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6.7430e-02, 4.1171e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.7444e-01, 2.8109e-02, 1.3706e-02, 4.8859e-03, 7.1062e-03, 3.9086e-03,
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6.7430e-02, 4.1171e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1256, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8744, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many binders 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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torch.Size([5, 3, 448, 448])
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question: ['How many 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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question: ['How many binders are in the image?'], responses:['2']
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
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tensor([6.4529e-01, 3.6404e-02, 7.7135e-03, 3.0475e-01, 3.1801e-03, 1.2575e-03,
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1.2838e-03, 1.2271e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.4529e-01, 3.6404e-02, 7.7135e-03, 3.0475e-01, 3.1801e-03, 1.2575e-03,
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1.2838e-03, 1.2271e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6952, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.3048, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Can you see the lamp in the image?')
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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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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
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tensor([9.5083e-01, 8.7571e-03, 4.8350e-03, 2.0759e-03, 2.8410e-03, 1.8060e-03,
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2.8713e-02, 1.4430e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5083e-01, 8.7571e-03, 4.8350e-03, 2.0759e-03, 2.8410e-03, 1.8060e-03,
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2.8713e-02, 1.4430e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([4.6084e-01, 1.6952e-01, 5.8545e-02, 2.5903e-01, 3.3738e-02, 8.2708e-03,
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9.6686e-03, 3.8927e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)ζεηζ¦ηεεΈδΈΊ:
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([4.6084e-01, 1.6952e-01, 5.8545e-02, 2.5903e-01, 3.3738e-02, 8.2708e-03,
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9.6686e-03, 3.8927e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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{True: tensor(0.9508, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0492, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4608, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5392, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the goat laying down?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([13, 3, 448, 448])
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tensor([9.0359e-01, 4.4981e-02, 9.4297e-03, 3.7294e-02, 2.8759e-03, 8.7658e-04,
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9.0464e-04, 5.1583e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.0359e-01, 4.4981e-02, 9.4297e-03, 3.7294e-02, 2.8759e-03, 8.7658e-04,
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9.0464e-04, 5.1583e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9036, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0964, 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='How many dogs 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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torch.Size([13, 3, 448, 448])
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Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.85 GiB. GPU 2 has a total capacty of 44.34 GiB of which 5.32 GiB is free. Including non-PyTorch memory, this process has 39.00 GiB memory in use. Of the allocated memory 36.23 GiB is allocated by PyTorch, and 2.14 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 5.85 GiB. GPU 1 has a total capacty of 44.34 GiB of which 3.38 GiB is free. Including non-PyTorch memory, this process has 40.95 GiB memory in use. Of the allocated memory 38.11 GiB is allocated by PyTorch, and 2.20 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 5.85 GiB. GPU 3 has a total capacty of 44.34 GiB of which 3.96 GiB is free. Including non-PyTorch memory, this process has 40.36 GiB memory in use. Of the allocated memory 38.07 GiB is allocated by PyTorch, and 1.73 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 17:20:09,379] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.26 | optimizer_step: 0.32
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[2024-10-22 17:20:09,379] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9610.22 | backward_microstep: 10485.01 | backward_inner_microstep: 9200.49 | backward_allreduce_microstep: 1284.03 | step_microstep: 7.34
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[2024-10-22 17:20:09,379] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9610.24 | backward: 10485.00 | backward_inner: 9200.51 | backward_allreduce: 1284.02 | step: 7.35
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0%| | 4/2424 [01:41<16:01:50, 23.85s/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='Are the two pins touching each other?')
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ANSWER1=EVAL(expr='not {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='Is there a plant in one of the vases?')
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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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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 dogs are standing on grass 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([1, 3, 448, 448])
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torch.Size([1, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='Do the golf balls in the left image look noticeably darker and grayer than those in the right image?')
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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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