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ANSWER1=EVAL(expr='{ANSWER0} == "grey material"') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many cups of dessert are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many beakers are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} > 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
torch.Size([3, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['What is the dog lying on?'], responses:['floor'] |
[('floor', 0.12748470390459177), ('ceiling', 0.1252678530291957), ('tile', 0.12500253489740884), ('doorway', 0.12451792096992438), ('backyard', 0.12449536067345307), ('shelf', 0.12441824477392803), ('chest', 0.1244098131788867), ('mattress', 0.12440356857261156)] |
[['floor', 'ceiling', 'tile', 'doorway', 'backyard', 'shelf', 'chest', 'mattress']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
question: ['How many beakers are in the image?'], responses:['5'] |
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)] |
[['5', '8', '4', '6', '3', '7', '11', '9']] |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
tensor([9.7454e-01, 1.0520e-02, 8.2711e-04, 3.9559e-03, 2.3529e-04, 1.5119e-03, |
4.1169e-05, 8.3643e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
floor ************* |
['floor', 'ceiling', 'tile', 'doorway', 'backyard', 'shelf', 'chest', 'mattress'] tensor([9.7454e-01, 1.0520e-02, 8.2711e-04, 3.9559e-03, 2.3529e-04, 1.5119e-03, |
4.1169e-05, 8.3643e-03], device='cuda:1', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {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>)} |
ANSWER0=VQA(image=LEFT,question='Is the drum on the left white?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([9.6220e-01, 4.7779e-06, 2.4956e-05, 1.7624e-02, 1.7609e-09, 2.0138e-02, |
3.3248e-06, 3.7219e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.6220e-01, 4.7779e-06, 2.4956e-05, 1.7624e-02, 1.7609e-09, 2.0138e-02, |
3.3248e-06, 3.7219e-06], device='cuda:3', grad_fn=<SelectBackward0>) |
question: ['How many cups of dessert are in the image?'], responses:['1'] |
question: ['Does at least one cake have strawberry on it?'], responses:['yes'] |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is there a person in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
[('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']] |
[('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 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
question: ['Is the drum on the left white?'], responses:['yes'] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402 |
question: ['Is there a person in the image?'], 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']] |
[('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([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
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: 3399 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
tensor([1.0000e+00, 3.8757e-08, 5.9891e-09, 2.3656e-07, 1.5653e-08, 5.3270e-09, |
8.7575e-10, 8.5814e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.8757e-08, 5.9891e-09, 2.3656e-07, 1.5653e-08, 5.3270e-09, |
8.7575e-10, 8.5814e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(5.9891e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.5164e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
tensor([1.0000e+00, 4.2958e-09, 2.9694e-10, 1.2469e-10, 3.7536e-10, 7.4210e-09, |
3.6899e-07, 7.0176e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.2958e-09, 2.9694e-10, 1.2469e-10, 3.7536e-10, 7.4210e-09, |
3.6899e-07, 7.0176e-11], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 1.0175e-08, 9.9312e-08, 5.7855e-08, 3.6914e-10, 3.2612e-10, |
2.8502e-10, 1.0519e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.0175e-08, 9.9312e-08, 5.7855e-08, 3.6914e-10, 3.2612e-10, |
2.8502e-10, 1.0519e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(4.2958e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(9.9312e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.3911e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
torch.Size([1, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many glass bottles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['How many dogs are in the image?'], responses:['ไธ'] |
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)] |
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
tensor([2.1603e-04, 8.2725e-04, 4.1412e-02, 7.2785e-01, 1.0650e-01, 4.9196e-02, |
5.1629e-03, 6.8838e-02], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
bulldog ************* |
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([2.1603e-04, 8.2725e-04, 4.1412e-02, 7.2785e-01, 1.0650e-01, 4.9196e-02, |
5.1629e-03, 6.8838e-02], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)} |
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