text stringlengths 0 1.16k |
|---|
question: ['How many water buffaloes 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: 839 |
question: ['How many open laptops can be seen in the image?'], responses:['0'] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
[('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']] |
tensor([1.0000e+00, 5.8092e-09, 4.9237e-11, 1.7536e-08, 3.4074e-11, 3.4715e-10, |
4.1933e-11, 1.5557e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.8092e-09, 4.9237e-11, 1.7536e-08, 3.4074e-11, 3.4715e-10, |
4.1933e-11, 1.5557e-09], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(4.9237e-11, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.9237e-11, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Does the image have a plain white background?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([1, 3, 448, 448]) |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
question: ['Does the image have a plain white background?'], 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([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
tensor([1.0000e+00, 3.8248e-09, 3.7244e-10, 1.0047e-08, 2.0274e-09, 3.4986e-10, |
2.2417e-11, 9.7654e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.8248e-09, 3.7244e-10, 1.0047e-08, 2.0274e-09, 3.4986e-10, |
2.2417e-11, 9.7654e-09], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(3.7244e-10, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-3.7244e-10, device='cuda:3', grad_fn=<SubBackward0>)} |
tensor([1.0000e+00, 1.9668e-09, 9.5185e-11, 1.3780e-11, 3.5326e-11, 2.9984e-09, |
3.6535e-08, 7.9863e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.9668e-09, 9.5185e-11, 1.3780e-11, 3.5326e-11, 2.9984e-09, |
3.6535e-08, 7.9863e-11], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(4.1724e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Does the television have leg stands?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
question: ['Is there at least one person in the image?'], responses:['yes'] |
torch.Size([3, 3, 448, 448]) |
[('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: ['Does the television have leg stands?'], responses:['no'] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
[('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: 3, images per sample: 3.0, dynamic token length: 835 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835 |
tensor([9.9999e-01, 2.2622e-06, 1.2415e-07, 1.3155e-09, 3.4113e-06, 5.3550e-08, |
1.3426e-07, 1.9624e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9999e-01, 2.2622e-06, 1.2415e-07, 1.3155e-09, 3.4113e-06, 5.3550e-08, |
1.3426e-07, 1.9624e-06], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.9870e-06, device='cuda:1', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
tensor([1.0000e+00, 3.7266e-06, 1.4906e-07, 8.8649e-10, 4.3866e-11, 1.1833e-09, |
3.7273e-09, 2.6443e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.7266e-06, 1.4906e-07, 8.8649e-10, 4.3866e-11, 1.1833e-09, |
3.7273e-09, 2.6443e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.7266e-06, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
question: ['How many dogs 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([13, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 7.0223e-08, 2.4725e-10, 8.6393e-08, 1.0441e-08, 1.8371e-08, |
3.1501e-10, 1.3093e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.0223e-08, 2.4725e-10, 8.6393e-08, 1.0441e-08, 1.8371e-08, |
3.1501e-10, 1.3093e-08], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.4725e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3817e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the top of the convertible off?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['Is the top of the convertible off?'], 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 |
tensor([1.0000e+00, 1.4815e-10, 8.6414e-11, 2.5798e-10, 1.1355e-10, 1.9551e-08, |
1.7357e-09, 1.6235e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.4815e-10, 8.6414e-11, 2.5798e-10, 1.1355e-10, 1.9551e-08, |
1.7357e-09, 1.6235e-10], device='cuda:1', grad_fn=<SelectBackward0>) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.