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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2233, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.7753, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0015, device='cuda:2', grad_fn=<SubBackward0>)}
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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: 1860
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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: 1860
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tensor([0.3827, 0.1206, 0.0368, 0.4210, 0.0223, 0.0077, 0.0082, 0.0007],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.3827, 0.1206, 0.0368, 0.4210, 0.0223, 0.0077, 0.0082, 0.0007],
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device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4210, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5790, 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='How many rodents 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([1, 3, 448, 448])
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question: ['How many rodents are in the image?'], responses:['1']
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tensor([7.2282e-01, 6.7233e-02, 9.7243e-03, 1.9451e-01, 3.4672e-03, 9.5859e-04,
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1.1933e-03, 8.6256e-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([7.2282e-01, 6.7233e-02, 9.7243e-03, 1.9451e-01, 3.4672e-03, 9.5859e-04,
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1.1933e-03, 8.6256e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8055, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1945, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many chimps 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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[('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])
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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tensor([9.1584e-01, 1.6766e-02, 6.5671e-03, 2.7311e-03, 3.9817e-03, 2.2444e-03,
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5.1658e-02, 2.0783e-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([9.1584e-01, 1.6766e-02, 6.5671e-03, 2.7311e-03, 3.9817e-03, 2.2444e-03,
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5.1658e-02, 2.0783e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0842, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9158, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.21 GiB. GPU 3 has a total capacty of 44.34 GiB of which 2.04 GiB is free. Including non-PyTorch memory, this process has 42.28 GiB memory in use. Of the allocated memory 40.52 GiB is allocated by PyTorch, and 1.21 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 17:25:47,560] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.43 | optimizer_gradients: 0.23 | optimizer_step: 0.31
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[2024-10-22 17:25:47,560] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12243.55 | backward_microstep: 11754.27 | backward_inner_microstep: 11748.45 | backward_allreduce_microstep: 5.62 | step_microstep: 7.82
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[2024-10-22 17:25:47,560] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12243.55 | backward: 11754.26 | backward_inner: 11748.58 | backward_allreduce: 5.61 | step: 7.83
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1%| | 18/2424 [07:19<16:08:54, 24.16s/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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ANSWER0=VQA(image=LEFT,question='Is a slice being lifted off the pizza in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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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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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 the food being served in a white dish?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([3, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='How many people 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([7, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='Is the animal in the image standing on all fours?')
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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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torch.Size([13, 3, 448, 448])
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question: ['Is a slice being lifted off the pizza in the image?'], 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([3, 3, 448, 448]) knan debug pixel values shape
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question: ['Is the food being served in a white dish?'], responses:['yes']
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question: ['How many people are in the image?'], responses:['4']
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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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[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
|
[['4', '5', '3', '8', '6', '1', '2', '11']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
|
tensor([6.0687e-01, 3.9183e-01, 7.2778e-05, 1.7290e-04, 1.2966e-04, 3.4735e-04,
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4.7665e-04, 1.0349e-04], 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([6.0687e-01, 3.9183e-01, 7.2778e-05, 1.7290e-04, 1.2966e-04, 3.4735e-04,
|
4.7665e-04, 1.0349e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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question: ['Is the animal in the image standing on all fours?'], responses:['yes']
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3918, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6069, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
|
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