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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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question: ['What color is the car?'], responses:['light']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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[('light', 0.1263865828213977), ('sunlight', 0.12497693187959452), ('lights', 0.1249334017905698), ('wine', 0.12483576877308507), ('water', 0.12478584053246268), ('glass', 0.12477465739247522), ('lamps', 0.12472148848257057), ('dark', 0.12458532832784439)]
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[['light', 'sunlight', 'lights', 'wine', 'water', 'glass', 'lamps', 'dark']]
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tensor([0.4019, 0.1898, 0.0374, 0.0955, 0.0138, 0.2151, 0.0424, 0.0042],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.4019, 0.1898, 0.0374, 0.0955, 0.0138, 0.2151, 0.0424, 0.0042],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2151, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.7849, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is there a dish visible in the image?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([6.5888e-01, 2.6279e-02, 3.1123e-01, 1.7001e-03, 1.4205e-04, 7.2244e-04,
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2.4622e-04, 8.0172e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.5888e-01, 2.6279e-02, 3.1123e-01, 1.7001e-03, 1.4205e-04, 7.2244e-04,
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2.4622e-04, 8.0172e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6589, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3112, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0299, device='cuda:3', 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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ANSWER0=VQA(image=LEFT,question='Is the case open?')
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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([7, 3, 448, 448])
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torch.Size([13, 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: 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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question: ['Is there a dish visible in the image?'], responses:['no']
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question: ['Is the case open?'], 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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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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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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tensor([9.4358e-01, 1.4325e-02, 7.4305e-03, 2.9042e-03, 4.2293e-03, 2.9479e-03,
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2.4371e-02, 2.1517e-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([9.4358e-01, 1.4325e-02, 7.4305e-03, 2.9042e-03, 4.2293e-03, 2.9479e-03,
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2.4371e-02, 2.1517e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9436, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0564, 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=RIGHT,question='Are all pizzas in boxes?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([11, 3, 448, 448])
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question: ['Are all pizzas in boxes?'], 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([11, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2884
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tensor([7.4248e-01, 2.5659e-01, 3.8339e-05, 1.4398e-04, 1.7427e-04, 4.3088e-04,
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1.0796e-04, 3.7021e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.4248e-01, 2.5659e-01, 3.8339e-05, 1.4398e-04, 1.7427e-04, 4.3088e-04,
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1.0796e-04, 3.7021e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7425, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2566, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0009, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([6.6288e-01, 2.0694e-02, 3.1312e-01, 1.5553e-03, 2.0547e-04, 5.9828e-04,
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9.6508e-05, 8.5317e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.6288e-01, 2.0694e-02, 3.1312e-01, 1.5553e-03, 2.0547e-04, 5.9828e-04,
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9.6508e-05, 8.5317e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6629, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3131, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0240, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is there a woman 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([1, 3, 448, 448])
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question: ['Is there a woman in the image?'], responses:['no']
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2884
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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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tensor([0.6604, 0.1125, 0.0674, 0.0873, 0.0145, 0.0199, 0.0080, 0.0300],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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light *************
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['light', 'sunlight', 'lights', 'wine', 'water', 'glass', 'lamps', 'dark'] tensor([0.6604, 0.1125, 0.0674, 0.0873, 0.0145, 0.0199, 0.0080, 0.0300],
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device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2885
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tensor([8.5124e-01, 1.4792e-01, 4.2582e-05, 7.5645e-05, 3.7543e-04, 1.1170e-04,
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2.0051e-04, 2.9250e-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([8.5124e-01, 1.4792e-01, 4.2582e-05, 7.5645e-05, 3.7543e-04, 1.1170e-04,
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2.0051e-04, 2.9250e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1479, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8512, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2884
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2884
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2885
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2885
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dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2885
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tensor([6.3628e-01, 3.6254e-01, 3.0155e-05, 1.1341e-04, 1.7093e-04, 4.7418e-04,
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3.5755e-04, 3.0529e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.3628e-01, 3.6254e-01, 3.0155e-05, 1.1341e-04, 1.7093e-04, 4.7418e-04,
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3.5755e-04, 3.0529e-05], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3625, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.6363, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0012, device='cuda:0', grad_fn=<DivBackward0>)}
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