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tensor([5.4584e-01, 4.5252e-01, 7.6914e-05, 1.2162e-04, 1.6979e-04, 8.0130e-04,
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4.3414e-04, 3.2882e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.4584e-01, 4.5252e-01, 7.6914e-05, 1.2162e-04, 1.6979e-04, 8.0130e-04,
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4.3414e-04, 3.2882e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4525, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5458, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0016, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many hamsters 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([3, 3, 448, 448])
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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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question: ['How many hamsters are in the image?'], responses:['1']
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
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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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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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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
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tensor([6.4988e-01, 4.4225e-02, 1.0504e-02, 1.8252e-03, 3.6301e-03, 1.4658e-03,
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2.8838e-01, 8.8708e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.4988e-01, 4.4225e-02, 1.0504e-02, 1.8252e-03, 3.6301e-03, 1.4658e-03,
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2.8838e-01, 8.8708e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2884, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7116, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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tensor([8.5591e-01, 2.9285e-02, 9.8087e-03, 2.3964e-03, 4.3501e-03, 2.0745e-03,
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9.6030e-02, 1.4712e-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([8.5591e-01, 2.9285e-02, 9.8087e-03, 2.3964e-03, 4.3501e-03, 2.0745e-03,
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9.6030e-02, 1.4712e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1441, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8559, 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=LEFT,question='Is there water in the image?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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tensor([9.1888e-01, 8.0291e-02, 6.5637e-05, 1.4110e-04, 2.6952e-04, 5.9935e-05,
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2.2502e-04, 6.5721e-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([9.1888e-01, 8.0291e-02, 6.5637e-05, 1.4110e-04, 2.6952e-04, 5.9935e-05,
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2.2502e-04, 6.5721e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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torch.Size([7, 3, 448, 448])
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0803, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9189, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is there water in the image?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([7, 3, 448, 448])
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Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.20 GiB. GPU 2 has a total capacty of 44.34 GiB of which 2.45 GiB is free. Including non-PyTorch memory, this process has 41.87 GiB memory in use. Of the allocated memory 38.69 GiB is allocated by PyTorch, and 2.54 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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question: ['Is there water in the image?'], responses:['yes']
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ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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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([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: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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Encountered ExecuteError: CUDA out of memory. Tried to allocate 656.00 MiB. GPU 0 has a total capacty of 44.34 GiB of which 496.94 MiB is free. Including non-PyTorch memory, this process has 43.84 GiB memory in use. Of the allocated memory 40.55 GiB is allocated by PyTorch, and 2.67 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 17:19:24,437] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.44 | optimizer_gradients: 0.20 | optimizer_step: 0.30
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[2024-10-22 17:19:24,438] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12988.75 | backward_microstep: 10520.11 | backward_inner_microstep: 10514.62 | backward_allreduce_microstep: 5.40 | step_microstep: 7.39
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[2024-10-22 17:19:24,438] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12988.76 | backward: 10520.10 | backward_inner: 10514.64 | backward_allreduce: 5.38 | step: 7.40
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0%| | 2/2424 [00:56<18:30:06, 27.50s/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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ANSWER0=VQA(image=LEFT,question='How many chimneys 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 warthogs 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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ANSWER0=VQA(image=RIGHT,question='Is there a structure with a wooden roof to the right of the yurt?')
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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='How many creatures are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 8')
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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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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many warthogs are in the image?'], responses:['5']
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question: ['How many creatures are in the image?'], responses:['many']
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[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
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[['5', '8', '4', '6', '3', '7', '11', '9']]
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[('many', 0.12680051474066337), ('few', 0.12559712123098582), ('several', 0.12545126119006317), ('blinds', 0.12452572291517987), ('moss', 0.12441899466837554), ('rainbow', 0.1244056457460399), ('kite', 0.12440323404357946), ('directions', 0.12439750546511286)]
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