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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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question: ['What shape are the pizzas?'], responses:['circle']
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[('circle', 0.12617788749485503), ('oven', 0.12487454990459093), ('scarf', 0.12486494697119241), ('opaque', 0.12484708980871326), ('bowl', 0.12483020150778239), ('moving', 0.12480932540671164), ('junk', 0.12479891070364031), ('bending', 0.12479708820251399)]
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[['circle', 'oven', 'scarf', 'opaque', 'bowl', 'moving', 'junk', 'bending']]
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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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tensor([8.5704e-01, 2.4643e-02, 1.1599e-01, 1.1841e-03, 1.2524e-04, 3.7390e-04,
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5.3127e-05, 5.9517e-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([8.5704e-01, 2.4643e-02, 1.1599e-01, 1.1841e-03, 1.2524e-04, 3.7390e-04,
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5.3127e-05, 5.9517e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.5704e-01, 2.4643e-02, 1.1599e-01, 1.1841e-03, 1.2524e-04, 3.7390e-04,
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5.3127e-05, 5.9517e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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最后的概率分布为: {True: tensor(0.8007, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1723, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0270, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is there a sofa/chair near the tall window?')
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ANSWER1=VQA(image=RIGHT,question='Is there a sofa/chair near the tall window?')
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ANSWER2=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER2)
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tensor([5.6061e-01, 2.4916e-02, 4.1015e-01, 1.0490e-03, 1.4676e-04, 1.9592e-03,
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1.0058e-04, 1.0656e-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([5.6061e-01, 2.4916e-02, 4.1015e-01, 1.0490e-03, 1.4676e-04, 1.9592e-03,
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1.0058e-04, 1.0656e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.6061e-01, 2.4916e-02, 4.1015e-01, 1.0490e-03, 1.4676e-04, 1.9592e-03,
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1.0058e-04, 1.0656e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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tensor([5.9303e-01, 8.0257e-02, 2.6053e-02, 5.9876e-03, 8.7239e-03, 5.4059e-03,
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2.8013e-01, 4.0851e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.9303e-01, 8.0257e-02, 2.6053e-02, 5.9876e-03, 8.7239e-03, 5.4059e-03,
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2.8013e-01, 4.0851e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.9303e-01, 8.0257e-02, 2.6053e-02, 5.9876e-03, 8.7239e-03, 5.4059e-03,
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2.8013e-01, 4.0851e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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最后的概率分布为: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0.9561, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0439, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is the shop door visible in the image?')
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ANSWER1=VQA(image=RIGHT,question='Is the shop door visible in the image?')
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ANSWER2=EVAL(expr='{ANSWER0} or {ANSWER1}')
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FINAL_ANSWER=RESULT(var=ANSWER2)
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torch.Size([1, 3, 448, 448])
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question: ['Is the shop door visible in the image?'], 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([1, 3, 448, 448]) knan debug pixel values shape
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tensor([7.0527e-01, 2.9400e-01, 3.2091e-05, 1.1284e-04, 1.2682e-04, 1.5019e-04,
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2.5693e-04, 4.9596e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.0527e-01, 2.9400e-01, 3.2091e-05, 1.1284e-04, 1.2682e-04, 1.5019e-04,
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2.5693e-04, 4.9596e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.0527e-01, 2.9400e-01, 3.2091e-05, 1.1284e-04, 1.2682e-04, 1.5019e-04,
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2.5693e-04, 4.9596e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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torch.Size([11, 3, 448, 448])
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question: ['Is there a sofa/chair near the tall window?'], responses:['yes']
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question: ['Is the dog facing left in the image?'], responses:['no']
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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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[('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]) knan debug pixel values shape
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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question: ['Is the shop door visible in the image?'], 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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torch.Size([11, 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([0.9270, 0.0015, 0.0068, 0.0082, 0.0462, 0.0042, 0.0038, 0.0023],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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circle *************
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['circle', 'oven', 'scarf', 'opaque', 'bowl', 'moving', 'junk', 'bending'] tensor([0.9270, 0.0015, 0.0068, 0.0082, 0.0462, 0.0042, 0.0038, 0.0023],
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device='cuda:1', grad_fn=<SelectBackward0>)
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['circle', 'oven', 'scarf', 'opaque', 'bowl', 'moving', 'junk', 'bending'] tensor([0.9270, 0.0015, 0.0068, 0.0082, 0.0462, 0.0042, 0.0038, 0.0023],
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device='cuda:1', grad_fn=<SelectBackward0>)
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最后的概率分布为: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many live ibexes are standing in the grass and weeds?')
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ANSWER1=VQA(image=RIGHT,question='How many live ibexes are standing in the grass and weeds?')
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ANSWER2=EVAL(expr='{ANSWER0} >= 1 or {ANSWER1} >= 1')
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FINAL_ANSWER=RESULT(var=ANSWER2)
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torch.Size([3, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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question: ['How many live ibexes are standing in the grass and weeds?'], 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: 13, images per sample: 13.0, dynamic token length: 3397
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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tensor([0.7319, 0.0620, 0.0332, 0.0167, 0.0228, 0.0122, 0.1195, 0.0018],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.7319, 0.0620, 0.0332, 0.0167, 0.0228, 0.0122, 0.1195, 0.0018],
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device='cuda:1', grad_fn=<SelectBackward0>)
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.7319, 0.0620, 0.0332, 0.0167, 0.0228, 0.0122, 0.1195, 0.0018],
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device='cuda:1', grad_fn=<SelectBackward0>)
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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([7.5520e-01, 2.4783e-02, 2.1637e-01, 2.1064e-03, 1.2114e-04, 4.9499e-04,
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2.0160e-04, 7.2251e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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