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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
tensor([0.4004, 0.2929, 0.0839, 0.1790, 0.0273, 0.0076, 0.0083, 0.0006], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.4004, 0.2929, 0.0839, 0.1790, 0.0273, 0.0076, 0.0083, 0.0006], |
device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2929, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.7071, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many horses are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([0.2418, 0.1031, 0.1242, 0.2248, 0.0738, 0.1143, 0.0271, 0.0909], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2418, 0.1031, 0.1242, 0.2248, 0.0738, 0.1143, 0.0271, 0.0909], |
device='cuda:3', grad_fn=<SelectBackward0>) |
torch.Size([13, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many slices of lemon are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} > 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
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 |
question: ['How many slices of lemon are in the image?'], 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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many horses are in the image?'], responses:['0'] |
tensor([5.1680e-01, 2.2677e-02, 4.5604e-01, 2.2491e-03, 1.8594e-04, 6.7346e-04, |
2.8364e-04, 1.0921e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.1680e-01, 2.2677e-02, 4.5604e-01, 2.2491e-03, 1.8594e-04, 6.7346e-04, |
2.8364e-04, 1.0921e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: [('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']] |
{True: tensor(0.5168, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.4560, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0272, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the dog in the image lying down?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
question: ['Is the dog in the image lying down?'], responses:['no'] |
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)] |
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
tensor([7.5439e-01, 2.4492e-01, 3.4567e-05, 8.1870e-05, 4.1508e-05, 2.8367e-04, |
2.0040e-04, 4.7515e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.5439e-01, 2.4492e-01, 3.4567e-05, 8.1870e-05, 4.1508e-05, 2.8367e-04, |
2.0040e-04, 4.7515e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2449, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.7544, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0007, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many pairs of tinted lips are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
question: ['How many pairs of tinted lips 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([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
tensor([0.2039, 0.2081, 0.2240, 0.0831, 0.1295, 0.0748, 0.0750, 0.0016], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.2039, 0.2081, 0.2240, 0.0831, 0.1295, 0.0748, 0.0750, 0.0016], |
device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9169, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0831, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
tensor([9.0932e-01, 1.6648e-02, 7.3888e-03, 2.5475e-03, 3.9510e-03, 1.9148e-03, |
5.8135e-02, 9.5141e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.0932e-01, 1.6648e-02, 7.3888e-03, 2.5475e-03, 3.9510e-03, 1.9148e-03, |
5.8135e-02, 9.5141e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9675, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0325, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is there a black horse in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
question: ['Is there a black horse in the image?'], responses:['no'] |
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)] |
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']] |
tensor([8.1644e-01, 2.3941e-02, 1.0624e-02, 3.9043e-03, 6.0502e-03, 4.9831e-03, |
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