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1%| | 24/2424 [09:45<16:06:27, 24.16s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many boats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 6')
ANSWER2=EVAL(expr='{ANSWER0} >= 12')
ANSWER3=EVAL(expr='{ANSWER1} or {ANSWER2}')
FINAL_ANSWER=RESULT(var=ANSWER3)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image contain a saxophone?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are seats available in the reading area?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the dog standing with a front leg off the ground?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([11, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many boats are in the image?'], responses:['3']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
question: ['Does the image contain a saxophone?'], responses:['yes']
question: ['Are seats available in the reading area?'], 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']]
torch.Size([11, 3, 448, 448]) knan debug pixel values shape
[('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']]
question: ['Is the dog standing with a front leg off the ground?'], responses:['yes']
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
[('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: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
tensor([0.1727, 0.1537, 0.0815, 0.1504, 0.1035, 0.1726, 0.1191, 0.0465],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.1727, 0.1537, 0.0815, 0.1504, 0.1035, 0.1726, 0.1191, 0.0465],
device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.2691, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.7309, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many wine glasses are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
question: ['How many wine glasses are in the image?'], responses:['1']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
[('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']]
tensor([6.0121e-01, 2.8716e-02, 3.6465e-01, 2.8917e-03, 2.3040e-04, 8.1680e-04,
2.0310e-04, 1.2839e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.0121e-01, 2.8716e-02, 3.6465e-01, 2.8917e-03, 2.3040e-04, 8.1680e-04,
2.0310e-04, 1.2839e-03], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.6012, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.3647, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0341, device='cuda:2', grad_fn=<DivBackward0>)}
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
ANSWER0=VQA(image=RIGHT,question='How many dogs are sitting in the grass?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([5.0367e-01, 2.0721e-02, 4.7316e-01, 1.5582e-03, 1.1687e-04, 2.7022e-04,
1.2675e-04, 3.7653e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.0367e-01, 2.0721e-02, 4.7316e-01, 1.5582e-03, 1.1687e-04, 2.7022e-04,
1.2675e-04, 3.7653e-04], device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([5, 3, 448, 448])
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.5037, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4732, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0232, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the image have a white background?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([5.9687e-01, 2.1178e-02, 3.7864e-01, 1.0281e-03, 2.1556e-04, 9.6603e-04,
1.1179e-04, 9.8668e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.9687e-01, 2.1178e-02, 3.7864e-01, 1.0281e-03, 2.1556e-04, 9.6603e-04,
1.1179e-04, 9.8668e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.5969, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3786, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0245, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Does the duck in the image have its beak on the ground?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
question: ['How many dogs are sitting in the grass?'], 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']]