text stringlengths 0 1.16k |
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841 |
question: ['Are all the birds floating on the water?'], responses:['yes'] |
question: ['How many graduation students are in the image?'], responses:['11'] |
[('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: 3, images per sample: 3.0, dynamic token length: 840 |
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)] |
[['11', '10', '12', '9', '8', '13', '7', '14']] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841 |
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: 3, images per sample: 3.0, dynamic token length: 841 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 842 |
tensor([1.0000e+00, 6.5545e-08, 6.8887e-08, 4.2000e-11, 7.0425e-07, 7.4474e-09, |
9.0994e-07, 5.6601e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 6.5545e-08, 6.8887e-08, 4.2000e-11, 7.0425e-07, 7.4474e-09, |
9.0994e-07, 5.6601e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 2.1482e-08, 5.2927e-11, 8.8789e-08, 1.5149e-10, 1.4166e-09, |
1.6335e-10, 1.6505e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.1482e-08, 5.2927e-11, 8.8789e-08, 1.5149e-10, 1.4166e-09, |
1.6335e-10, 1.6505e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.2650e-06, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(5.2927e-11, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1916e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many mittens are shown in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
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([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 |
tensor([1.0000e+00, 2.2724e-09, 5.6586e-08, 4.3210e-09, 3.4329e-11, 4.1462e-11, |
1.4715e-11, 1.4006e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.2724e-09, 5.6586e-08, 4.3210e-09, 3.4329e-11, 4.1462e-11, |
1.4715e-11, 1.4006e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([9.5964e-01, 2.2349e-03, 8.8398e-03, 2.0771e-04, 7.5712e-06, 1.7576e-02, |
1.4288e-04, 1.1348e-02], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
11 ************* |
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.5964e-01, 2.2349e-03, 8.8398e-03, 2.0771e-04, 7.5712e-06, 1.7576e-02, |
1.4288e-04, 1.1348e-02], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(5.6586e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.6586e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Are there multiple animals in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='How many penguins are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['How many mittens are shown in the image?'], responses:['0'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
[('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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
question: ['Are there multiple animals 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([13, 3, 448, 448]) knan debug pixel values shape |
torch.Size([3, 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: ['How many penguins are in the image?'], responses:['100'] |
[('100', 0.1277092174007614), ('120', 0.12519936731884676), ('88', 0.12483671971182599), ('80', 0.12474858811112934), ('60', 0.12457749608485191), ('99', 0.1243465850330014), ('90', 0.12430147627057883), ('101', 0.12428055006900451)] |
[['100', '120', '88', '80', '60', '99', '90', '101']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
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 |
tensor([1.0000e+00, 2.6887e-08, 2.9321e-08, 1.9643e-07, 7.5286e-09, 3.0288e-08, |
1.6749e-09, 6.5658e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.6887e-08, 2.9321e-08, 1.9643e-07, 7.5286e-09, 3.0288e-08, |
1.6749e-09, 6.5658e-08], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.9321e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.2831e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 3.1609e-10, 6.8359e-11, 1.3384e-10, 1.0754e-10, 1.6974e-08, |
1.9783e-09, 2.8664e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.1609e-10, 6.8359e-11, 1.3384e-10, 1.0754e-10, 1.6974e-08, |
1.9783e-09, 2.8664e-10], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.9783e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([9.8283e-01, 5.0127e-03, 7.2148e-07, 1.0445e-03, 3.6491e-03, 2.8633e-03, |
1.9558e-03, 2.6464e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
100 ************* |
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([9.8283e-01, 5.0127e-03, 7.2148e-07, 1.0445e-03, 3.6491e-03, 2.8633e-03, |
1.9558e-03, 2.6464e-03], 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(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)} |
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