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[['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: 1865 |
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: 1865 |
question: ['Are triangular pennants on display in the image?'], responses:['yes'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
[('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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
tensor([6.4539e-01, 7.1716e-02, 1.4706e-02, 2.5274e-01, 8.9224e-03, 3.0837e-03, |
2.8959e-03, 5.4215e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.4539e-01, 7.1716e-02, 1.4706e-02, 2.5274e-01, 8.9224e-03, 3.0837e-03, |
2.8959e-03, 5.4215e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([0.5833, 0.0243, 0.3840, 0.0017, 0.0009, 0.0028, 0.0007, 0.0023], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([0.5833, 0.0243, 0.3840, 0.0017, 0.0009, 0.0028, 0.0007, 0.0023], |
device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5833, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.3840, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0327, device='cuda:1', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6454, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3546, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many striped animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is the dog wearing a collar?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
tensor([9.7554e-01, 3.3465e-03, 1.5859e-03, 5.4363e-04, 1.0626e-03, 1.0400e-03, |
3.2528e-03, 1.3628e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.7554e-01, 3.3465e-03, 1.5859e-03, 5.4363e-04, 1.0626e-03, 1.0400e-03, |
3.2528e-03, 1.3628e-02], device='cuda:3', grad_fn=<SelectBackward0>) |
torch.Size([7, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0.9755, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0245, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the dog wearing a collar?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Is the dog wearing a collar?'], 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([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
question: ['How many striped animals are in the image?'], responses:['1'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859 |
[('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']] |
question: ['Is the dog wearing a collar?'], 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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
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: 1859 |
tensor([8.5384e-01, 1.9942e-02, 1.2301e-01, 1.2081e-03, 7.3192e-05, 2.9512e-04, |
7.4671e-05, 1.5597e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.5384e-01, 1.9942e-02, 1.2301e-01, 1.2081e-03, 7.3192e-05, 2.9512e-04, |
7.4671e-05, 1.5597e-03], device='cuda:2', grad_fn=<SelectBackward0>) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859 |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8538, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1230, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0232, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many trains are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([8.8904e-01, 1.5546e-02, 9.3773e-02, 8.7034e-04, 4.1621e-05, 1.5454e-04, |
3.4063e-05, 5.3917e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.8904e-01, 1.5546e-02, 9.3773e-02, 8.7034e-04, 4.1621e-05, 1.5454e-04, |
3.4063e-05, 5.3917e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8890, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.0938, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0172, device='cuda:0', grad_fn=<SubBackward0>)} |
question: ['How many trains 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']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([7.7419e-01, 4.9477e-02, 2.4074e-02, 8.3197e-03, 1.1370e-02, 5.7157e-03, |
1.2616e-01, 6.9965e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([7.7419e-01, 4.9477e-02, 2.4074e-02, 8.3197e-03, 1.1370e-02, 5.7157e-03, |
1.2616e-01, 6.9965e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7742, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2258, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([8.1681e-01, 1.8225e-01, 8.4227e-05, 1.2896e-04, 5.7960e-05, 2.7477e-04, |
2.5149e-04, 1.3817e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.1681e-01, 1.8225e-01, 8.4227e-05, 1.2896e-04, 5.7960e-05, 2.7477e-04, |
2.5149e-04, 1.3817e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1823, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8168, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0009, device='cuda:3', grad_fn=<DivBackward0>)} |
tensor([5.1881e-01, 1.8218e-01, 3.8186e-02, 2.3392e-01, 1.8038e-02, 3.5521e-03, |
5.1683e-03, 1.4199e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
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