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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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question: ['How many Canadian geese 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([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: 1862
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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: 1862
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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: 1862
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tensor([0.9615, 0.0087, 0.0077, 0.0019, 0.0041, 0.0019, 0.0023, 0.0118],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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0 *************
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['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([0.9615, 0.0087, 0.0077, 0.0019, 0.0041, 0.0019, 0.0023, 0.0118],
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device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0.9615, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0385, 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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ANSWER0=VQA(image=LEFT,question='Is there a front awning 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([7, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.21 GiB. GPU 2 has a total capacty of 44.34 GiB of which 414.94 MiB is free. Including non-PyTorch memory, this process has 43.92 GiB memory in use. Of the allocated memory 40.52 GiB is allocated by PyTorch, and 2.77 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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tensor([8.1799e-01, 7.6144e-02, 4.6174e-02, 3.8271e-02, 1.1318e-02, 5.6900e-03,
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4.1587e-03, 2.5036e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.1799e-01, 7.6144e-02, 4.6174e-02, 3.8271e-02, 1.1318e-02, 5.6900e-03,
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4.1587e-03, 2.5036e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.8180, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1820, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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[2024-10-22 17:20:33,357] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.42 | optimizer_gradients: 0.25 | optimizer_step: 0.32
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[2024-10-22 17:20:33,357] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12181.19 | backward_microstep: 11681.89 | backward_inner_microstep: 11676.95 | backward_allreduce_microstep: 4.85 | step_microstep: 95.61
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[2024-10-22 17:20:33,357] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12181.21 | backward: 11681.88 | backward_inner: 11676.98 | backward_allreduce: 4.83 | step: 95.63
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0%| | 5/2424 [02:05<16:03:20, 23.89s/it]Registering VQA_lavis 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 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=RIGHT,question='Is the fragrance bottle a different color than its box?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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ANSWER0=VQA(image=RIGHT,question='How many of the ape's feet can be seen 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='Do boats float in the water on a sunny day in the image?')
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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 drawers are on the cabinet?')
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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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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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torch.Size([5, 3, 448, 448])
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question: ['How many drawers are on the cabinet?'], responses:['5']
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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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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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question: ['How many of the ape'], 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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question: ['Do boats float in the water on a sunny day in the image?'], responses:['no']
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question: ['Is the fragrance bottle a different color than its box?'], responses:['yes']
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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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[('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([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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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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tensor([0.1837, 0.0978, 0.1799, 0.1981, 0.1306, 0.1129, 0.0346, 0.0623],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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6 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.1837, 0.0978, 0.1799, 0.1981, 0.1306, 0.1129, 0.0346, 0.0623],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.1306, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8694, 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 people 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: 3402
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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 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([7, 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: 3399
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