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5.8157e-05, 1.2676e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.7111, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2637, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0253, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Does the left image feature a barn style door made of weathered-look horizontal wood boards?')
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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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question: ['How many chimpanzees are in the image?'], 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([3, 3, 448, 448]) knan debug pixel values shape
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Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.22 GiB. GPU 0 has a total capacty of 44.34 GiB of which 1.06 GiB is free. Including non-PyTorch memory, this process has 43.26 GiB memory in use. Of the allocated memory 40.56 GiB is allocated by PyTorch, and 2.09 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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ๆๅ็ๆฆ็ๅๅธไธบ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
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tensor([0.1860, 0.1344, 0.1143, 0.1859, 0.0636, 0.1613, 0.0567, 0.0978],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.1860, 0.1344, 0.1143, 0.1859, 0.0636, 0.1613, 0.0567, 0.0978],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0636, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9364, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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[2024-10-22 17:19:49,259] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.37 | optimizer_step: 0.33
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[2024-10-22 17:19:49,260] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12288.91 | backward_microstep: 12509.25 | backward_inner_microstep: 10684.06 | backward_allreduce_microstep: 1824.77 | step_microstep: 7.80
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[2024-10-22 17:19:49,260] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12288.93 | backward: 12509.24 | backward_inner: 10684.28 | backward_allreduce: 1824.72 | step: 7.82
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0%| | 3/2424 [01:21<17:40:19, 26.28s/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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ANSWER0=VQA(image=RIGHT,question='How many white capped bottles are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 16')
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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=RIGHT,question='Is there an unworn knee pad to the right of a model's legs?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Does the sleepwear feature a Disney Princess theme on the front?')
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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 pendant style lamps are above the bakery case 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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torch.Size([5, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many white capped bottles are in the image?'], responses:['many']
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question: ['Does the sleepwear feature a Disney Princess theme on the front?'], responses:['no']
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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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[['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions']]
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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([1, 3, 448, 448]) knan debug pixel values shape
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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: 326
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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question: ['Is there an unworn knee pad to the right of a model'], responses:['yes']
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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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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tensor([0.6039, 0.1310, 0.1869, 0.0089, 0.0136, 0.0314, 0.0049, 0.0195],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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many *************
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['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions'] tensor([0.6039, 0.1310, 0.1869, 0.0089, 0.0136, 0.0314, 0.0049, 0.0195],
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device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([5.6103e-01, 4.3699e-01, 4.2379e-04, 1.2735e-04, 3.0892e-04, 5.4178e-04,
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2.2134e-04, 3.5507e-04], 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([5.6103e-01, 4.3699e-01, 4.2379e-04, 1.2735e-04, 3.0892e-04, 5.4178e-04,
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2.2134e-04, 3.5507e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is the mouth of the dog open?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.4370, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5610, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0020, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many paper towel rolls 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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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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question: ['How many pendant style lamps are above the bakery case in the image?'], responses:['2']
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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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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tensor([5.0163e-01, 2.4089e-02, 4.7123e-01, 8.8055e-04, 1.5856e-04, 9.0009e-04,
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2.1315e-04, 8.9567e-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([5.0163e-01, 2.4089e-02, 4.7123e-01, 8.8055e-04, 1.5856e-04, 9.0009e-04,
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2.1315e-04, 8.9567e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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