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Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='How many birds are in the picture?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='What color is the area on the front bottom of the train?') |
ANSWER1=EVAL(expr='{ANSWER0} == "yellow"') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many weights does the rack hold?') |
ANSWER1=EVAL(expr='{ANSWER0} > 12') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
torch.Size([1, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many shoes are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([5, 3, 448, 448]) |
question: ['What color is the area on the front bottom of the train?'], responses:['yellow'] |
question: ['How many weights does the rack hold?'], responses:['100'] |
[('yellow', 0.13019233292980176), ('red', 0.12608840659087261), ('green', 0.12436926918223776), ('maroon', 0.12425930516133966), ('pink', 0.12421440410307089), ('mask', 0.12363437991296296), ('orange', 0.12363130058084727), ('color', 0.12361060153886716)] |
[['yellow', 'red', 'green', 'maroon', 'pink', 'mask', 'orange', 'color']] |
[('100', 0.1277092174007614), ('120', 0.12519936731884676), ('88', 0.12483671971182599), ('80', 0.12474858811112934), ('60', 0.12457749608485191), ('99', 0.1243465850330014), ('90', 0.12430147627057883), ('101', 0.12428055006900451)] |
[['100', '120', '88', '80', '60', '99', '90', '101']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329 |
question: ['How many shoes are in the image?'], responses:['2'] |
tensor([6.9038e-01, 2.8817e-01, 3.9985e-03, 4.6899e-03, 1.6804e-03, 1.5558e-06, |
1.1077e-02, 6.8873e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yellow ************* |
['yellow', 'red', 'green', 'maroon', 'pink', 'mask', 'orange', 'color'] tensor([6.9038e-01, 2.8817e-01, 3.9985e-03, 4.6899e-03, 1.6804e-03, 1.5558e-06, |
1.1077e-02, 6.8873e-06], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([8.3134e-01, 9.8632e-02, 7.2783e-04, 9.9718e-03, 4.2922e-02, 6.1986e-03, |
9.8973e-03, 3.0913e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
100 ************* |
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([8.3134e-01, 9.8632e-02, 7.2783e-04, 9.9718e-03, 4.2922e-02, 6.1986e-03, |
9.8973e-03, 3.0913e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)] |
[['2', '3', '4', '1', '5', '8', '7', '29']] |
最后的概率分布为: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)} |
最后的概率分布为: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many hyenas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='Are the shelves full?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
question: ['How many birds are in the picture?'], responses:['2'] |
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)] |
[['2', '3', '4', '1', '5', '8', '7', '29']] |
question: ['How many hyenas are in the image?'], responses:['2'] |
question: ['Are the shelves full?'], responses:['yes'] |
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)] |
[['2', '3', '4', '1', '5', '8', '7', '29']] |
[('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([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1857 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([9.9999e-01, 6.6814e-07, 2.0377e-07, 1.2219e-05, 4.8868e-09, 1.3440e-08, |
7.3073e-09, 6.5773e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9999e-01, 6.6814e-07, 2.0377e-07, 1.2219e-05, 4.8868e-09, 1.3440e-08, |
7.3073e-09, 6.5773e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1857 |
最后的概率分布为: {True: tensor(1.2219e-05, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1857 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1857 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858 |
tensor([1.0000e+00, 7.3627e-09, 1.2238e-08, 4.4365e-08, 1.7727e-10, 4.3884e-10, |
2.2619e-11, 2.0935e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.3627e-09, 1.2238e-08, 4.4365e-08, 1.7727e-10, 4.3884e-10, |
2.2619e-11, 2.0935e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 7.3382e-07, 3.6535e-08, 7.9799e-08, 3.0398e-10, 5.7235e-10, |
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