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Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='How many slices of lemon are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} > 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is the dog moving toward the camera?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many graduation students are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many basins are on the counter?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many slices of lemon are in the image?'], responses:['0'] |
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)] |
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
question: ['Is the dog moving toward the camera?'], responses:['yes'] |
question: ['How many graduation students are in the image?'], responses:['40'] |
question: ['How many basins are on the counter?'], responses:['两个'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
[('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']] |
[('40', 0.12638022987124733), ('39', 0.12509919407251455), ('42', 0.12494223232783619), ('41', 0.12482626048065008), ('45', 0.12479694604159434), ('38', 0.12473125094691345), ('47', 0.1246423477331973), ('32', 0.1245815385260468)] |
[['40', '39', '42', '41', '45', '38', '47', '32']] |
[('monday', 0.12549230061942876), ('leopard', 0.12538459168488658), ('kia', 0.12528000119427152), ('halloween', 0.12500937705608223), ('tigers', 0.12497983900108975), ('no', 0.12478765121697721), ('spring', 0.12453769928322637), ('awake', 0.12452853994403748)] |
[['monday', 'leopard', 'kia', 'halloween', 'tigers', 'no', 'spring', 'awake']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
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: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
tensor([9.9999e-01, 1.6114e-06, 5.8620e-08, 9.9661e-10, 1.6495e-06, 6.9835e-08, |
1.0198e-06, 4.3641e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9999e-01, 1.6114e-06, 5.8620e-08, 9.9661e-10, 1.6495e-06, 6.9835e-08, |
1.0198e-06, 4.3641e-06], device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.8215e-06, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many opened laptops are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['How many opened laptops 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([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([1.0000e+00, 1.2670e-08, 8.6778e-09, 1.5418e-08, 1.9810e-11, 3.5817e-10, |
6.4104e-11, 3.8480e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.2670e-08, 8.6778e-09, 1.5418e-08, 1.9810e-11, 3.5817e-10, |
6.4104e-11, 3.8480e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([0.7718, 0.0142, 0.0061, 0.0116, 0.1176, 0.0067, 0.0622, 0.0096], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
40 ************* |
['40', '39', '42', '41', '45', '38', '47', '32'] tensor([0.7718, 0.0142, 0.0061, 0.0116, 0.1176, 0.0067, 0.0622, 0.0096], |
device='cuda:1', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(8.6778e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-8.6778e-09, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
最后的概率分布为: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Does the left image show a dog standing on green grass?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([0.0308, 0.1136, 0.3559, 0.0231, 0.0048, 0.0364, 0.0898, 0.3456], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
kia ************* |
['monday', 'leopard', 'kia', 'halloween', 'tigers', 'no', 'spring', 'awake'] tensor([0.0308, 0.1136, 0.3559, 0.0231, 0.0048, 0.0364, 0.0898, 0.3456], |
device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many gorillas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 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']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([1.0000e+00, 1.7731e-10, 7.2767e-11, 1.6786e-10, 1.2849e-10, 1.9770e-08, |
2.0612e-09, 1.9008e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
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