text stringlengths 0 1.16k |
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2.7737e-04, 1.2238e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.6138e-01, 4.3720e-01, 8.9261e-05, 1.4403e-04, 3.6972e-04, 4.1917e-04, |
2.7737e-04, 1.2238e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([5.7654e-01, 1.6163e-02, 4.0463e-01, 5.5568e-04, 1.5422e-04, 1.3415e-03, |
9.0513e-05, 5.2435e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.7654e-01, 1.6163e-02, 4.0463e-01, 5.5568e-04, 1.5422e-04, 1.3415e-03, |
9.0513e-05, 5.2435e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6480, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.3423, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0098, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many pairs of free weights are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4372, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5614, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0014, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many people are looking straight ahead?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5765, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.4046, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0188, device='cuda:3', grad_fn=<SubBackward0>)} |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='How many monkeys are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['How many pairs of free weights 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']] |
tensor([6.4017e-01, 2.1907e-02, 2.4519e-02, 6.1995e-03, 1.0243e-03, 3.0241e-01, |
3.2551e-03, 5.1483e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([6.4017e-01, 2.1907e-02, 2.4519e-02, 6.1995e-03, 1.0243e-03, 3.0241e-01, |
3.2551e-03, 5.1483e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0245, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9755, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Are the items in the image laid on a plain white surface?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
question: ['How many people are looking straight ahead?'], 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([7, 3, 448, 448]) knan debug pixel values shape |
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 |
question: ['Are the items in the image laid on a plain white surface?'], 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([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
question: ['How many monkeys 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']] |
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 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([0.3201, 0.2342, 0.2200, 0.1218, 0.0477, 0.0396, 0.0160, 0.0006], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.3201, 0.2342, 0.2200, 0.1218, 0.0477, 0.0396, 0.0160, 0.0006], |
device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1218, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8782, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many barber chairs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
question: ['How many barber chairs 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([1, 3, 448, 448]) knan debug pixel values shape |
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([0.3522, 0.2006, 0.0997, 0.2664, 0.0534, 0.0112, 0.0159, 0.0005], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.3522, 0.2006, 0.0997, 0.2664, 0.0534, 0.0112, 0.0159, 0.0005], |
device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2664, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.7336, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many wartgogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([9.4809e-01, 8.9698e-03, 4.1656e-02, 3.2545e-04, 4.1501e-05, 1.5969e-04, |
6.1542e-06, 7.4851e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.4809e-01, 8.9698e-03, 4.1656e-02, 3.2545e-04, 4.1501e-05, 1.5969e-04, |
6.1542e-06, 7.4851e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9481, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0417, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0103, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Are there human shoes in the image?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([7, 3, 448, 448]) |
tensor([5.4116e-01, 6.4672e-02, 1.3547e-02, 3.7189e-01, 4.9875e-03, 1.4729e-03, |
2.0136e-03, 2.5635e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([5.4116e-01, 6.4672e-02, 1.3547e-02, 3.7189e-01, 4.9875e-03, 1.4729e-03, |
2.0136e-03, 2.5635e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: torch.Size([7, 3, 448, 448]) |
{True: tensor(0.5412, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.4588, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is there a human in the image?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
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