text stringlengths 0 1.16k |
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[('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([7.7614e-01, 7.6849e-02, 2.3436e-02, 1.1181e-01, 6.5079e-03, 2.8856e-03, |
2.2485e-03, 1.1937e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.7614e-01, 7.6849e-02, 2.3436e-02, 1.1181e-01, 6.5079e-03, 2.8856e-03, |
2.2485e-03, 1.1937e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9648, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0352, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many goats are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} > 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353 |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353 |
tensor([0.5883, 0.0066, 0.2033, 0.1487, 0.0275, 0.0201, 0.0013, 0.0042], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.5883, 0.0066, 0.2033, 0.1487, 0.0275, 0.0201, 0.0013, 0.0042], |
device='cuda:1', grad_fn=<SelectBackward0>) |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1354 |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5883, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.4117, 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 contain a woman carrying groceries?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353 |
question: ['How many goats are in the image?'], responses:['2'] |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1354 |
[('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: 5, images per sample: 5.0, dynamic token length: 1354 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1354 |
question: ['Does the left image contain a woman carrying groceries?'], 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']] |
tensor([5.9197e-01, 4.0686e-01, 4.4624e-05, 9.6502e-05, 1.1192e-04, 6.8548e-04, |
1.8786e-04, 4.5830e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.9197e-01, 4.0686e-01, 4.4624e-05, 9.6502e-05, 1.1192e-04, 6.8548e-04, |
1.8786e-04, 4.5830e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4069, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5920, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0012, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Are the sails furled in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
tensor([6.4830e-01, 2.2182e-02, 4.7974e-03, 7.7346e-04, 1.3161e-03, 5.2729e-04, |
3.2208e-01, 3.1213e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.4830e-01, 2.2182e-02, 4.7974e-03, 7.7346e-04, 1.3161e-03, 5.2729e-04, |
3.2208e-01, 3.1213e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6483, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.3517, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the dingo laying on the grass?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([13, 3, 448, 448]) |
question: ['Are the sails furled in the image?'], 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: 1862 |
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: 1862 |
question: ['Is the dingo laying on the grass?'], responses:['no'] |
tensor([7.9703e-01, 7.8999e-02, 1.3728e-02, 1.0144e-01, 5.3787e-03, 1.4466e-03, |
1.8577e-03, 1.2667e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.9703e-01, 7.8999e-02, 1.3728e-02, 1.0144e-01, 5.3787e-03, 1.4466e-03, |
1.8577e-03, 1.2667e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8986, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1014, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
[('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']] |
ANSWER0=VQA(image=RIGHT,question='Is the collar on the dog clearly visible?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
question: ['Is the collar on the dog clearly visible?'], 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 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([7.6433e-01, 2.5825e-02, 2.0572e-01, 1.9109e-03, 1.7751e-04, 6.7216e-04, |
6.7135e-05, 1.3026e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.6433e-01, 2.5825e-02, 2.0572e-01, 1.9109e-03, 1.7751e-04, 6.7216e-04, |
6.7135e-05, 1.3026e-03], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7643, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2057, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0300, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the dog in the left image facing right?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
tensor([6.3650e-01, 3.6266e-01, 6.5721e-05, 1.3120e-04, 5.1079e-05, 3.0518e-04, |
2.4135e-04, 4.5158e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.3650e-01, 3.6266e-01, 6.5721e-05, 1.3120e-04, 5.1079e-05, 3.0518e-04, |
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