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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.3275e-01, 1.1398e-02, 4.5345e-01, 9.0136e-04, 1.6238e-04, 6.0110e-04,
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9.1763e-05, 6.5200e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5327, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.4534, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0138, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Are the dogs sitting on grass?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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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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torch.Size([13, 3, 448, 448])
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845
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tensor([6.3344e-01, 2.1691e-02, 3.3906e-01, 1.4470e-03, 2.4090e-04, 5.5805e-04,
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6.3833e-05, 3.4992e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.3344e-01, 2.1691e-02, 3.3906e-01, 1.4470e-03, 2.4090e-04, 5.5805e-04,
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6.3833e-05, 3.4992e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([5.3241e-01, 2.2816e-02, 4.4137e-01, 1.5094e-03, 1.5974e-04, 8.2376e-04,
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1.2816e-04, 7.8719e-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([5.3241e-01, 2.2816e-02, 4.4137e-01, 1.5094e-03, 1.5974e-04, 8.2376e-04,
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1.2816e-04, 7.8719e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6334, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3391, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0275, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many televisions 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([1, 3, 448, 448])
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5324, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4414, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0262, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many water bottles 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([1, 3, 448, 448])
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question: ['How many televisions 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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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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question: ['How many water bottles 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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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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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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tensor([9.5330e-01, 8.7778e-03, 3.2286e-03, 1.1466e-03, 1.5712e-03, 1.2429e-03,
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3.0643e-02, 8.9494e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5330e-01, 8.7778e-03, 3.2286e-03, 1.1466e-03, 1.5712e-03, 1.2429e-03,
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3.0643e-02, 8.9494e-05], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9533, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0467, 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=LEFT,question='Is the dog in the image lying down?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([5.4488e-01, 2.4759e-02, 4.2434e-01, 6.7493e-04, 2.7263e-04, 4.2909e-03,
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1.2259e-04, 6.5555e-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([5.4488e-01, 2.4759e-02, 4.2434e-01, 6.7493e-04, 2.7263e-04, 4.2909e-03,
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1.2259e-04, 6.5555e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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torch.Size([7, 3, 448, 448])
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tensor([8.0683e-01, 4.5519e-02, 2.7604e-02, 1.1497e-02, 1.4345e-02, 8.6757e-03,
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8.5033e-02, 4.9568e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.0683e-01, 4.5519e-02, 2.7604e-02, 1.1497e-02, 1.4345e-02, 8.6757e-03,
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8.5033e-02, 4.9568e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8068, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1932, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Do the mittens have hands in them?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5449, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.4243, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0308, device='cuda:2', grad_fn=<SubBackward0>)}
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question: ['Are the dogs sitting on grass?'], responses:['no']
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ANSWER0=VQA(image=LEFT,question='How many bottles have blue lids in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 4')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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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([13, 3, 448, 448])
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['Is the dog in the image lying down?'], responses:['yes']
|
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
|
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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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: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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question: ['Do the mittens have hands in them?'], responses:['no']
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question: ['How many bottles have blue lids in the image?'], responses:['2']
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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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[('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: 7, images per sample: 7.0, dynamic token length: 1862
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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