750e-03, 2.6846e-03,
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1.2573e-01, 1.7865e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.1985e-01, 2.9868e-02, 1.3672e-02, 3.1464e-03, 4.8750e-03, 2.6846e-03,
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1.2573e-01, 1.7865e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4375, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.5618, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0007, device='cuda:2', grad_fn=<SubBackward0>)}
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question: ['How many men are working on the roof of the house?'], responses:['1']
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0299, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9701, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many power poles are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 6')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many elephants 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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torch.Size([7, 3, 448, 448])
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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([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: 3400
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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