text stringlengths 0 1.16k |
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no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.2803e-01, 7.1562e-02, 3.2194e-06, 3.4914e-05, 5.4308e-05, 9.8693e-05, |
2.0402e-04, 1.3851e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([7.4572e-01, 2.1031e-02, 2.2629e-01, 3.1866e-03, 1.3216e-04, 3.6522e-04, |
5.6162e-05, 3.2173e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.4572e-01, 2.1031e-02, 2.2629e-01, 3.1866e-03, 1.3216e-04, 3.6522e-04, |
5.6162e-05, 3.2173e-03], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2263, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7457, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0280, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Does the dog in the image have a white coat?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9280, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0716, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0004, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the door of the bus open?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
question: ['Is the door of the bus open?'], 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([3, 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: 3399 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
tensor([8.5091e-01, 1.6684e-02, 1.3049e-01, 9.4970e-04, 8.6112e-05, 4.6358e-04, |
5.0723e-05, 3.7148e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.5091e-01, 1.6684e-02, 1.3049e-01, 9.4970e-04, 8.6112e-05, 4.6358e-04, |
5.0723e-05, 3.7148e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8509, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.1305, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0186, device='cuda:1', grad_fn=<DivBackward0>)} |
question: ['Does the dog in the image have a white coat?'], responses:['no'] |
ANSWER0=VQA(image=RIGHT,question='How many skunks are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
[('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: 13, images per sample: 13.0, dynamic token length: 3399 |
question: ['How many skunks are in the image?'], responses:['1'] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
[('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 |
tensor([5.5558e-01, 3.6207e-02, 1.6070e-02, 3.8188e-01, 5.7296e-03, 1.9793e-03, |
2.2703e-03, 2.7766e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([5.5558e-01, 3.6207e-02, 1.6070e-02, 3.8188e-01, 5.7296e-03, 1.9793e-03, |
2.2703e-03, 2.7766e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5556, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.4444, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT, question='Is the dog wearing a harness?') |
FINAL_ANSWER = RESULT(var=ANSWER0) |
torch.Size([7, 3, 448, 448]) |
tensor([9.0312e-01, 9.5125e-02, 7.1845e-05, 1.3221e-04, 4.3737e-04, 6.7731e-04, |
2.6310e-04, 1.7617e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.0312e-01, 9.5125e-02, 7.1845e-05, 1.3221e-04, 4.3737e-04, 6.7731e-04, |
2.6310e-04, 1.7617e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0951, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9031, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0018, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many laptops are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
tensor([6.9585e-01, 5.0770e-02, 2.0506e-02, 5.8373e-03, 8.8224e-03, 4.1373e-03, |
2.1375e-01, 3.2152e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.9585e-01, 5.0770e-02, 2.0506e-02, 5.8373e-03, 8.8224e-03, 4.1373e-03, |
2.1375e-01, 3.2152e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6959, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.3041, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the golf ball on a tee?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
question: ['How many laptops are in the image?'], responses:['3'] |
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)] |
[['3', '4', '1', '5', '8', '2', '6', '12']] |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
question: ['Is the dog wearing a harness?'], 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']] |
question: ['Is the golf ball on a tee?'], responses:['no'] |
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 |
[('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([3, 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: 1859 |
tensor([0.5371, 0.1358, 0.0469, 0.0267, 0.0033, 0.2383, 0.0101, 0.0016], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.5371, 0.1358, 0.0469, 0.0267, 0.0033, 0.2383, 0.0101, 0.0016], |
device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0469, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9531, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is there a flying bird in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 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: 1859 |
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