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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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tensor([8.6859e-01, 1.2009e-02, 1.8626e-02, 1.9008e-03, 3.2477e-04, 9.7495e-02,
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7.9239e-04, 2.6076e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([8.6859e-01, 1.2009e-02, 1.8626e-02, 1.9008e-03, 3.2477e-04, 9.7495e-02,
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7.9239e-04, 2.6076e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0186, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9814, 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=RIGHT,question='How many dogs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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tensor([9.6876e-01, 3.5251e-03, 5.0874e-03, 7.5759e-04, 9.4968e-04, 1.1430e-03,
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1.1530e-02, 8.2494e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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0 *************
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['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.6876e-01, 3.5251e-03, 5.0874e-03, 7.5759e-04, 9.4968e-04, 1.1430e-03,
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1.1530e-02, 8.2494e-03], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0.9688, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0312, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many hyenas 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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torch.Size([7, 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 hyenas are in the image?'], responses:['1']
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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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([7, 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: 3397
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question: ['How many dogs are in the image?'], responses:['3']
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[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
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[['3', '4', '1', '5', '8', '2', '6', '12']]
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([0.3187, 0.1717, 0.2277, 0.0124, 0.0505, 0.1007, 0.1144, 0.0039],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.3187, 0.1717, 0.2277, 0.0124, 0.0505, 0.1007, 0.1144, 0.0039],
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device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1007, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8993, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the dog indoors?')
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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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tensor([8.7537e-01, 2.8137e-02, 1.3290e-02, 6.5039e-03, 8.3140e-03, 4.5687e-03,
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6.3415e-02, 3.9736e-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([8.7537e-01, 2.8137e-02, 1.3290e-02, 6.5039e-03, 8.3140e-03, 4.5687e-03,
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6.3415e-02, 3.9736e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: tensor([4.3948e-01, 3.0950e-01, 5.6930e-02, 1.6566e-01, 2.0943e-02, 3.2115e-03,
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4.1471e-03, 1.3396e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] {True: tensor(0.8754, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1246, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([4.3948e-01, 3.0950e-01, 5.6930e-02, 1.6566e-01, 2.0943e-02, 3.2115e-03,
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4.1471e-03, 1.3396e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8343, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1657, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
|
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([7, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
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question: ['Is the dog indoors?'], responses:['yes']
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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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question: ['How many dogs are in the image?'], responses:['1']
|
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
|
[['1', '3', '4', '8', '6', '12', '2', '47']]
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
|
question: ['Is there a woman in the image?'], responses:['yes']
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
|
[('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']]
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
|
tensor([0.6484, 0.0389, 0.0285, 0.0082, 0.0011, 0.2703, 0.0039, 0.0008],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.6484, 0.0389, 0.0285, 0.0082, 0.0011, 0.2703, 0.0039, 0.0008],
|
device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9472, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0528, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([1, 3, 448, 448])
|
question: ['How many dogs are in the image?'], responses:['1']
|
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
|
[['1', '3', '4', '8', '6', '12', '2', '47']]
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
|
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
|
tensor([9.5437e-01, 8.2572e-03, 4.0229e-03, 1.8953e-03, 2.5952e-03, 1.6405e-03,
|
2.7074e-02, 1.4816e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5437e-01, 8.2572e-03, 4.0229e-03, 1.8953e-03, 2.5952e-03, 1.6405e-03,
|
2.7074e-02, 1.4816e-04], device='cuda:1', grad_fn=<SelectBackward0>)
|
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