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torch.Size([7, 3, 448, 448])
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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([5, 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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question: ['How many striped animals 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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question: ['What color are the vases?'], responses:['green']
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[('green', 0.1326115459908909), ('yellow', 0.12668030247077625), ('red', 0.12551779073733718), ('wild', 0.12324669870262604), ('orange and blue', 0.12319974118412196), ('bronze', 0.1230515752050065), ('pink', 0.12286305245049417), ('red white blue', 0.12282929325874692)]
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[['green', 'yellow', 'red', 'wild', 'orange and blue', 'bronze', 'pink', 'red white blue']]
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torch.Size([7, 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: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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tensor([7.5867e-01, 1.3153e-01, 3.5402e-02, 5.4936e-02, 1.3025e-02, 3.2994e-03,
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2.9108e-03, 2.3080e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.5867e-01, 1.3153e-01, 3.5402e-02, 5.4936e-02, 1.3025e-02, 3.2994e-03,
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2.9108e-03, 2.3080e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7587, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2413, 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 trains 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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tensor([9.3517e-01, 5.4534e-03, 6.6178e-03, 8.8892e-04, 7.4647e-03, 1.7699e-02,
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2.4815e-02, 1.8915e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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green *************
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['green', 'yellow', 'red', 'wild', 'orange and blue', 'bronze', 'pink', 'red white blue'] tensor([9.3517e-01, 5.4534e-03, 6.6178e-03, 8.8892e-04, 7.4647e-03, 1.7699e-02,
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2.4815e-02, 1.8915e-03], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['How many trains 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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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: 3398
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tensor([8.6995e-01, 2.8220e-02, 9.7606e-02, 1.6825e-03, 1.8305e-04, 7.6259e-04,
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9.6310e-05, 1.4989e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.6995e-01, 2.8220e-02, 9.7606e-02, 1.6825e-03, 1.8305e-04, 7.6259e-04,
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9.6310e-05, 1.4989e-03], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8700, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0976, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0324, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is the dog wearing a collar?')
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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: 13, images per sample: 13.0, dynamic token length: 3398
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tensor([7.7226e-01, 4.9382e-02, 2.4779e-02, 9.1145e-03, 1.1726e-02, 5.8950e-03,
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1.2610e-01, 7.4362e-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([7.7226e-01, 4.9382e-02, 2.4779e-02, 9.1145e-03, 1.1726e-02, 5.8950e-03,
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1.2610e-01, 7.4362e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7723, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2277, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the dog wearing a collar?')
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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 5.85 GiB. GPU 2 has a total capacty of 44.34 GiB of which 4.60 GiB is free. Including non-PyTorch memory, this process has 39.73 GiB memory in use. Of the allocated memory 36.88 GiB is allocated by PyTorch, and 2.22 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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question: ['Is the dog wearing a collar?'], responses:['yes']
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ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 6')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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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])
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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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question: ['How many bottles are in the image?'], responses:['6']
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[('6', 0.12794147189263105), ('8', 0.12539492259598553), ('12', 0.12539359088927945), ('5', 0.12471292164321114), ('4', 0.12443617393590153), ('1', 0.12417386497855347), ('11', 0.12398049124372558), ('3', 0.12396656282071232)]
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[['6', '8', '12', '5', '4', '1', '11', '3']]
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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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: 1859
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tensor([5.1310e-01, 1.7908e-01, 3.9958e-02, 2.4096e-01, 1.7558e-02, 3.7972e-03,
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5.4078e-03, 1.4406e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([5.1310e-01, 1.7908e-01, 3.9958e-02, 2.4096e-01, 1.7558e-02, 3.7972e-03,
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5.4078e-03, 1.4406e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5131, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.4869, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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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([8.8332e-01, 1.5928e-02, 9.9107e-02, 8.5229e-04, 4.1472e-05, 1.5831e-04,
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3.5962e-05, 5.5374e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.8332e-01, 1.5928e-02, 9.9107e-02, 8.5229e-04, 4.1472e-05, 1.5831e-04,
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3.5962e-05, 5.5374e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8833, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.0991, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0176, device='cuda:0', grad_fn=<SubBackward0>)}
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tensor([0.5156, 0.1897, 0.0097, 0.2419, 0.0231, 0.0018, 0.0145, 0.0037],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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6 *************
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['6', '8', '12', '5', '4', '1', '11', '3'] tensor([0.5156, 0.1897, 0.0097, 0.2419, 0.0231, 0.0018, 0.0145, 0.0037],
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device='cuda:2', grad_fn=<SelectBackward0>)
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